removing deepspeed guard for LoRA Triton kernels

* fix(example): align example to correct adapter

* fix: add missing load in 4 bit

.github/workflows/base.yml

@@ -40,12 +40,24 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "126"
+            cuda_version: 12.6.3
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.6.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
             pytorch: nightly
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: next
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -67,7 +79,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './docker/Dockerfile-base' }}
+          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/docs.yml

@@ -20,9 +20,12 @@ jobs:
           uses: actions/setup-python@v5
           with:
             python-version: '3.11'
-        - name: install dependencies
+        - name: Install dependencies
           run: |
-            python3 -m pip install jupyter
+            python3 -m pip install jupyter quartodoc
+            python3 -m pip install -e . --no-deps
+        - name: Build autodoc
+          run: quartodoc build
         - name: Publish to GitHub Pages (and render)
           uses: quarto-dev/quarto-actions/publish@v2
           with:

.github/workflows/main.yml

@@ -25,12 +25,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras: vllm
-            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -87,12 +87,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-            is_latest: true
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/multi-gpu-e2e.yml

@@ -42,8 +42,7 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
-            # awaiting vllm#12721
+            axolotl_extras: vllm
-            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
     runs-on: [self-hosted, modal]

.github/workflows/tests-nightly.yml

@@ -33,6 +33,15 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
+      - name: Restore HF cache
+        id: hf-cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            /home/runner/.cache/huggingface/hub/datasets--*
+            /home/runner/.cache/huggingface/hub/models--*
+          key: ${{ runner.os }}-hf-hub-cache-v2
+
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -46,7 +55,7 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} --index-url https://download.pytorch.org/whl/cpu
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Update requirements.txt
         run: |
@@ -58,8 +67,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install --upgrade pip
+          pip3 show torch
-          pip3 install --upgrade packaging==23.2
           pip3 install --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
@@ -73,10 +81,15 @@ jobs:
         run: |
           axolotl --help
 
+      - name: Pre-Download dataset fixture
+        run: |
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+
       - name: Run tests
         run: |
-          pytest -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest tests/patched/
+          pytest -v tests/patched/
+          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -136,4 +149,4 @@ jobs:
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.tests
+          modal run cicd.e2e_tests

.github/workflows/tests.yml

@@ -63,7 +63,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
+          key: ${{ runner.os }}-hf-hub-cache-v2
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -96,10 +96,15 @@ jobs:
         run: |
           axolotl --help
 
+      - name: Pre-Download dataset fixture
+        run: |
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
           pytest -v tests/patched/
+          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -136,7 +141,7 @@ jobs:
           path: |
             /home/runner/.cache/huggingface/hub/datasets--*
             /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-${{ hashFiles('**/conftest.py') }}
+          key: ${{ runner.os }}-hf-hub-cache-v2
 
       - name: Setup Python
         uses: actions/setup-python@v5
@@ -170,10 +175,14 @@ jobs:
         run: |
           axolotl --help
 
+      - name: Show HF cache
+        run: huggingface-cli scan-cache
+
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
           pytest -v tests/patched/
+          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -227,7 +236,7 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.tests
+          modal run cicd.e2e_tests
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
@@ -251,7 +260,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -274,4 +283,4 @@ jobs:
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run cicd.tests
+          modal run cicd.e2e_tests

.gitignore

@@ -181,6 +181,10 @@ prepared-datasets/
 submit.sh
 *.out*
 
+# Quartodoc generated files
+objects.json
+site_libs/
+
 typings/
 out/
 

.isort.cfg

@@ -1,3 +1,4 @@
 [settings]
 profile=black
 known_third_party=wandb,comet_ml
+known_local_folder=src,tests

README.md

@@ -97,6 +97,7 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 - [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
 - [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
 - [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
+- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
 - [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
 
 ## 🤝 Getting Help

_quarto.yml

@@ -1,6 +1,180 @@
 project:
   type: website
 
+quartodoc:
+  dir: docs/api
+  package: axolotl
+  title: API Reference
+  parser: google
+
+  sections:
+    - title: Core
+      desc: Core functionality for training
+      contents:
+        - train
+        - evaluate
+        - datasets
+        - convert
+        - prompt_tokenizers
+        - logging_config
+        - core.trainer_builder
+        - core.training_args
+        - core.chat.messages
+        - core.chat.format.chatml
+        - core.chat.format.llama3x
+        - core.chat.format.shared
+        - core.datasets.chat
+        - core.datasets.transforms.chat_builder
+    - title: CLI
+      desc: Command-line interface
+      contents:
+        - cli.main
+        - cli.train
+        - cli.evaluate
+        - cli.args
+        - cli.checks
+        - cli.config
+        - cli.inference
+        - cli.merge_lora
+        - cli.merge_sharded_fsdp_weights
+        - cli.preprocess
+        - cli.sweeps
+        - cli.utils
+        - cli.vllm_serve
+        - cli.cloud.base
+        - cli.cloud.modal_
+    - title: Trainers
+      desc: Training implementations
+      contents:
+        - core.trainers.base
+        - core.trainers.trl
+        - core.trainers.dpo.trainer
+        - core.trainers.grpo.trainer
+    - title: Prompt Strategies
+      desc: Prompt formatting strategies
+      contents:
+        - prompt_strategies.base
+        - prompt_strategies.chat_template
+        - prompt_strategies.alpaca_chat
+        - prompt_strategies.alpaca_instruct
+        - prompt_strategies.alpaca_w_system
+        - prompt_strategies.user_defined
+        - prompt_strategies.llama2_chat
+        - prompt_strategies.completion
+        - prompt_strategies.input_output
+        - prompt_strategies.stepwise_supervised
+        - prompt_strategies.metharme
+        - prompt_strategies.orcamini
+        - prompt_strategies.pygmalion
+        - prompt_strategies.messages.chat
+        - prompt_strategies.dpo.chat_template
+        - prompt_strategies.dpo.llama3
+        - prompt_strategies.dpo.chatml
+        - prompt_strategies.dpo.zephyr
+        - prompt_strategies.dpo.user_defined
+        - prompt_strategies.dpo.passthrough
+        - prompt_strategies.kto.llama3
+        - prompt_strategies.kto.chatml
+        - prompt_strategies.kto.user_defined
+        - prompt_strategies.orpo.chat_template
+        - prompt_strategies.bradley_terry.llama3
+    - title: Kernels
+      desc: Low-level performance optimizations
+      contents:
+        - kernels.lora
+        - kernels.geglu
+        - kernels.swiglu
+        - kernels.quantize
+        - kernels.utils
+    - title: MonkeyPatches
+      desc: Runtime patches for model optimizations
+      contents:
+        - monkeypatch.llama_attn_hijack_flash
+        - monkeypatch.llama_attn_hijack_xformers
+        - monkeypatch.mistral_attn_hijack_flash
+        - monkeypatch.multipack
+        - monkeypatch.relora
+        - monkeypatch.llama_expand_mask
+        - monkeypatch.lora_kernels
+        - monkeypatch.utils
+        - monkeypatch.btlm_attn_hijack_flash
+        - monkeypatch.llama_patch_multipack
+        - monkeypatch.stablelm_attn_hijack_flash
+        - monkeypatch.trainer_fsdp_optim
+        - monkeypatch.transformers_fa_utils
+        - monkeypatch.unsloth_
+        - monkeypatch.attention.mllama
+        - monkeypatch.data.batch_dataset_fetcher
+        - monkeypatch.mixtral
+    - title: Utils
+      desc: Utility functions
+      contents:
+        - utils.models
+        - utils.tokenization
+        - utils.chat_templates
+        - utils.lora
+        - utils.lora_embeddings
+        - utils.model_shard_quant
+        - utils.bench
+        - utils.freeze
+        - utils.trainer
+        - utils.schedulers
+        - utils.distributed
+        - utils.dict
+        - utils.optimizers.adopt
+        - utils.data.pretraining
+        - utils.data.sft
+        - utils.gradient_checkpointing.unsloth
+    - title: Schemas
+      desc: Pydantic data models for Axolotl config
+      contents:
+        - utils.schemas.config
+        - utils.schemas.model
+        - utils.schemas.training
+        - utils.schemas.datasets
+        - utils.schemas.peft
+        - utils.schemas.trl
+        - utils.schemas.multimodal
+        - utils.schemas.integrations
+        - utils.schemas.enums
+        - utils.schemas.utils
+    - title: Integrations
+      desc: Third-party integrations and extensions
+      contents:
+        - integrations.base
+        - integrations.cut_cross_entropy.args
+        - integrations.grokfast.optimizer
+        - integrations.kd.trainer
+        - integrations.liger.args
+        - integrations.lm_eval.args
+        - integrations.spectrum.args
+    - title: Common
+      desc: Common utilities and shared functionality
+      contents:
+        - common.architectures
+        - common.const
+        - common.datasets
+    - title: Models
+      desc: Custom model implementations
+      contents:
+        - models.mamba.modeling_mamba
+    - title: Data Processing
+      desc: Data processing utilities
+      contents:
+        - utils.collators.core
+        - utils.collators.batching
+        - utils.collators.mamba
+        - utils.collators.mm_chat
+        - utils.samplers.multipack
+    - title: Callbacks
+      desc: Training callbacks
+      contents:
+        - utils.callbacks.perplexity
+        - utils.callbacks.profiler
+        - utils.callbacks.lisa
+        - utils.callbacks.mlflow_
+        - utils.callbacks.comet_
+
 website:
   title: "Axolotl"
   description: "We make fine-tuning accessible, scalable, and fun"
@@ -35,6 +209,8 @@ website:
             - docs/inference.qmd
             - docs/cli.qmd
             - docs/config.qmd
+            - text: "API Reference"
+              href: docs/api
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
@@ -68,6 +244,7 @@ website:
             - docs/unsloth.qmd
             - docs/torchao.qmd
             - docs/custom_integrations.qmd
+            - docs/sequence_parallelism.qmd
 
         - section: "Troubleshooting"
           contents:
@@ -80,3 +257,22 @@ format:
     theme: darkly
     css: styles.css
     toc: true
+    # Enable better handling of line breaks in markdown
+    preserve-tabs: true
+    html-math-method: mathjax
+    # Improved markdown processing options
+    md-extensions:
+      - markdown_it
+      - def_list
+      - attr_list
+      - fenced_divs
+      - tables
+      - html_admonition
+      - lineblocks
+      - fancy_lists
+    # Control whitespace handling
+    whitespace: preserve
+    # Process newlines in paragraphs
+    wrap: preserve
+    # Better line break handling
+    preserve-linebreaks: true

cicd/Dockerfile.jinja

@@ -33,9 +33,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
 
 RUN pip install packaging==23.2 setuptools==75.8.0
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -3,9 +3,10 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /workspace/axolotl/tests/
+pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli /workspace/axolotl/tests/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/lora_kernels  # running these with the other patches causes a failure
 pytest -v --durations=10 --ignore=tests/e2e/patched/lora_kernels /workspace/axolotl/tests/e2e/patched
 pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
 pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
-pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+pytest -v --durations=10 /workspace/axolotl/tests/cli
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ --ignore=tests/cli /workspace/axolotl/tests/e2e/

cicd/multigpu.sh

@@ -2,4 +2,5 @@
 set -e
 
 # only run one test at a time so as not to OOM the GPU
-pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/
+pytest -v -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
+pytest -v -n1 /workspace/axolotl/tests/e2e/multigpu/solo/

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile-base-next

@@ -0,0 +1,38 @@
+ARG CUDA_VERSION="12.8.1"
+ARG CUDNN_VERSION="8"
+ARG UBUNTU_VERSION="22.04"
+ARG MAX_JOBS=4
+
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+
+ENV PATH="/root/miniconda3/bin:${PATH}"
+
+ARG PYTHON_VERSION="3.11"
+ARG PYTORCH_VERSION="next"
+ARG CUDA="128"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+
+ENV PYTHON_VERSION=$PYTHON_VERSION
+ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
+
+RUN apt-get update \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
+    && wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && mkdir /root/.conda \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
+    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+
+ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+
+WORKDIR /workspace
+
+RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
+    python3 -m pip install --no-cache-dir -U torch==2.7.0 --extra-index-url https://download.pytorch.org/whl/test/cu$CUDA && \
+    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
+    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
+
+RUN git lfs install --skip-repo && \
+    pip3 install awscli && \
+    pip3 install -U --no-cache-dir pydantic==2.10.6

docs/.gitignore

@@ -1,2 +1,4 @@
 /.quarto/
 _site/
+/api/*.qmd
+/api/*.html

docs/cli.qmd

@@ -1,5 +1,5 @@
 ---
-title: "CLI Reference"
+title: "Command Line Interface (CLI)"
 format:
   html:
     toc: true
@@ -170,7 +170,7 @@ axolotl merge-sharded-fsdp-weights config.yml
 
 ### evaluate
 
-Evaluates a model's performance using metrics specified in the config.
+Evaluates a model's performance (loss etc) on the train and eval datasets.
 
 ```bash
 # Basic evaluation
@@ -197,6 +197,8 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
+See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
+
 ## Legacy CLI Usage
 
 While the new Click-based CLI is preferred, Axolotl still supports the legacy module-based CLI:
@@ -235,7 +237,7 @@ Create a cloud config YAML with your Modal settings:
 ```yaml
 # cloud_config.yml
 provider: modal
-gpu: a100  # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
+gpu: a100       # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
 gpu_count: 1    # Number of GPUs to use
 timeout: 86400  # Maximum runtime in seconds (24 hours)
 branch: main    # Git branch to use (optional)
@@ -248,7 +250,7 @@ volumes:        # Persistent storage volumes
   - name: axolotl-artifacts
     mount: /workspace/artifacts
 
-env:            # Environment variables
+secrets:        # Secrets to inject
   - WANDB_API_KEY
   - HF_TOKEN
 ```
@@ -274,15 +276,27 @@ axolotl lm-eval config.yml --cloud cloud_config.yml
 ### Cloud Configuration Options
 
 ```yaml
-provider: # compute provider, currently only `modal` is supported
+provider:    # compute provider, currently only `modal` is supported
-gpu: # GPU type to use
+gpu:         # GPU type to use
-gpu_count: # Number of GPUs (default: 1)
+gpu_count:   # Number of GPUs (default: 1)
-memory: # RAM in GB (default: 128)
+memory:      # RAM in GB (default: 128)
-timeout: # Maximum runtime in seconds
+timeout:     # Maximum runtime in seconds
 timeout_preprocess: # Preprocessing timeout
-branch: # Git branch to use
+branch:      # Git branch to use
-docker_tag: # Custom Docker image tag
+docker_tag:  # Custom Docker image tag
-volumes: # List of persistent storage volumes
+volumes:     # List of persistent storage volumes
-env: # Environment variables to pass
+
-secrets: # Secrets to inject
+# Environment variables to pass. Can be specified in two ways:
+# 1. As a string: Will load the value from the host computer's environment variables
+# 2. As a key-value pair: Will use the specified value directly
+# Example:
+# env:
+#   - CUSTOM_VAR  # Loads from host's $CUSTOM_VAR
+#   - {CUSTOM_VAR: "value"}  # Uses "value" directly
+env:
+
+# Secrets to inject. Same input format as `env` but for sensitive data.
+secrets:
+  # - HF_TOKEN
+  # - WANDB_API_KEY
 ```

docs/config.qmd

@@ -32,6 +32,9 @@ tokenizer_legacy:
 resize_token_embeddings_to_32x:
 # Optional[bool] Whether to shrink the embeddings to len(tokenizer). By default, we won't shrink.
 shrink_embeddings:
+# Whether to load the model with randomly initialized weights. Useful for
+# pre-training a model from scratch or debugging purposes.
+random_init_weights:
 
 # (Internal use only)
 # Used to identify which the model is based on
@@ -235,10 +238,10 @@ simpo_gamma: 0.5  # Target reward margin for the SimPO loss
 # grpo
 trl:
   use_vllm: # Optional[bool]. Whether to use VLLM for RL training.
-  vllm_device: # Optional[str]. Device to use for VLLM.
+  vllm_server_host: # Optional[str]. Host of the vLLM server to connect to.
-  vllm_gpu_memory_utilization: # Optional[float]. GPU memory utilization for VLLM.
+  vllm_server_port: # Optional[int]. Port of the vLLM server to connect to.
-  vllm_max_model_len: # Optional[int]. Maximum length of the model for VLLM.
+  vllm_server_timeout: # Optional[int]. Total timeout (in seconds) to wait for the vLLM server to respond.
-  vllm_dtype: # Optional[str]. Data type for VLLM.
+  vllm_guided_decoding_regex: # Optional[str]. Regex for vLLM guided decoding.
 
   beta: # Optional[float]. Beta parameter for the RL training. Same as `rl_beta`. Use
   max_completion_length: # Optional[int]. Maximum length of the completion for RL training.
@@ -317,9 +320,13 @@ total_num_tokens:
 sample_packing_group_size: 100000
 # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
 sample_packing_bin_size: 200
+sample_pack_sequentially: # Optional[bool]. Whether to pack samples sequentially.
+
 # whether to concatenate samples during pretraining
 pretraining_sample_concatenation:
 
+curriculum_sampling: # Optional[bool]. Whether to use sequential sampling for curriculum learning
+
 # Use batch flattening for speedups when not using sample_packing
 batch_flattening:
 
@@ -351,7 +358,27 @@ lora_target_modules:
 #  - down_proj
 #  - up_proj
 lora_target_linear: # If true, will target all linear modules
-peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
+
+# List[int] | int. # The layer indices to transform, otherwise, apply to all layers
+# https://huggingface.co/docs/peft/v0.15.0/en/package_reference/lora#peft.LoraConfig.layers_to_transform
+peft_layers_to_transform:
+
+# Optional[bool]. Whether to use DoRA.
+# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#weight-decomposed-low-rank-adaptation-dora
+peft_use_dora:
+
+# Optional[bool]. Whether to use RSLoRA.
+# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#rank-stabilized-lora
+peft_use_rslora:
+
+# Optional[list[tuple[int, int]]]. List of layer indices to replicate.
+# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#memory-efficient-layer-replication-with-lora
+peft_layer_replication:
+
+# bool | Literal["gaussian", "eva", "olora", "pissa", "pissa_niter_[number of iters]", "corda", "loftq"]
+# How to initialize LoRA weights. Default to True which is MS original implementation.
+# https://huggingface.co/docs/peft/v0.15.0/en/developer_guides/lora#initialization
+peft_init_lora_weights:
 
 # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
 # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
@@ -463,6 +490,7 @@ auto_find_batch_size: # Optional[bool]
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
+do_causal_lm_eval: # Whether to run causal language model evaluation for metrics in `eval_causal_lm_metrics`.
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
 profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
@@ -482,7 +510,8 @@ train_on_inputs: false
 # Note that training loss may have an oscillating pattern with this enabled.
 group_by_length: false
 
-# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
+# Whether to use gradient checkpointing. Available options are: true, false, "offload".
+# https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false
 # additional kwargs to pass to the trainer for gradient checkpointing
 # gradient_checkpointing_kwargs:
@@ -503,36 +532,58 @@ lr_div_factor: # Learning rate div factor
 
 # Specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
-# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
+# https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
 #
 # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
 # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
 # in the examples/ for your model and fine-tuning use case.
 #
 # Valid values for 'optimizer' include:
-# - adamw_hf
 # - adamw_torch
 # - adamw_torch_fused
 # - adamw_torch_xla
+# - adamw_torch_npu_fused
 # - adamw_apex_fused
-# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
+# - adopt_adamw  (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
+# - adamw_torch_4bit
+# - ademamix
 # - sgd
 # - adagrad
 # - adamw_bnb_8bit
+# - adamw_8bit   # alias for adamw_bnb_8bit
+# - ademamix_8bit
 # - lion_8bit
 # - lion_32bit
 # - paged_adamw_32bit
 # - paged_adamw_8bit
+# - paged_ademamix_32bit
+# - paged_ademamix_8bit
 # - paged_lion_32bit
 # - paged_lion_8bit
+# - rmsprop
+# - rmsprop_bnb
+# - rmsprop_bnb_8bit
+# - rmsprop_bnb_32bit
 # - galore_adamw
 # - galore_adamw_8bit
 # - galore_adafactor
 # - galore_adamw_layerwise
 # - galore_adamw_8bit_layerwise
 # - galore_adafactor_layerwise
+# - lomo
+# - adalomo
+# - grokadamw
+# - schedule_free_adamw
+# - schedule_free_sgd
+# - apollo_adamw
+# - apollo_adamw_layerwise
+#
+# Additional custom optimizers include:
+# - optimi_adamw
+# - ao_adamw_8bit
+# - ao_adamw_fp8
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -561,29 +612,42 @@ max_grad_norm:
 # currently only supported on Llama and Mistral
 neftune_noise_alpha:
 
-# Whether to bettertransformers
+# Optional[bool]. Whether to bettertransformers
 flash_optimum:
-# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
+
+# Note: Only one of the following attention patches can be used at a time.
+# For example, if you set `xformers_attention` to `true`, do not set `flash_attention` to `true`.
+
+# Optional[bool]. Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
 xformers_attention:
-# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
+# Optional[bool]. Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
 flash_attention:
-flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
+flash_attn_cross_entropy:  # Optional[bool]. Whether to use flash-attention cross entropy implementation - advanced use only
-flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
+flash_attn_rms_norm:  # Optional[bool]. Whether to use flash-attention rms norm implementation - advanced use only
-flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
+flash_attn_fuse_qkv: # Optional[bool]. Whether to fuse QKV into a single operation
-flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
+flash_attn_fuse_mlp: # Optional[bool]. Whether to fuse part of the MLP into a single operation
-# Whether to use scaled-dot-product attention
+# Optional[bool]. Whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
-# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
+# Optional[bool]. Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
 s2_attention:
+
 # Optional[bool]. Whether to use low_cpu_mem_usage
 low_cpu_mem_usage:
-# Resume from a specific checkpoint dir
+# Optional[str]. Resume from a specific checkpoint dir
 resume_from_checkpoint:
-# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
+# Optional[bool]. If resume_from_checkpoint isn't set and you simply want it to start where it left off.
 # Be careful with this being turned on between different models.
 auto_resume_from_checkpoints: false
 
+## Multimodal section
+# int | tuple[int, int] | None . Size to resize images to, width x height.
+# Will read from model/processor config if not set.
+image_size:
+# str. Algorithm to use for image resizing. "bilinear", "bicubic", "lanczos". Default is "bilinear".
+image_resize_algorithm: 'bilinear'
+## End of multimodal section
+
 # Don't mess with this, it's here for accelerate and torchrun
 local_rank:
 
@@ -617,6 +681,17 @@ ddp_timeout:
 ddp_bucket_cap_mb:
 ddp_broadcast_buffers:
 
+# Sequence parallelism
+# Set to a divisor of the number of GPUs available to split sequences into chunks of equal size.
+# Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
+# E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
+# subsequences, or set to 4 to split into four equal-sized subsequences.
+# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
+sequence_parallel_degree:
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+# Must evenly divide the number of KV heads in your model.
+heads_k_stride: 1
+
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 

docs/dataset_preprocessing.qmd

@@ -6,7 +6,7 @@ description: How datasets are processed
 ## Overview
 
 Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the [dataset format](docs/dataset-formats) and prompt strategies to:
+the [dataset format](dataset-formats) and prompt strategies to:
 
  - parse the dataset based on the *dataset format*
  - transform the dataset to how you would interact with the model based on the *prompt strategy*

docs/docker.qmd

@@ -103,8 +103,7 @@ This uses the same tags as the [`main` image](#sec-main-tags).
 
 - `JUPYTER_DISABLE`: Disable Jupyter lab.
 - `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
-- `PUBLIC_KEY`: Add a public key for the SSH service.
+- `PUBLIC_KEY` / `SSH_KEY`: Add a public key for the SSH service.
-- `SSH_KEY`: Add a private key for the SSH service.
 
 #### Volume mounts
 

docs/faq.qmd

@@ -35,7 +35,21 @@ description: Frequently asked questions
 
 **Q: How to call Axolotl via custom python scripts?**
 
-> A: Yes, since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
+> A: Since Axolotl is just Python, please see `src/axolotl/cli/main.py` on how each command is called.
+
+**Q: How to know the value to use for `fsdp_transformer_layer_cls_to_wrap`?**
+
+> A: This is the class name of the transformer layer to wrap with FSDP. For example, for `LlamaForCausalLM`, the value is `LlamaDecoderLayer`. To find this for a specific model, check the model's `PreTrainedModel` definition and look for `_no_split_modules` variable in the `modeling_<model_name>.py` file within `transformers` library.
+
+**Q: ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as pad_token**
+
+> A: This is because the tokenizer does not have a padding token. Please add a padding token to the tokenizer via:
+
+> ```yaml
+> special_tokens:
+>   # str. If you're not sure, set to same as `eos_token`.
+>   pad_token: "..."
+> ```
 
 ### Chat templates
 

docs/lora_optims.qmd

@@ -17,6 +17,7 @@ We currently support several common model architectures, including (but not limi
 - `qwen2`
 - `gemma`
 - `gemma2`
+- `gemma3`
 
 <details>
 

docs/multi-gpu.qmd

@@ -18,6 +18,7 @@ Axolotl supports several methods for multi-GPU training:
 
 - DeepSpeed (recommended)
 - FSDP (Fully Sharded Data Parallel)
+- Sequence parallelism
 - FSDP + QLoRA
 
 ## DeepSpeed {#sec-deepspeed}
@@ -66,6 +67,28 @@ fsdp_config:
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```
 
+## Sequence parallelism {#sec-sequence-parallelism}
+
+We support sequence parallelism (SP) via the
+[ring-flash-attention](https://github.com/zhuzilin/ring-flash-attention) project. This
+allows one to split up sequences across GPUs, which is useful in the event that a
+single sequence causes OOM errors during model training.
+
+First, install `ring-flash-attn`, recommended via `pip install axolotl[ring-flash-attn]`,
+or from source with `pip install .[ring-flash-attn]`.
+
+Your Axolotl YAML config should contain the following lines:
+
+```{.yaml}
+sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
+flash_attention: true  # Required with sequence parallelism
+
+# Optional; strides across the key dimension. Larger values use more memory but will make training faster.
+heads_k_stride: 1
+```
+
+See our [dedicated guide](sequence_parallelism.qmd) for more details.
+
 ### FSDP + QLoRA {#sec-fsdp-qlora}
 
 For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).

docs/multimodal.qmd

@@ -1,28 +1,171 @@
-# MultiModal / Vision Language Models (BETA)
+---
+title: MultiModal / Vision Language Models (BETA)
+format:
+  html:
+    toc: true
+    toc-depth: 3
+---
 
-### Supported Models
+## Supported Models
 
-- Mllama, i.e. llama with vision models
+- [Mllama](#sec-mllama)
+- [Pixtral](#sec-pixtral)
+- [Llava-1.5](#sec-llava-15)
+- [Mistral-Small-3.1](#sec-mistral-small-31)
+- [Gemma-3](#sec-gemma-3)
+- [Qwen2-VL](#sec-qwen2-vl)
+- [Qwen2.5-VL](#sec-qwen25-vl)
 
-### Usage
+## Usage
 
-Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+Multimodal support is limited and doesn't have full feature parity.
-you'll need to use the following in YAML in combination with the rest of the required hyperparams.
+
+Here are the hyperparams you'll need to use to finetune a multimodal model.
 
 ```yaml
-base_model: alpindale/Llama-3.2-11B-Vision-Instruct
 processor_type: AutoProcessor
-skip_prepare_dataset: true
 
-chat_template: llama3_2_vision
+skip_prepare_dataset: true
+remove_unused_columns: false  # leave columns in place as they are needed to handle image embeddings during training
+sample_packing: false  # not yet supported with multimodal
+
+chat_template:  # see in next section
+
+# example dataset
 datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
     field_messages: messages
-remove_unused_columns: false
-sample_packing: false
 
-# only finetune the Language model, leave the vision model and vision tower frozen
+# (optional) if doing lora, only finetune the Language model,
+# leave the vision model and vision tower frozen
+# load_in_8bit: true
+adapter: lora
 lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+# (optional) if you want to resize images to a set size
+image_size: 512
+image_resize_algorithm: bilinear
+```
+
+Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
+
+::: {.callout-warning}
+Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
+:::
+
+### Mllama {#sec-mllama}
+
+```yaml
+base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
+
+chat_template: llama3_2_vision
+```
+
+### Pixtral {#sec-pixtral}
+
+```yaml
+base_model: mistralai/Pixtral-12B-2409
+
+chat_template: pixtral
+```
+
+### Llava-1.5 {#sec-llava-15}
+
+```yaml
+base_model: llava-hf/llava-1.5-7b-hf
+
+chat_template: llava
+```
+
+### Mistral-Small-3.1 {#sec-mistral-small-31}
+
+```yaml
+base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+
+chat_template: mistral_v7_tekken
+```
+
+### Gemma-3 {#sec-gemma-3}
+
+::: {.callout-tip}
+The Gemma3-1B model is a text-only model, so please train as regular text model.
+:::
+
+For multi-modal 4B/12B/27B models, use the following config:
+
+```yaml
+base_model: google/gemma-3-4b-it
+
+chat_template: gemma3
+```
+
+### Qwen2-VL {#sec-qwen2-vl}
+
+```yaml
+base_model: Qwen/Qwen2-VL-7B-Instruct
+
+chat_template: qwen2_vl
+```
+
+### Qwen2.5-VL {#sec-qwen25-vl}
+
+```yaml
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+
+chat_template: qwen2_vl  # same as qwen2-vl
+```
+
+## Dataset Format
+
+For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.
+
+- A message is a list of `role` and `content`.
+- `role` can be `system`, `user`, `assistant`, etc.
+- `content` is a list of `type` and (`text` or `image` or `path` or `url` or `base64`).
+
+::: {.callout-note}
+For backwards compatibility:
+
+- If the dataset has a `images` or `image` column of `list[Image]`, it will be appended to the first `content` list as `{"type": "image", "image": ...}`. However, if the content already has a `{"type": "image"}` but no `image` key, it will be set the `image` key.
+- If `content` is a string, it will be converted to a list with `type` as `text`.
+:::
+
+::: {.callout-tip}
+For image loading, you can use the following keys within `content` alongside `"type": "image"`:
+
+- `"path": "/path/to/image.jpg"`
+- `"url": "https://example.com/image.jpg"`
+- `"base64": "..."`
+- `"image": PIL.Image`
+:::
+
+Here is an example of a multi-modal dataset:
+```json
+[
+  {
+    "messages": [
+        {
+            "role": "system",
+            "content": [
+              {"type": "text", "text": "You are a helpful assistant."}
+              ]
+        },
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+                {"type": "text", "text": "Describe this image in detail."}
+            ]
+        },
+        {
+            "role": "assistant",
+            "content": [
+              {"type": "text", "text": "The image is a bee."}
+            ]
+        }
+    ]
+  }
+]
 ```

docs/rlhf.qmd

@@ -502,9 +502,48 @@ The input format is a simple JSON input with customizable fields based on the ab
 Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
 :::
 
+If you have multiple GPUs available, we reccomend using `vLLM` with the `GRPOTrainer` to significantly speedup trajectory generation during training.
+First, launch a `vLLM` server using `trl vllm-serve` - you may use a config file or CLI overrides to configure your vLLM server. In this example, we're
+using 4 GPUs - 2 for training, and 2 for vLLM:
+
+::: {.callout-important}
+Make sure you've installed the correct version of vLLM by including it as an extra when installing axolotl, e.g. `pip install axolotl[vllm]`.
+:::
+
+```yaml
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+
+vllm:
+    host: 0.0.0.0
+    port: 8000
+    tensor_parallel_size: 2
+    gpu_memory_utilization: 0.85
+    dtype: auto
+    # max_model_len: # you may find it useful to set the vLLM model context length if you know this beforehand
+
+rl: grpo
+trl:
+    use_vllm: true
+    vllm_server_host: 0.0.0.0
+    vllm_server_port: 8000
+    vllm_server_timeout: 300
+```
+
+```bash
+CUDA_VISIBLE_DEVICES=2,3 axolotl vllm_serve grpo.yaml
+```
+
+Your `vLLM` instance will now attempt to spin up, and it's time to kick off training utilizing our remaining two GPUs. In another terminal, execute:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 axolotl train grpo.yaml --num-processes 2
+```
+
+#### Reward functions
+
 GRPO uses custom reward functions and transformations. Please have them ready locally.
 
-For ex, to load OpenAI's GSM8K and use a random reward for completions:
+For example, to load OpenAI's GSM8K and use a random reward for completions:
 
 ```python
 # rewards.py
@@ -530,8 +569,6 @@ trl:
     beta: 0.001
     max_completion_length: 256
     use_vllm: True
-    vllm_device: auto
-    vllm_gpu_memory_utilization: 0.15
     num_generations: 4
     reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
     reward_weights: [1.0]

docs/sequence_parallelism.qmd

@@ -0,0 +1,97 @@
+---
+title: Sequence Parallelism
+description: Train with long sequences split across multiple GPUs.
+---
+
+# Sequence Parallelism
+
+Sequence parallelism is a technique that splits sequences across multiple GPUs,
+allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
+GPU processes a different portion of the sequence, and the results are aggregated
+through a ring communication pattern.
+
+## When to Use Sequence Parallelism
+
+Use sequence parallelism when:
+
+- You need to train with sequence lengths that don't fit into a single GPU's memory
+- You have multiple GPUs available
+- You're experiencing OOM (Out Of Memory) errors with long sequences
+
+## Configuration
+
+To enable sequence parallelism, add the following to your configuration file:
+
+```yaml
+# Set to a divisor (> 1) of the number of GPUs available
+sequence_parallel_degree: 4  # Split sequences across 4 GPUs
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+heads_k_stride: 1
+```
+
+The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
+
+- With 8 GPUs, valid values would be 2, 4, or 8
+- With 4 GPUs, valid values would be 2 or 4
+
+## Implementation Details
+
+When sequence parallelism is enabled:
+
+1. Each sequence is divided into equal chunks across the GPUs in a sequence parallel group
+2. The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids
+3. Position IDs are adjusted to maintain proper relative positions, especially for packed sequences
+4. The trainer uses special ring communication patterns for attention operations
+
+## Requirements
+
+To use sequence parallelism, you need:
+
+- Multiple GPUs (at least 2)
+- The `ring-flash-attn` package. Install with:
+  - `pip install axolotl[ring-flash-attn]` (preferred)
+  - `pip install ring-flash-attn>=0.1.4`
+
+## Limitations
+
+- Flash attention must be enabled for this to work (`flash_attention: true` in config YAML)
+- May have a small performance overhead due to communication between GPUs
+
+## Example
+
+```yaml
+base_model: meta-llama/Llama-3-8B-Instruct
+sequence_len: 8192
+
+...
+
+sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
+flash_attention: true  # Required with sequence parallelism
+# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
+heads_k_stride: 1
+
+...
+```
+
+This will train the Llama 3 8B model with 8K context length, with each sequence split
+into 2 subsequences of length 4096 across 2 GPUs.
+
+## Sample Packing with Sequence Parallelism
+
+Sequence parallelism is compatible with Axolotl's sample packing functionality. When using both features together:
+
+1. Samples are first packed together
+2. The packed sequences are then divided across GPUs in the sequence parallel group
+3. Position IDs are automatically adjusted to maintain proper relative positions
+
+## Effect on Batch Size
+
+When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
+
+- Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
+- The number of batches processed per step decreases
+
+For example:
+- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
+- With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
+- If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

examples/cohere/command-r-7b-qlora.yml

@@ -0,0 +1,71 @@
+base_model: CohereForAI/c4ai-command-r7b-12-2024
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+# huggingface repo
+chat_template: cohere
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/gemma3/gemma-3-1b-qlora.yml

@@ -0,0 +1,79 @@
+base_model: google/gemma-3-1b-it
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+# huggingface repo
+chat_template: gemma3
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/gemma3/gemma-3-4b-qlora.yml

@@ -0,0 +1,68 @@
+base_model: google/gemma-3-4b-it
+strict: false
+
+load_in_4bit: true
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+chat_template: gemma3
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -0,0 +1,70 @@
+base_model: google/gemma-3-4b-it
+processor_type: AutoProcessor
+strict: false
+
+load_in_4bit: true
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+chat_template: gemma3
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -19,7 +19,6 @@ val_set_size: 0.0
 output_dir: ./outputs/lora-out
 
 dataset_exact_deduplication: true
-test_value: true
 
 sequence_len: 4096
 sample_packing: true

examples/llama-3/lora-1b-sample-packing-sequentially.yml

@@ -0,0 +1,80 @@
+base_model: meta-llama/Llama-3.2-1B
+# optionally might have model_type or tokenizer_type
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.0
+output_dir: ./outputs/lora-out
+
+test_value: true
+
+sequence_len: 4096
+sample_packing: true
+sample_packing_sequentially: true
+curriculum_sampling: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>

examples/llava/lora-7b.yaml

@@ -0,0 +1,63 @@
+base_model: llava-hf/llava-1.5-7b-hf
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: llava
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/mistral/mistral-small-3.1-24B-lora.yml

@@ -0,0 +1,66 @@
+base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+processor_type: AutoProcessor
+strict: false
+
+load_in_8bit: true
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: mistral_v7_tekken
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 2048
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet.
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

examples/pixtral/lora-12b.yml

@@ -0,0 +1,65 @@
+base_model: mistral-community/pixtral-12b
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: pixtral
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <pad>

examples/qwen2-vl/lora-7b.yaml

@@ -0,0 +1,63 @@
+base_model: Qwen/Qwen2-VL-7B-Instruct
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: qwen2_vl
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

requirements-dev.txt

@@ -2,3 +2,5 @@ pre-commit
 black
 mypy
 types-requests
+quartodoc
+jupyter

requirements.txt

@@ -1,24 +1,23 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.45.3
+bitsandbytes==0.45.4
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
-flash-attn==2.7.4.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
-liger-kernel==0.5.3
+liger-kernel==0.5.5
 # END section
 
 packaging==23.2
 
 peft==0.15.0
-transformers==4.49.0
+transformers==4.50.3
 tokenizers>=0.21.1
 accelerate==1.5.2
-datasets==3.4.1
+datasets==3.5.0
-deepspeed==0.16.4
+deepspeed==0.15.4
-trl==0.15.1
+trl==0.16.0
 
 optimum==1.16.2
 hf_transfer
@@ -36,6 +35,7 @@ einops
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
+
 # qlora things
 evaluate==0.4.1
 scipy

requirements_env.txt

@@ -1,315 +0,0 @@
-accelerate==0.34.1
-addict==2.4.0
-aiofiles==23.2.1
-aiohttp==3.9.0
-aiosignal==1.3.1
-aiostream==0.5.2
-alembic==1.13.1
-annotated-types==0.6.0
-annoy==1.17.3
-ansible==6.7.0
-ansible-core==2.13.13
-ansible-vault==2.1.0
-anyio==3.7.1
-appdirs==1.4.4
-art==6.0
-asgiref==3.7.2
-async-timeout==4.0.2
-attrdict==2.0.1
-attrs==22.2.0
-awscli==1.32.75
--e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
-backoff==2.2.1
-base58==2.1.1
-beartype==0.17.2
-bitnet==0.2.1
-bitsandbytes==0.42.0
-bittensor==6.7.0
-black==23.7.0
-blinker==1.7.0
-boto3==1.34.75
-botocore==1.34.75
-cachetools==5.3.3
-cachy==0.1.1
-certifi==2023.7.22
-cffi==1.16.0
-cfgv==3.3.1
-chai-guanaco==1.2.4
-charset-normalizer==3.2.0
-cleo==0.6.8
-click==8.1.7
-cloudpickle==2.0.0
-cohere==4.11.2
-colorama==0.4.4
-coloredlogs==15.0.1
-CoLT5-attention==0.10.20
-contextlib2==21.6.0
-contourpy==1.2.0
-cryptography==41.0.3
-cycler==0.12.1
-cytoolz==0.12.3
-databricks-cli==0.18.0
-dataclasses-json==0.5.7
-datasets==2.11.0
-ddt==1.6.0
-decorator==5.1.1
-deepspeed==0.15.0
-# Editable Git install with no remote (dialogpt==0.1)
--e /Users/wing/Projects/ml/dialogpt/src
-dill==0.3.6
-distlib==0.3.6
-docker==7.0.0
-docker-pycreds==0.4.0
-docstring-parser==0.15
-docutils==0.16
-ecdsa==0.18.0
-einops==0.7.0
-einops-exts==0.0.4
-einx==0.1.3
-entrypoints==0.4
-eth-hash==0.6.0
-eth-keys==0.5.0
-eth-typing==4.0.0
-eth-utils==2.3.1
-evaluate==0.4.0
-exceptiongroup==1.1.1
-fastapi==0.109.2
-fastcore==1.5.29
-ffmpy==0.4.0
-filelock==3.12.2
--e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
-fire==0.5.0
-first==2.0.2
-flake8==7.0.0
-Flask==3.0.1
-fonttools==4.47.2
-frozendict==2.4.1
-frozenlist==1.3.3
-fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
-fsspec==2023.6.0
-fuzzywuzzy==0.18.0
-gitdb==4.0.10
-GitPython==3.1.31
-google-pasta==0.2.0
-gradio==4.42.0
-gradio_client==1.3.0
-greenlet==2.0.2
-grpclib==0.4.7
-gunicorn==21.2.0
-h11==0.14.0
-h2==4.1.0
-hpack==4.0.0
-httpcore==0.17.3
-httpx==0.24.1
-huggingface-hub==0.23.4
-humanfriendly==10.0
-hyperframe==6.0.1
-identify==2.5.24
-idna==3.4
-immutables==0.20
-importlib-metadata==6.7.0
-importlib-resources==6.1.1
-inflection==0.5.1
-iniconfig==2.0.0
-itsdangerous==2.1.2
-Jinja2==3.1.2
-jmespath==1.0.1
-joblib==1.3.2
-jsonlines==3.1.0
-jsonschema==2.6.0
-kiwisolver==1.4.5
-langchain==0.0.144
-Levenshtein==0.24.0
-libcst==1.1.0
-liger-kernel==0.0.0
-lion-pytorch==0.1.2
-llama-cpp-python==0.1.36
-llvmlite==0.40.1
-local-attention==1.9.0
-loguru==0.7.0
-Mako==1.3.2
-Markdown==3.5.2
-markdown-it-py==3.0.0
-markdown2==2.4.10
-MarkupSafe==2.1.2
-marshmallow==3.19.0
-marshmallow-enum==1.5.1
-matplotlib==3.8.2
-mccabe==0.7.0
-mdurl==0.1.2
-MEGABYTE-pytorch==0.0.7
--e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
-mlflow==2.10.0
-modal==0.62.77
-more-itertools==10.2.0
-mpmath==1.2.1
-msgpack==1.0.7
-msgpack-numpy-opentensor==0.5.0
-multidict==6.0.4
-multiprocess==0.70.14
-munch==2.5.0
-mypy==1.3.0
-mypy-extensions==1.0.0
-nest-asyncio==1.6.0
-netaddr==0.10.1
-networkx==3.0rc1
-nh3==0.2.14
-nodeenv==1.8.0
-nomic==2.0.2
-numba==0.57.1
-numexpr==2.8.4
-numpy==1.24.4
-oauthlib==3.2.2
-openai==0.27.4
-openapi==1.1.0
-openapi-schema-pydantic==1.2.4
-optimum==1.8.6
-orjson==3.10.7
-packaging==23.1
-pandas==2.0.0
-parameterized==0.9.0
-password-strength==0.0.3.post2
-pastel==0.1.1
-pathos==0.3.0
-pathspec==0.11.1
-pathtools==0.1.2
-peft==0.11.1
-pendulum==3.0.0
-Pillow==9.5.0
-pip-tools==1.11.0
-platformdirs==3.2.0
-pluggy==1.4.0
-poetry==0.7.1
-pox==0.3.2
-ppft==1.7.6.6
-pre-commit==3.3.2
-prettytable==3.10.0
-prompt-toolkit==3.0.39
-protobuf==3.20.2
-protobuf3-to-dict==0.1.5
-psutil==5.9.5
-psycopg==3.1.18
-PuLP==2.8.0
-py==1.11.0
-py-bip39-bindings==0.1.11
-py-cpuinfo==9.0.0
-py-ed25519-zebra-bindings==1.0.1
-py-sr25519-bindings==0.2.0
-pyarrow==11.0.0
-pyasn1==0.6.0
-pycodestyle==2.11.1
-pycparser==2.21
-pycryptodome==3.20.0
-pydantic==2.5.3
-pydantic_core==2.14.6
-pydub==0.25.1
-pyfiglet==0.8.post1
-pyflakes==3.2.0
-Pygments==2.15.1
-PyJWT==2.8.0
-pylev==1.4.0
-PyNaCl==1.5.0
-pynvml==11.5.0
-pyparsing==2.4.7
-pyrsistent==0.14.11
-pytest==8.0.2
-pytest-asyncio==0.23.4
-python-dateutil==2.8.2
-python-dotenv==1.0.1
-python-Levenshtein==0.24.0
-python-multipart==0.0.9
-pytz==2023.3
-PyYAML==6.0.1
-querystring-parser==1.2.4
-rapidfuzz==3.6.1
-regex==2023.6.3
-requests==2.31.0
-requests-toolbelt==0.8.0
-resolvelib==0.8.1
-responses==0.18.0
-retry==0.9.2
-rich==13.7.0
-rsa==4.7.2
-ruff==0.6.3
-s3transfer==0.10.1
-safetensors==0.4.5
-sagemaker==2.148.0
-scalecodec==1.2.7
-schedulefree==1.2.1
-schema==0.7.5
-scikit-learn==1.4.0
-scipy==1.9.3
-seaborn==0.13.2
-semantic-version==2.10.0
-sentencepiece==0.2.0
-sentry-sdk==1.19.1
-setproctitle==1.3.2
-shellingham==1.5.4
-shortuuid==1.0.11
-shtab==1.6.5
-sigtools==4.0.1
-six==1.16.0
-skypilot==0.4.1
-smdebug-rulesconfig==1.0.1
-smmap==5.0.0
-sniffio==1.3.0
-SQLAlchemy==1.4.47
-sqlparse==0.4.4
-starlette==0.36.3
-substrate-interface==1.5.2
-svgwrite==1.4.3
-sympy==1.11.1
-synchronicity==0.6.7
-tabulate==0.9.0
-tblib==1.7.0
-tenacity==8.2.2
-tensor-parallel==2.0.0
-termcolor==2.2.0
-text2art==0.2.0
-threadpoolctl==3.2.0
-tiktoken==0.6.0
-time-machine==2.14.1
-timm==0.9.16
-tokenizers==0.19.1
-tokenmonster==1.1.12
-toml==0.9.6
-tomli==2.0.1
-tomlkit==0.12.0
-toolz==0.12.1
-torch==2.2.0
-torchdata==0.6.1
-torchdiffeq==0.2.3
-TorchFix==0.4.0
-torchtext==0.15.2
-torchvision==0.17.0
-tqdm==4.66.2
-transformers==4.44.2
-trl==0.9.6
-typer==0.12.5
-types-certifi==2021.10.8.3
-types-requests==2.31.0.20240125
-types-setuptools==69.0.0.20240125
-types-toml==0.10.8.7
-typing==3.7.4.3
-typing-inspect==0.8.0
-typing_extensions==4.9.0
-tyro==0.5.18
-tzdata==2023.3
-unique-names-generator==1.0.2
-urllib3==2.2.2
-uvicorn==0.22.0
-vector_quantize_pytorch==1.14.1
-virtualenv==20.23.0
-voyager==2.0.2
-wandb==0.16.2
-watchfiles==0.21.0
-wavedrom==2.0.3.post3
-wcwidth==0.2.6
-websocket-client==1.7.0
-websockets==12.0
-Werkzeug==3.0.1
-wonderwords==2.2.0
-xxhash==3.2.0
-yarl==1.8.2
-zetascale==2.2.7
-zipp==3.15.0

setup.py

@@ -10,19 +10,13 @@ from pathlib import Path
 from setuptools import find_packages, setup
 
 
-def parse_requirements():
+def parse_requirements(extras_require_map):
     _install_requires = []
     _dependency_links = []
     with open("./requirements.txt", encoding="utf-8") as requirements_file:
         lines = [r.strip() for r in requirements_file.readlines()]
         for line in lines:
-            is_extras = (
+            is_extras = "deepspeed" in line or "mamba-ssm" in line
-                "flash-attn" in line
-                or "flash-attention" in line
-                or "deepspeed" in line
-                or "mamba-ssm" in line
-                or "lion-pytorch" in line
-            )
             if line.startswith("--extra-index-url"):
                 # Handle custom index URLs
                 _, url = line.split()
@@ -39,7 +33,6 @@ def parse_requirements():
                 "bitsandbytes",
                 "triton",
                 "mamba-ssm",
-                "flash-attn",
                 "xformers",
                 "autoawq",
                 "liger-kernel",
@@ -74,6 +67,7 @@ def parse_requirements():
             if (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers==0.0.29.post2")
+                extras_require_map["vllm"] = ["vllm==0.8.1"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
@@ -93,7 +87,7 @@ def parse_requirements():
 
     except PackageNotFoundError:
         pass
-    return _install_requires, _dependency_links
+    return _install_requires, _dependency_links, extras_require_map
 
 
 def get_package_version():
@@ -110,7 +104,50 @@ def get_package_version():
     return version_
 
 
-install_requires, dependency_links = parse_requirements()
+extras_require = {
+    "flash-attn": ["flash-attn==2.7.4.post1"],
+    "ring-flash-attn": [
+        "flash-attn==2.7.4.post1",
+        "ring-flash-attn>=0.1.4",
+        "yunchang==0.6.0",
+    ],
+    "deepspeed": [
+        "deepspeed==0.15.4",
+        "deepspeed-kernels",
+    ],
+    "mamba-ssm": [
+        "mamba-ssm==1.2.0.post1",
+        "causal_conv1d",
+    ],
+    "auto-gptq": [
+        "auto-gptq==0.5.1",
+    ],
+    "mlflow": [
+        "mlflow",
+    ],
+    "galore": [
+        "galore_torch",
+    ],
+    "apollo": [
+        "apollo-torch",
+    ],
+    "optimizers": [
+        "galore_torch",
+        "apollo-torch",
+        "lomo-optim==0.1.1",
+        "torch-optimi==0.2.1",
+    ],
+    "ray": [
+        "ray[train]",
+    ],
+    "vllm": [
+        "vllm==0.7.2",
+    ],
+}
+
+install_requires, dependency_links, extras_require_build = parse_requirements(
+    extras_require
+)
 
 setup(
     version=get_package_version(),
@@ -123,41 +160,5 @@ setup(
             "axolotl=axolotl.cli.main:main",
         ],
     },
-    extras_require={
+    extras_require=extras_require_build,
-        "flash-attn": [
-            "flash-attn==2.7.4.post1",
-        ],
-        "deepspeed": [
-            "deepspeed==0.16.4",
-            "deepspeed-kernels",
-        ],
-        "mamba-ssm": [
-            "mamba-ssm==1.2.0.post1",
-            "causal_conv1d",
-        ],
-        "auto-gptq": [
-            "auto-gptq==0.5.1",
-        ],
-        "mlflow": [
-            "mlflow",
-        ],
-        "lion-pytorch": [
-            "lion-pytorch==0.1.2",
-        ],
-        "galore": [
-            "galore_torch",
-        ],
-        "optimizers": [
-            "galore_torch",
-            "lion-pytorch==0.1.2",
-            "lomo-optim==0.1.1",
-            "torch-optimi==0.2.1",
-        ],
-        "ray": [
-            "ray[train]",
-        ],
-        "vllm": [
-            "vllm==0.7.2",
-        ],
-    },
 )

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.8.0.dev0"
+__version__ = "0.8.0"

src/axolotl/cli/args.py

@@ -35,6 +35,55 @@ class TrainerCliArgs:
     num_processes: Optional[int] = field(default=None)
 
 
+@dataclass
+class VllmServeCliArgs:
+    """Dataclass with CLI arguments for `axolotl vllm-serve` command."""
+
+    tensor_parallel_size: int = field(
+        default=1,
+        metadata={"help": "Number of tensor parallel workers to use."},
+    )
+    host: str = field(
+        default="0.0.0.0",  # nosec B104
+        metadata={"help": "Host address to run the server on."},
+    )
+    port: int = field(
+        default=8000,
+        metadata={"help": "Port to run the server on."},
+    )
+    gpu_memory_utilization: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV "
+            "cache on the device dedicated to generation powered by vLLM. Higher values will increase the KV cache "
+            "size and thus improve the model's throughput. However, if the value is too high, it may cause "
+            "out-of-memory (OOM) errors during initialization."
+        },
+    )
+    dtype: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Data type to use for vLLM generation. If set to 'auto', the data type will be automatically "
+            "determined based on the model configuration. Find the supported values in the vLLM documentation."
+        },
+    )
+    max_model_len: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "If set, the `max_model_len` to use for vLLM. This can be useful when running with reduced "
+            "`vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model "
+            "context size, which might be much larger than the KV cache, leading to inefficiencies."
+        },
+    )
+    enable_prefix_caching: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether to enable prefix caching in vLLM. If set to `True`, ensure that the model and the "
+            "hardware support this feature."
+        },
+    )
+
+
 @dataclass
 class EvaluateCliArgs:
     """Dataclass with CLI arguments for `axolotl evaluate` command."""

src/axolotl/cli/inference.py

@@ -56,7 +56,7 @@ def do_inference(
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Inference-specific CLI arguments.
     """
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None
@@ -151,7 +151,7 @@ def do_inference_gradio(
     """
     import gradio as gr
 
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
     prompter = cli_args.prompter
 
     prompter_module = None
@@ -256,7 +256,7 @@ def do_cli(
     """
     # pylint: disable=duplicate-code
     print_axolotl_text_art()
-    parsed_cfg = load_cfg(config, inference=True, **kwargs)
+    parsed_cfg = load_cfg(config, inference=True, rl=None, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser(InferenceCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/main.py

@@ -14,7 +14,12 @@ import yaml
 from dotenv import load_dotenv
 
 import axolotl
-from axolotl.cli.args import EvaluateCliArgs, PreprocessCliArgs, TrainerCliArgs
+from axolotl.cli.args import (
+    EvaluateCliArgs,
+    PreprocessCliArgs,
+    TrainerCliArgs,
+    VllmServeCliArgs,
+)
 from axolotl.cli.sweeps import generate_sweep_configs
 from axolotl.cli.utils import (
     add_options_from_config,
@@ -23,9 +28,10 @@ from axolotl.cli.utils import (
     fetch_from_github,
     filter_none_kwargs,
 )
+from axolotl.cli.vllm_serve import do_vllm_serve
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
+from axolotl.utils.schemas.config import AxolotlInputConfig
 
 
 @click.group()
@@ -316,6 +322,14 @@ def fetch(directory: str, dest: Optional[str]) -> None:
     fetch_from_github(f"{directory}/", dest)
 
 
+@cli.command()
+@click.argument("config", type=click.Path(exists=True, path_type=str))
+@add_options_from_dataclass(VllmServeCliArgs)
+@filter_none_kwargs
+def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
+    do_vllm_serve(config, cli_args)
+
+
 cli.add_command(lm_eval)
 
 

src/axolotl/cli/merge_lora.py

@@ -27,7 +27,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     print_axolotl_text_art()
 
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg)
+    model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
@@ -44,6 +44,9 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         )
         tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
 
+        if processor:
+            processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """
@@ -71,8 +74,10 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         load_in_8bit=False,
         load_in_4bit=False,
         flash_attention=False,
+        sequence_parallel_degree=None,
         deepspeed=None,
         fsdp=None,
+        fsdp_config=None,
         **kwargs,
     )
 
@@ -83,13 +88,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
             f"Target directory for merge: `{parsed_cfg.lora_model_dir}` does not exist."
         )
 
-    parsed_cfg.load_in_4bit = False
-    parsed_cfg.load_in_8bit = False
-    parsed_cfg.flash_attention = False
-    parsed_cfg.deepspeed = None
-    parsed_cfg.fsdp = None
-    parsed_cfg.fsdp_config = None
-
     do_merge_lora(cfg=parsed_cfg)
 
 

src/axolotl/cli/train.py

@@ -17,13 +17,14 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
 
 LOG = logging.getLogger(__name__)
 
 
-def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
+def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     """
     Trains a `transformers` model by first loading the dataset(s) specified in the
     `axolotl` config, and then calling `axolotl.train.train`. Also runs the plugin
@@ -33,6 +34,9 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: Training-specific CLI arguments.
     """
+    # Enable expandable segments for cuda allocation to improve VRAM usage
+    set_pytorch_cuda_alloc_conf()
+
     print_axolotl_text_art()
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
@@ -44,16 +48,13 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
     model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+    del model, tokenizer, trainer
+
     plugin_manager = PluginManager.get_instance()
-
-    del model
-    del tokenizer
-    del trainer
-
     plugin_manager.post_train_unload(cfg)
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     """
     Parses `axolotl` config, CLI args, and calls `do_train`.
 

src/axolotl/cli/utils.py

@@ -13,11 +13,16 @@ from typing import Any, Callable, Type, Union, get_args, get_origin
 import click
 import requests
 from pydantic import BaseModel
-from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers import (
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    PreTrainedTokenizerFast,
+    ProcessorMixin,
+)
 
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer
+from axolotl.utils.models import load_model, load_processor, load_tokenizer
 
 configure_logging()
 LOG = logging.getLogger(__name__)
@@ -295,9 +300,13 @@ def load_model_and_tokenizer(
     *,
     cfg: DictDefault,
     inference: bool = False,
-) -> tuple[PreTrainedModel, PreTrainedTokenizer | PreTrainedTokenizerFast | Any]:
+) -> tuple[
+    PreTrainedModel,
+    PreTrainedTokenizer | PreTrainedTokenizerFast | Any,
+    ProcessorMixin | None,
+]:
     """
-    Helper function for loading a model and tokenizer specified in the given `axolotl`
+    Helper function for loading a model, tokenizer, and processor specified in the given `axolotl`
     config.
 
     Args:
@@ -305,7 +314,7 @@ def load_model_and_tokenizer(
         inference: Boolean denoting inference mode.
 
     Returns:
-        `transformers` model and tokenizer.
+        Tuple of (PreTrainedModel, PreTrainedTokenizer, ProcessorMixin).
     """
     LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
@@ -313,4 +322,9 @@ def load_model_and_tokenizer(
     LOG.info("loading model...")
     model, _ = load_model(cfg, tokenizer, inference=inference)
 
-    return model, tokenizer
+    processor = None
+    if cfg.is_multimodal:
+        LOG.info("loading processor...")
+        processor = load_processor(cfg, tokenizer)
+
+    return model, tokenizer, processor

src/axolotl/cli/vllm_serve.py

@@ -0,0 +1,55 @@
+"""
+CLI to start the vllm server for online RL
+"""
+
+from pathlib import Path
+from typing import Union
+
+from trl.scripts.vllm_serve import ScriptArguments
+from trl.scripts.vllm_serve import main as vllm_serve_main
+
+from axolotl.cli.config import load_cfg
+
+
+def do_vllm_serve(
+    config: Union[Path, str],
+    cli_args: dict,
+):
+    """
+    Starts the VLLM server for serving LLM models used for online RL
+
+    Args
+        :param cfg: Parsed doct of the YAML config
+        :param cli_args: dict of additional command-line arguments of type VllmServeCliArgs
+
+    Returns:
+        process_id: the process id of the started VLLM server
+    """
+    cfg = load_cfg(config)
+    model = cfg.base_model
+
+    tensor_parallel_size = (
+        cli_args.get("tensor_parallel_size") or cfg.vllm.tensor_parallel_size
+    )
+    host = cli_args.get("host") or cfg.vllm.host
+    port = cli_args.get("port") or cfg.vllm.port
+    gpu_memory_utilization = (
+        cli_args.get("gpu_memory_utilization") or cfg.vllm.gpu_memory_utilization
+    )
+    dtype = cli_args.get("dtype") or cfg.vllm.dtype
+    max_model_len = cli_args.get("max_model_len") or cfg.vllm.max_model_len
+    enable_prefix_caching = (
+        cli_args.get("enable_prefix_caching") or cfg.vllm.enable_prefix_caching
+    )
+
+    vllm_script_args = ScriptArguments(
+        model,
+        tensor_parallel_size=tensor_parallel_size,
+        host=host,
+        port=port,
+        gpu_memory_utilization=gpu_memory_utilization,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        enable_prefix_caching=enable_prefix_caching,
+    )
+    vllm_serve_main(vllm_script_args)

src/axolotl/core/trainer_builder.py

@@ -13,9 +13,7 @@
 # limitations under the License.
 
 # pylint: disable=too-many-lines
-"""
+"""Builder for the training args and trainer"""
-Builder for the training args and trainer
-"""
 
 import abc
 import importlib
@@ -38,7 +36,7 @@ from transformers import (
 from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
-from axolotl.core.trainers.base import (
+from axolotl.core.trainers import (
     AxolotlCPOTrainer,
     AxolotlKTOTrainer,
     AxolotlMambaTrainer,
@@ -62,6 +60,7 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
+from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
@@ -70,7 +69,6 @@ from axolotl.utils.callbacks import (
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
-    SaveModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
@@ -85,8 +83,8 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
-from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -250,7 +248,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.gc_steps:
             callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
-        callbacks.append(SaveModelCallback())
 
         return callbacks
 
@@ -527,9 +524,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             and self.cfg.eval_steps
             and self.cfg.save_steps % self.cfg.eval_steps == 0
         ) or False
+
+        # handle ddp
+        ddp_find_unused_parameters = None
+        if self.cfg.ddp:
+            ddp_find_unused_parameters = bool(self.cfg.ddp_find_unused_parameters)
         training_arguments_kwargs["ddp_find_unused_parameters"] = (
-            False if self.cfg.ddp else None
+            ddp_find_unused_parameters
         )
+
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
         report_to = []
@@ -749,6 +752,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.accelerator_config
             )
 
+        if self.cfg.image_size:
+            training_arguments_kwargs["image_size"] = self.cfg.image_size
+        if self.cfg.image_resize_algorithm:
+            training_arguments_kwargs["image_resize_algorithm"] = (
+                self.cfg.image_resize_algorithm
+            )
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
         if self.cfg.kd_alpha is not None:
@@ -764,6 +773,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kd_top_k_before_softmax
             )
 
+        training_arguments_kwargs["sequence_parallel_degree"] = (
+            self.cfg.sequence_parallel_degree
+        )
+
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
         elif self.cfg.process_reward_model:
@@ -847,9 +860,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if self.cfg.pretraining_sample_concatenation is False:
+            if (
-                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
+                self.cfg.pretraining_sample_concatenation is False
-            if self.cfg.micro_batch_size > 1:
+                or self.cfg.micro_batch_size > 1
+            ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
@@ -877,9 +891,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if "max_length" in kwargs:
                 kwargs.pop("max_length")
         elif use_batch_sampler_collator:
-            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
+            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES or (
-                collator = V2BatchSamplerDataCollatorForSeq2Seq
-            elif (
                 self.cfg.model_config_type in ["llama"]
                 and self.cfg.flash_attention is not True
             ):
@@ -889,8 +901,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             if self.cfg.processor_type and self.processor:
                 collator = MultiModalChatDataCollator
-                kwargs["processor"] = self.processor
+                kwargs["processing_strategy"] = get_processing_strategy(
-                kwargs["chat_template"] = training_args.chat_template
+                    self.processor,
+                    training_args.chat_template,
+                    self.cfg.chat_template,
+                    image_size=training_args.image_size,
+                    image_resize_algorithm=training_args.image_resize_algorithm,
+                )
             elif self.cfg.batch_flattening:
                 collator = DataCollatorWithFlattening
                 collator_args.pop(0)
@@ -910,6 +927,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = DataCollatorForSeq2Seq
 
         kwargs["return_tensors"] = "pt"
+        if issubclass(collator, DataCollatorForSeq2Seq):
+            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
 
         return collator(
             *collator_args,
@@ -922,7 +941,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
-        callbacks.append(SaveModelCallback())
 
         return callbacks
 

src/axolotl/core/trainers/__init__.py

@@ -0,0 +1,18 @@
+"""Init for axolotl.core.trainers"""
+
+# pylint: disable=unused-import
+# flake8: noqa
+
+from .base import AxolotlTrainer
+from .dpo.trainer import AxolotlDPOTrainer
+from .grpo.trainer import AxolotlGRPOTrainer
+from .mamba import AxolotlMambaTrainer
+from .relora import ReLoRATrainer
+from .trl import (
+    AxolotlCPOTrainer,
+    AxolotlKTOTrainer,
+    AxolotlORPOTrainer,
+    AxolotlPRMTrainer,
+    AxolotlRewardTrainer,
+    TRLPPOTrainer,
+)

src/axolotl/core/trainers/base.py

@@ -1,365 +1,49 @@
-"""
+"""Module for customized trainers"""
-module for customized trainers
+
-"""
+# pylint: disable=too-many-lines
 
 from __future__ import annotations
 
-# pylint: disable=too-many-lines
 import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Dict, Literal, Optional
+from typing import Literal
 
+import datasets
 import torch
 from datasets import Dataset
-from peft.optimizers import create_loraplus_optimizer
+from torch.utils.data import (
-from torch import nn
+    BatchSampler,
-from torch.optim.lr_scheduler import OneCycleLR
+    DataLoader,
-from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
+    RandomSampler,
+    Sampler,
+    SequentialSampler,
+)
 from transformers import Trainer
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
-from transformers.utils import is_sagemaker_mp_enabled
-from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
+from typing_extensions import override
 
-from axolotl.integrations.base import BaseOptimizerFactory
+from axolotl.core.trainers.mixins import (
-from axolotl.monkeypatch.relora import ReLoRAScheduler
+    OptimizerMixin,
-from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
+    RngLoaderMixin,
-from axolotl.utils.schedulers import (
+    SchedulerMixin,
-    RexLR,
+    SequenceParallelMixin,
-    get_cosine_schedule_with_min_lr,
-    get_cosine_schedule_with_quadratic_warmup,
-    get_cosine_schedule_with_warmup_decay_constant,
 )
+from axolotl.core.trainers.utils import (
+    sanitize_kwargs_for_ds_tagging,
+    sanitize_kwargs_for_tagging,
+)
+from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
-if is_sagemaker_mp_enabled():
+LOG = logging.getLogger(__name__)
-    import smdistributed.modelparallel.torch as smp
-
-LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
-def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
+class AxolotlTrainer(
-    if isinstance(tag_names, str):
+    SchedulerMixin, OptimizerMixin, RngLoaderMixin, SequenceParallelMixin, Trainer
-        tag_names = [tag_names]
+):
-
+    """Extend the base Trainer for axolotl helpers"""
-    if kwargs is not None:
-        if "tags" not in kwargs:
-            kwargs["tags"] = tag_names
-        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
-            kwargs["tags"].extend(tag_names)
-        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
-            tag_names.append(kwargs["tags"])
-            kwargs["tags"] = tag_names
-
-    return kwargs
-
-
-def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
-    if isinstance(dataset_tags, str):
-        dataset_tags = [dataset_tags]
-
-    if (dataset_tags is not None) and (kwargs is not None):
-        if "dataset_tags" not in kwargs:
-            kwargs["dataset_tags"] = dataset_tags
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
-            kwargs["dataset_tags"].extend(dataset_tags)
-        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
-            dataset_tags.append(kwargs["dataset_tags"])
-            kwargs["dataset_tags"] = dataset_tags
-
-    return kwargs
-
-
-class SchedulerMixin(Trainer):
-    """
-    Mixin class for scheduler setup in CausalTrainer.
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_scheduler(
-        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
-    ):
-        """
-        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
-        passed as an argument.
-
-        Args:
-            num_training_steps (int): The number of training steps to do.
-            optimizer (torch.optim.Optimizer): The training optimizer
-        """
-        use_cosine_quadratic = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.lr_quadratic_warmup is True
-        )
-
-        use_cosine_min_lr = (
-            self.args.lr_scheduler_type == "cosine"
-            and self.args.cosine_min_lr_ratio is not None
-        )
-
-        # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
-            # fmt: on
-            if self.args.alternate_lr_scheduler_type == "one_cycle":
-                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
-                pct_start = num_warmup_steps / num_training_steps
-                extra_lr_kwargs = {}
-                if "pct_start" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["pct_start"] = pct_start
-                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
-                    extra_lr_kwargs["anneal_strategy"] = "cos"
-
-                self.lr_scheduler = OneCycleLR(
-                    optimizer,
-                    max_lr=self.args.learning_rate,
-                    total_steps=num_training_steps,
-                    **extra_lr_kwargs,
-                    **self.args.lr_scheduler_kwargs,
-                )
-            elif self.args.alternate_lr_scheduler_type == "rex":
-                if use_cosine_min_lr:
-                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-
-                self.lr_scheduler = RexLR(
-                    optimizer=optimizer,
-                    max_lr=self.args.learning_rate,
-                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
-                    total_steps=num_training_steps,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                )
-            elif use_cosine_quadratic:
-                if use_cosine_min_lr:
-                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
-
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                )
-            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
-                )
-            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
-                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
-                    optimizer,
-                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
-                    num_training_steps=num_training_steps,
-                    min_lr_ratio=self.args.cosine_min_lr_ratio,
-                )
-            else:
-                return super().create_scheduler(num_training_steps, optimizer=optimizer)
-        else:
-            if use_cosine_quadratic:
-                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
-
-            if use_cosine_min_lr:
-                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
-
-        return self.lr_scheduler
-
-
-class OptimizerMixin(Trainer):
-    """
-    Mixin class for shared handling of building custom optimizers
-    """
-
-    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-
-    def create_optimizer_grouped_parameters(
-        self, opt_model, optimizer_kwargs
-    ) -> list[dict]:
-        decay_parameters = self.get_decay_parameter_names(opt_model)
-        params: dict = {
-            "to_weight_decay": {},  # LayerNorm and bias
-            "embeddings": {},  # lm_head, embed_tokens,
-            "no_weight_decay": {},
-        }
-        lr_groups_lookup = {}
-        lr_groups_learning_rates = {}
-        if self.args.lr_groups:
-            for lr_group in self.args.lr_groups:
-                group_name = lr_group["name"]
-                group_modules = lr_group["modules"]
-                for module in group_modules:
-                    lr_groups_lookup[module] = group_name
-                lr_groups_learning_rates[group_name] = lr_group["lr"]
-                params[f"to_weight_decay_{group_name}"] = {}
-
-        for name, param in opt_model.named_parameters():
-            if not param.requires_grad:
-                continue
-            if name.endswith("modules_to_save.default.weight") or any(
-                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
-            ):
-                params["embeddings"][name] = param
-            elif name in decay_parameters:
-                lr_group_modules = [
-                    group_modules
-                    for group_modules in lr_groups_lookup
-                    if group_modules in name
-                ]
-                if lr_groups_lookup and any(lr_group_modules):
-                    lr_group_module = lr_group_modules[0]
-                    group_name = lr_groups_lookup[lr_group_module]
-                    params[f"to_weight_decay_{group_name}"][name] = param
-                else:
-                    params["to_weight_decay"][name] = param
-            else:
-                params["no_weight_decay"][name] = param
-        optimizer_grouped_parameters = []
-        if params["to_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["to_weight_decay"].values()),
-                    "weight_decay": self.args.weight_decay,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-            if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
-            elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["embeddings"].values()),
-                    "weight_decay": 0.0,
-                    "lr": lr,
-                }
-            )
-        if params["no_weight_decay"]:
-            optimizer_grouped_parameters.append(
-                {
-                    "params": list(params["no_weight_decay"].values()),
-                    "weight_decay": 0.0,
-                    "lr": optimizer_kwargs["lr"],
-                }
-            )
-        for group_name, group_lr in lr_groups_learning_rates.items():
-            if params[f"to_weight_decay_{group_name}"]:
-                optimizer_grouped_parameters.append(
-                    {
-                        "params": list(
-                            params[f"to_weight_decay_{group_name}"].values()
-                        ),
-                        "weight_decay": self.args.weight_decay,
-                        "lr": group_lr,
-                    }
-                )
-
-        return optimizer_grouped_parameters
-
-    def create_optimizer(self):
-        if (
-            self.args.loraplus_lr_ratio is None
-            and self.args.embedding_lr_scale is None
-            and self.args.embedding_lr is None
-            and self.args.lr_groups is None
-            and self.optimizer_cls_and_kwargs is None
-        ):
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-
-        if (
-            not self.optimizer
-            and self.optimizer_cls_and_kwargs is not None
-            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
-        ):
-            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            self.optimizer = optimizer_factory_cls()(
-                opt_model, self.args, **optimizer_kwargs
-            )
-
-        if not self.optimizer:
-            if self.optimizer_cls_and_kwargs is not None:
-                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
-            else:
-                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
-                    self.args, opt_model
-                )
-
-            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
-                opt_model, optimizer_kwargs
-            )
-
-            if self.args.loraplus_lr_ratio is not None:
-                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-                loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", 1e-6
-                )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                    opt_model,
-                    optimizer_cls,
-                    loraplus_lr_ratio=loraplus_lr_ratio,
-                    loraplus_lr_embedding=loraplus_lr_embedding,
-                    **optimizer_kwargs,
-                )
-            else:
-                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for GaLore optimizer.
-                if "params" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
-
-                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
-                # e.g. for LOMO optimizer.
-                if "model" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
-
-                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
-                # to avoid arguments conflicts.
-                if "optimizer_dict" in optimizer_kwargs:
-                    optimizer_grouped_parameters = optimizer_kwargs.pop(
-                        "optimizer_dict"
-                    )
-
-                self.optimizer = optimizer_cls(
-                    optimizer_grouped_parameters, **optimizer_kwargs
-                )
-
-            if optimizer_cls.__name__ == "Adam8bit":
-                import bitsandbytes
-
-                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-                skipped = 0
-                for module in opt_model.modules():
-                    if isinstance(module, nn.Embedding):
-                        skipped += sum(
-                            {
-                                p.data_ptr(): p.numel() for p in module.parameters()
-                            }.values()
-                        )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                        manager.register_module_override(
-                            module, "weight", {"optim_bits": 32}
-                        )
-                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-                LOG.info(f"skipped: {skipped/2**20}M params")
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
-
-class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
-    """
-    Extend the base Trainer for axolotl helpers
-    """
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
@@ -376,12 +60,18 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         self.eval_data_collator = eval_data_collator
         self.dataset_tags = dataset_tags
         self._signature_columns = None  # workaround for pylint
+
         super().__init__(*_args, **kwargs)
+
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
+        # Initialize sequence parallelism if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._setup_sequence_parallel()
+
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.torch_compile:
             torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
@@ -394,142 +84,248 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
             )
         return super()._wrap_model(model, training=training, dataloader=dataloader)
 
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+    def _create_multipack_sampler(
-        if self.args.sample_packing and not self.args.pretraining:
+        self, base_sampler: Sampler, dataset: Dataset
-            if self.args.multipack_real_batches:
+    ) -> MultipackBatchSampler:
-                batch_size = self.args.per_device_train_batch_size
+        """
-                batch_max_len = self.args.max_seq_length
+        Helper method to create a `MultipackBatchSampler` for multipacking sequences
-            else:
+        for training.
-                batch_size = 1
-                train_batch_size = (
-                    self.state.train_batch_size or self.args.per_device_train_batch_size
-                )
-                batch_max_len = train_batch_size * self.args.max_seq_length
 
-            if self.args.curriculum_sampling:
+        Args:
-                sampler = SequentialSampler(self.train_dataset)
+            base_sampler: Sampler to wrap with `MultipackBatchSampler`.
-            else:
+            dataset: Dataset to sample from.
-                sampler = RandomSampler(self.train_dataset)
 
-            return MultipackBatchSampler(
+        Returns:
-                sampler,
+            Multipack (sample packing) batch sampler.
-                lengths=get_dataset_lengths(self.train_dataset),
+        """
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+        if self.args.multipack_real_batches:
-                batch_max_len=batch_max_len,
+            batch_size = self.args.per_device_train_batch_size
-                batch_size=batch_size,
+            batch_max_len = self.args.max_seq_length
-                group_size=self.args.sample_packing_group_size,
+        else:
-                bin_size=self.args.sample_packing_bin_size,
+            batch_size = 1
-                drop_last=True,
+            train_batch_size = (
+                self.state.train_batch_size or self.args.per_device_train_batch_size
             )
-        if self.args.curriculum_sampling:
+            batch_max_len = train_batch_size * self.args.max_seq_length
-            return SequentialSampler(self.train_dataset)
-        return super()._get_train_sampler()
 
-    def _get_eval_sampler(
+        return MultipackBatchSampler(
-        self, eval_dataset: Dataset
+            base_sampler,
-    ) -> Optional[torch.utils.data.Sampler]:
+            lengths=get_dataset_lengths(dataset),
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            packing_efficiency_estimate=self.args.sample_packing_efficiency,
-            if self.args.multipack_real_batches:
+            batch_max_len=batch_max_len,
-                batch_size = self.args.per_device_eval_batch_size
+            batch_size=batch_size,
-                batch_max_len = self.args.max_seq_length
+            sequential=self.args.sample_packing_sequentially,
-            else:
+            drop_last=True,
-                batch_size = 1
+        )
-                batch_max_len = (
+
-                    self.args.per_device_eval_batch_size * self.args.max_seq_length
+    def _get_train_sampler(self) -> Sampler | None:
-                )
+        """
-            return MultipackBatchSampler(
+        Helper method to get the sampler for training. Handles cases for sequence
-                SequentialSampler(eval_dataset),
+        parallelism, sample packing, and curriculum sampling (sequential).
-                lengths=get_dataset_lengths(self.eval_dataset),
+
-                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+        Returns:
-                batch_max_len=batch_max_len,
+            If the dataset is non-empty, a sampler is returned, the type of which
-                batch_size=batch_size,
+                depends on the passed training args.
-                group_size=self.args.sample_packing_group_size,
+        """
-                bin_size=self.args.sample_packing_bin_size,
+        use_sample_packing = self.args.sample_packing and not self.args.pretraining
-                drop_last=True,
+
+        # Determine the base sampler first
+        if self.args.sequence_parallel_degree > 1:
+            base_sampler = self._sp_get_train_sampler(self.train_dataset)
+        elif self.args.curriculum_sampling:
+            base_sampler = SequentialSampler(self.train_dataset)
+        elif use_sample_packing:
+            base_sampler = RandomSampler(self.train_dataset)
+        else:
+            # Default to parent class implementation for standard random sampling
+            return super()._get_train_sampler()
+
+        # Apply multipack wrapper if needed
+        if use_sample_packing:
+            return self._create_multipack_sampler(
+                base_sampler=base_sampler,
+                dataset=self.train_dataset,
             )
-        return super()._get_eval_sampler(eval_dataset)
 
-    def get_train_dataloader(self) -> DataLoader:
+        return base_sampler
-        if self.args.sample_packing and not self.args.pretraining:
-            train_dataset = self.train_dataset
-            if "length" in train_dataset.features.keys():
-                train_dataset = train_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self._train_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params["prefetch_factor"] = (
-                    self.args.dataloader_prefetch_factor
-                )
 
-            sampler = self._get_train_sampler()
+    def _get_eval_sampler(self, eval_dataset: Dataset | None = None) -> Sampler | None:
+        """
+        Helper method to get the sampler for evaluation. Handles sequence parallelism
+        and sample packing cases.
+
+        Returns:
+            If the dataset is non-empty, a sampler is returned, the type of which
+                depends on the passed training args.
+        """
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        # Multipacking enabled if training is enabled and eval is not explicitly disabled
+        use_multipack = (
+            self.args.sample_packing and self.args.eval_sample_packing is not False
+        )
+
+        # Determine the base sampler
+        if self.args.sequence_parallel_degree > 1:
+            base_sampler = self._sp_get_eval_sampler(eval_dataset)
+        elif use_multipack:
+            base_sampler = SequentialSampler(eval_dataset)
+        else:
+            return super()._get_eval_sampler(eval_dataset)
+
+        # Apply multipack wrapper if needed
+        if use_multipack:
+            return self._create_multipack_sampler(
+                base_sampler=base_sampler,
+                dataset=eval_dataset,
+            )
+
+        return base_sampler
+
+    def _create_dataloader_params(self, is_eval=False, custom_batch_size=None):
+        """Create common dataloader parameters for train or eval."""
+        batch_size = custom_batch_size or (
+            self.args.eval_batch_size if is_eval else self._train_batch_size
+        )
+
+        params = {
+            "batch_size": batch_size,
+            "collate_fn": self.data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+        }
+
+        # Add persistent workers only for training
+        if not is_eval and hasattr(self.args, "dataloader_persistent_workers"):
+            params["persistent_workers"] = self.args.dataloader_persistent_workers
+
+        # Add prefetch factor if specified
+        if self.args.dataloader_prefetch_factor:
+            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        return params
+
+    def _prepare_dataloader(
+        self, dataset, sampler, is_eval=False, custom_batch_size=None
+    ):
+        """Prepare a dataloader with the given dataset and sampler."""
+        # Get base parameters
+        dataloader_params = self._create_dataloader_params(is_eval, custom_batch_size)
+
+        # Add sampler configuration
+        if not isinstance(dataset, torch.utils.data.IterableDataset):
             if isinstance(sampler, BatchSampler):
+                # batch_size and batch_sampler are mutually exclusive
                 dataloader_params["batch_sampler"] = sampler
                 del dataloader_params["batch_size"]
             else:
                 dataloader_params["sampler"] = sampler
                 dataloader_params["drop_last"] = self.args.dataloader_drop_last
-            dataloader_params["worker_init_fn"] = seed_worker
 
+            if not is_eval:
+                dataloader_params["worker_init_fn"] = seed_worker
+
+        # Create the dataloader
+        dataloader = DataLoader(dataset, **dataloader_params)
+
+        if self.args.sample_packing and (
+            (not is_eval and not self.args.pretraining)
+            or (is_eval and self.args.eval_sample_packing is not False)
+        ):
             self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(train_dataset, **dataloader_params)
-            )
-        return super().get_train_dataloader()
 
-    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        # Return unprepared dataloader if using sequence parallelism
+        if self.args.sequence_parallel_degree > 1:
+            return dataloader
+
+        # Otherwise prepare with accelerator
+        return self.accelerator.prepare_data_loader(dataloader)
+
+    def get_train_dataloader(self) -> DataLoader:
+        """Get dataloader for training"""
+        train_dataset = self.train_dataset
+        data_collator = self.data_collator  # type: ignore
+
+        # Handle dataset preprocessing
+        if isinstance(train_dataset, datasets.Dataset):
+            if self.args.sample_packing and not self.args.pretraining:
+                train_dataset = train_dataset.remove_columns(["length"])
+            if not self.args.sample_packing or self.args.pretraining:
+                train_dataset = self._remove_unused_columns(
+                    train_dataset, description="training"
+                )
+        else:
+            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+                data_collator,
+                description="training",
+            )
+
+        # Get sampler and create dataloader
+        sampler = self._get_train_sampler()
+        return self._prepare_dataloader(train_dataset, sampler, is_eval=False)
+
+    def get_eval_dataloader(self, eval_dataset: Dataset | None = None) -> DataLoader:
+        """Get dataloader for evaluation"""
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        # Handle special case: sample packing is enabled but eval_sample_packing is False
         if self.args.sample_packing and self.args.eval_sample_packing is False:
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.eval_data_collator
             )
-            if eval_dataset:
+            if "length" in eval_dataset.column_names:
                 eval_dataset = eval_dataset.remove_columns(["length"])
             dataloader = super().get_eval_dataloader(eval_dataset)
             self.data_collator = (  # pylint: disable=attribute-defined-outside-init
                 self.train_data_collator
             )
+
             return dataloader
 
-        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+        # Handle sample packing or sequence parallelism
-            eval_dataset = (
+        if (
-                eval_dataset if eval_dataset is not None else self.eval_dataset
+            self.args.sample_packing
+            and self.args.eval_sample_packing is not False
+            or self.args.sequence_parallel_degree > 1
+        ):
+            # Get appropriate data collator
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.eval_data_collator
+                if hasattr(self, "eval_data_collator") and self.eval_data_collator
+                else self.data_collator
+            )
+            if "length" in eval_dataset.column_names:
+                eval_dataset = eval_dataset.remove_columns(["length"])
+
+            # Handle dataset preprocessing for SP
+            if self.args.sequence_parallel_degree > 1:
+                if isinstance(eval_dataset, datasets.Dataset):
+                    eval_dataset = self._remove_unused_columns(
+                        eval_dataset, description="evaluation"
+                    )
+                else:
+                    self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+                        self.data_collator, description="evaluation"
+                    )
+
+            # Use eval_batch_size for sample packing, per_device_eval_batch_size otherwise
+            batch_size = (
+                self.args.eval_batch_size
+                if self.args.sample_packing
+                else self.args.per_device_eval_batch_size
+            )
+            sampler = self._get_eval_sampler(eval_dataset)
+            dataloader = self._prepare_dataloader(
+                eval_dataset, sampler, is_eval=True, custom_batch_size=batch_size
             )
 
-            eval_sampler = self._get_eval_sampler(eval_dataset)
+            return dataloader
-            eval_dataset = eval_dataset.remove_columns(["length"])
-            data_collator = self.data_collator
-            dataloader_params = {
-                "batch_size": self.args.eval_batch_size,
-                "collate_fn": data_collator,
-                "num_workers": self.args.dataloader_num_workers,
-                "pin_memory": self.args.dataloader_pin_memory,
-            }
-            if self.args.dataloader_prefetch_factor:
-                dataloader_params["prefetch_factor"] = (
-                    self.args.dataloader_prefetch_factor
-                )
-
-            if isinstance(eval_sampler, BatchSampler):
-                dataloader_params["batch_sampler"] = eval_sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = eval_sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-            self.accelerator.even_batches = False
-            return self.accelerator.prepare_data_loader(
-                DataLoader(eval_dataset, **dataloader_params)
-            )
 
         return super().get_eval_dataloader(eval_dataset)
 
     def _get_bench_sampler(
         self, bench_dataset: Dataset
-    ) -> Optional[torch.utils.data.Sampler]:
+    ) -> torch.utils.data.Sampler | None:
         if self.args.world_size <= 1:
             return SequentialSampler(bench_dataset)
         return None
@@ -554,6 +350,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         return DataLoader(bench_dataset, **dataloader_params)
         # return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
 
+    @override
     def compute_loss(
         self, model, inputs, return_outputs=False, num_items_in_batch=None
     ):
@@ -570,6 +367,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 return_outputs=return_outputs,
                 num_items_in_batch=num_items_in_batch,
             )
+
         return super().compute_loss(
             model,
             inputs,
@@ -744,10 +542,10 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
+        kwargs = sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 
@@ -764,15 +562,13 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
 
         return res
 
-    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
+    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.
 
         Args:
-            logs (`Dict[str, float]`):
+            logs: The values to log.
-                The values to log.
+            start_time: The start of training.
-            start_time (`Optional[float]`):
-                The start of training.
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
@@ -784,7 +580,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         return super().log(logs, start_time)
 
     def store_metrics(
-        self, metrics: Dict[str, float], train_eval: Literal["train", "eval"] = "train"
+        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
     ) -> None:
         for key, value in metrics.items():
             self._stored_metrics[train_eval][key].append(value)
@@ -796,111 +592,3 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
-
-
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """
-    Mamba specific trainer to handle loss calculation
-    """
-
-    tag_names = ["axolotl", "mamba"]
-
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
-
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
-
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
-
-        return lm_loss
-
-
-class ReLoRATrainer(AxolotlTrainer):
-    """
-    Trainer subclass that uses the OneCycleLR scheduler
-    """
-
-    tag_names = ["axolotl", "relora"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr_scheduler = None
-
-    def create_scheduler(
-        self,
-        num_training_steps: int,
-        optimizer: Optional[torch.optim.Optimizer] = None,
-    ):
-        optimizer = self.optimizer if optimizer is None else optimizer
-        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
-
-        if self.args.relora_steps:
-            warmup_steps = (
-                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
-            )
-            anneal_steps = (
-                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
-            )
-            self.lr_scheduler = ReLoRAScheduler(
-                optimizer,
-                lr_scheduler,
-                self.args.relora_steps,
-                anneal_steps,
-                warmup_steps,
-            )
-        else:
-            self.lr_scheduler = lr_scheduler
-
-        return self.lr_scheduler
-
-
-class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-
-class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
-    """
-    Extend the base KTOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "kto"]
-
-
-class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
-    """
-    Extend the base CPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "cpo"]
-
-
-class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
-    """
-    Extend the base RewardTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "reward"]
-
-
-class AxolotlPRMTrainer(SchedulerMixin, PRMTrainer):
-    """
-    Extend the base trl.PRMTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "prm"]

src/axolotl/core/trainers/dpo/trainer.py

@@ -13,17 +13,17 @@ from transformers import Trainer
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 
-from axolotl.core.trainers.base import (
+from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
-    SchedulerMixin,
+from axolotl.core.trainers.utils import (
-    _sanitize_kwargs_for_ds_tagging,
+    sanitize_kwargs_for_ds_tagging,
-    _sanitize_kwargs_for_tagging,
+    sanitize_kwargs_for_tagging,
 )
 
 if is_sagemaker_mp_enabled():
     import smdistributed.modelparallel.torch as smp
 
 
-class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
+class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
     """
     Extend the base DPOTrainer for axolotl helpers
     """
@@ -74,10 +74,10 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
+        kwargs = sanitize_kwargs_for_ds_tagging(
             dataset_tags=self.dataset_tags, kwargs=kwargs
         )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+        kwargs = sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
 

src/axolotl/core/trainers/grpo/__init__.py

@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
+from axolotl.utils.schemas.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -40,18 +40,15 @@ class GRPOStrategy:
 
         if trl.use_vllm:
             grpo_args_kwargs["use_vllm"] = trl.use_vllm
-            grpo_args_kwargs["vllm_device"] = (
+            grpo_args_kwargs["vllm_server_host"] = trl.vllm_server_host
-                trl.vllm_device if trl.vllm_device else "auto"
+            grpo_args_kwargs["vllm_server_port"] = trl.vllm_server_port
-            )
+            if trl.vllm_server_timeout:
-
+                grpo_args_kwargs["vllm_server_timeout"] = trl.vllm_server_timeout
-            if trl.vllm_gpu_memory_utilization:
+            if trl.vllm_guided_decoding_regex:
-                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
+                grpo_args_kwargs["vllm_guided_decoding_regex"] = (
-                    trl.vllm_gpu_memory_utilization
+                    trl.vllm_guided_decoding_regex
                 )
 
-            if trl.vllm_max_model_len:
-                grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
-
         if trl.num_generations:
             grpo_args_kwargs["num_generations"] = trl.num_generations
 
@@ -70,6 +67,25 @@ class GRPOStrategy:
         if trl.reward_weights:
             grpo_args_kwargs["reward_weights"] = trl.reward_weights
 
+        if trl.scale_rewards is not None:
+            grpo_args_kwargs["scale_rewards"] = trl.scale_rewards
+
+        if trl.temperature is not None:
+            grpo_args_kwargs["temperature"] = trl.temperature
+        if trl.top_p is not None:
+            grpo_args_kwargs["top_p"] = trl.top_p
+        if trl.top_k is not None:
+            grpo_args_kwargs["top_k"] = trl.top_k
+        if trl.min_p is not None:
+            grpo_args_kwargs["min_p"] = trl.min_p
+        if trl.repetition_penalty is not None:
+            grpo_args_kwargs["repetition_penalty"] = trl.repetition_penalty
+
+        if trl.num_iterations is not None:
+            grpo_args_kwargs["num_iterations"] = trl.num_iterations
+        if trl.epsilon is not None:
+            grpo_args_kwargs["epsilon"] = trl.epsilon
+
         return grpo_args_kwargs
 
     @classmethod

src/axolotl/core/trainers/grpo/trainer.py

@@ -2,108 +2,68 @@
 Axolotl GRPO trainer
 """
 
-from accelerate.utils import is_peft_model
+from contextlib import nullcontext
-from accelerate.utils.other import is_compiled_module
-from transformers import PreTrainedModel
-from trl import GRPOConfig, GRPOTrainer
-from trl.models import unwrap_model_for_generation
 
-from axolotl.core.trainers.base import SchedulerMixin
+from accelerate.utils import is_deepspeed_available, is_peft_model
+from trl import GRPOTrainer
+from trl.extras.profiling import profiling_decorator
+
+from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
+
+if is_deepspeed_available():
+    import deepspeed
 
 
-# mypy: ignore-errors
+class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
-class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
     """
     Extend the base GRPOTrainer for axolotl helpers
     """
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    def __init__(self, *args, **kwargs):
+    @profiling_decorator
-        super().__init__(*args, **kwargs)
+    def _move_model_to_vllm(self):
-
+        # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
-        # pylint: disable=access-member-before-definition
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        # Enable gradient checkpointing if requested
+        zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
-        if kwargs["args"].gradient_checkpointing:
+        gather_if_zero3 = (
-            # Ensure use_cache is disabled
+            deepspeed.zero.GatheredParameters if zero_stage_3 else nullcontext
-            if hasattr(self.model, "config"):
-                self.model.config.use_cache = False
-
-            # Enable gradient checkpointing on the base model for PEFT
-            if is_peft_model(self.model) and hasattr(
-                self.model.base_model, "gradient_checkpointing_enable"
-            ):
-                self.model.base_model.gradient_checkpointing_enable()
-            # Enable gradient checkpointing for non-PEFT models
-            elif hasattr(self.model, "gradient_checkpointing_enable"):
-                self.model.gradient_checkpointing_enable()
-            self.model = self._enable_gradient_checkpointing(self.model, kwargs["args"])
-        # pylint: enable=access-member-before-definition
-
-    def _enable_gradient_checkpointing(
-        self, model: PreTrainedModel, args: GRPOConfig
-    ) -> PreTrainedModel:
-        """Enables gradient checkpointing for the model."""
-        # pylint: disable=unused-argument,redefined-builtin
-        gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
-        use_reentrant = (
-            "use_reentrant" not in gradient_checkpointing_kwargs
-            or gradient_checkpointing_kwargs["use_reentrant"]
         )
 
-        if use_reentrant:
+        if is_peft_model(self.model):
-            if hasattr(model, "enable_input_require_grads"):
+            # With PEFT and DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as merging
-                model.enable_input_require_grads()
+            # adapters in a sharded manner is not supported.
-            else:
+            with gather_if_zero3(list(self.model.parameters())):
+                self.model.merge_adapter()
 
-                def make_inputs_require_grad(module, input, output):
+                # Update vLLM weights while parameters are gathered
-                    output.requires_grad_(True)
+                for name, param in self.model.named_parameters():
+                    # When using PEFT, we need to recover the original parameter name and discard some parameters
+                    name = (
+                        name.removeprefix("base_model.model.")
+                        .removeprefix("base_model.model.")
+                        .replace(".base_layer", "")
+                    )
+                    if self.model.prefix in name:
+                        continue
+                    # When module to save, remove its prefix and discard the original module
+                    if "original_module" in name:
+                        continue
+                    name = name.replace("modules_to_save.default.", "")
 
-                model.get_input_embeddings().register_forward_hook(
+                    if self.accelerator.is_main_process:
-                    make_inputs_require_grad
+                        self.vllm_client.update_named_param(name, param.data)
-                )
 
-        return model
+                # Unmerge adapters while parameters are still gathered
-        # pylint: enable=unused-argument,redefined-builtin
+                self.model.unmerge_adapter()
+                # Parameters will automatically be repartitioned when exiting the context
+        else:
+            # For non-PEFT models, simply gather and update each parameter individually.
+            for name, param in self.model.named_parameters():
+                with gather_if_zero3([param]):
+                    if self.accelerator.is_main_process:
+                        self.vllm_client.update_named_param(name, param.data)
 
-    def _move_model_to_vllm(self):
+        # Reset cache on main process
-        with unwrap_model_for_generation(
+        if self.accelerator.is_main_process:
-            self.model,
+            self.vllm_client.reset_prefix_cache()
-            self.accelerator,
-            gather_deepspeed3_params=self.args.ds3_gather_for_generation,
-        ) as unwrapped_model:
-            if is_compiled_module(unwrapped_model):
-                unwrapped_model = (
-                    unwrapped_model._orig_mod  # pylint: disable=protected-access
-                )
-            if is_peft_model(unwrapped_model):
-                unwrapped_model.merge_adapter()
-                state_dict = unwrapped_model.state_dict()
-                # Remove base_model and base_layer prefixes
-                state_dict = {
-                    k.removeprefix("base_model.model.")
-                    .removeprefix("base_model.model.")
-                    .replace(".base_layer", ""): v
-                    for k, v in state_dict.items()
-                }
-                # Remove values with adapter prefix (example: "_lora")
-                state_dict = {
-                    k: v
-                    for k, v in state_dict.items()
-                    if unwrapped_model.prefix not in k
-                }
-                # When module to save, remove its prefix and discard the original module
-                state_dict = {
-                    k.replace("modules_to_save.default.", ""): v
-                    for k, v in state_dict.items()
-                    if "original_module" not in k
-                }
-            else:
-                state_dict = unwrapped_model.state_dict()
-            if self.accelerator.is_main_process:
-                llm_model = (
-                    self.llm.llm_engine.model_executor.driver_worker.model_runner.model
-                )
-                llm_model.load_weights(state_dict.items())
-            if is_peft_model(unwrapped_model):
-                unwrapped_model.unmerge_adapter()

src/axolotl/core/trainers/mamba.py

@@ -0,0 +1,32 @@
+"""Module for mamba trainer"""
+
+import torch
+
+from axolotl.core.trainers.base import AxolotlTrainer
+
+
+class AxolotlMambaTrainer(AxolotlTrainer):
+    """Mamba specific trainer to handle loss calculation"""
+
+    tag_names = ["axolotl", "mamba"]
+
+    def compute_loss(
+        self,
+        model,
+        inputs,
+        return_outputs=False,  # pylint: disable=unused-argument
+        num_items_in_batch=None,  # pylint: disable=unused-argument
+    ):
+        input_ids = inputs.pop("input_ids")
+        lm_logits = model(input_ids).logits
+
+        labels = input_ids.to(lm_logits.device)
+        shift_logits = lm_logits[:, :-1, :].contiguous()
+        labels = labels[:, 1:].contiguous()
+
+        loss_fct = torch.nn.CrossEntropyLoss()
+        lm_loss = loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
+        )
+
+        return lm_loss

src/axolotl/core/trainers/mixins/__init__.py

@@ -0,0 +1,9 @@
+"""Init for axolotl.core.trainers.mixins"""
+
+# pylint: disable=unused-import
+# flake8: noqa
+
+from .optimizer import OptimizerMixin
+from .rng_state_loader import RngLoaderMixin
+from .scheduler import SchedulerMixin
+from .sequence_parallel import SequenceParallelMixin

src/axolotl/core/trainers/mixins/optimizer.py

@@ -0,0 +1,201 @@
+"""Module for Axolotl trainer optimizer mixin"""
+
+import logging
+
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
+from transformers.trainer import Trainer
+from transformers.utils import is_sagemaker_mp_enabled
+
+from axolotl.integrations.base import BaseOptimizerFactory
+
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+LOG = logging.getLogger(__name__)
+
+
+class OptimizerMixin(Trainer):
+    """Mixin class for shared handling of building custom optimizers"""
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_optimizer_grouped_parameters(
+        self, opt_model, optimizer_kwargs
+    ) -> list[dict]:
+        decay_parameters = self.get_decay_parameter_names(opt_model)
+        params: dict = {
+            "to_weight_decay": {},  # LayerNorm and bias
+            "embeddings": {},  # lm_head, embed_tokens,
+            "no_weight_decay": {},
+        }
+        lr_groups_lookup = {}
+        lr_groups_learning_rates = {}
+        if self.args.lr_groups:
+            for lr_group in self.args.lr_groups:
+                group_name = lr_group["name"]
+                group_modules = lr_group["modules"]
+                for module in group_modules:
+                    lr_groups_lookup[module] = group_name
+                lr_groups_learning_rates[group_name] = lr_group["lr"]
+                params[f"to_weight_decay_{group_name}"] = {}
+
+        for name, param in opt_model.named_parameters():
+            if not param.requires_grad:
+                continue
+            if name.endswith("modules_to_save.default.weight") or any(
+                embed_name in name for embed_name in ["embed_tokens", "lm_head"]
+            ):
+                params["embeddings"][name] = param
+            elif name in decay_parameters:
+                lr_group_modules = [
+                    group_modules
+                    for group_modules in lr_groups_lookup
+                    if group_modules in name
+                ]
+                if lr_groups_lookup and any(lr_group_modules):
+                    lr_group_module = lr_group_modules[0]
+                    group_name = lr_groups_lookup[lr_group_module]
+                    params[f"to_weight_decay_{group_name}"][name] = param
+                else:
+                    params["to_weight_decay"][name] = param
+            else:
+                params["no_weight_decay"][name] = param
+        optimizer_grouped_parameters = []
+        if params["to_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["to_weight_decay"].values()),
+                    "weight_decay": self.args.weight_decay,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        if params["embeddings"]:
+            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+            if self.args.embedding_lr_scale:
+                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+            elif self.args.embedding_lr:
+                lr = self.args.embedding_lr  # pylint: disable=invalid-name
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["embeddings"].values()),
+                    "weight_decay": 0.0,
+                    "lr": lr,
+                }
+            )
+        if params["no_weight_decay"]:
+            optimizer_grouped_parameters.append(
+                {
+                    "params": list(params["no_weight_decay"].values()),
+                    "weight_decay": 0.0,
+                    "lr": optimizer_kwargs["lr"],
+                }
+            )
+        for group_name, group_lr in lr_groups_learning_rates.items():
+            if params[f"to_weight_decay_{group_name}"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(
+                            params[f"to_weight_decay_{group_name}"].values()
+                        ),
+                        "weight_decay": self.args.weight_decay,
+                        "lr": group_lr,
+                    }
+                )
+
+        return optimizer_grouped_parameters
+
+    def create_optimizer(self):
+        if (
+            self.args.loraplus_lr_ratio is None
+            and self.args.embedding_lr_scale is None
+            and self.args.embedding_lr is None
+            and self.args.lr_groups is None
+            and self.optimizer_cls_and_kwargs is None
+        ):
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+
+        if (
+            not self.optimizer
+            and self.optimizer_cls_and_kwargs is not None
+            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
+        ):
+            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            self.optimizer = optimizer_factory_cls()(
+                opt_model, self.args, **optimizer_kwargs
+            )
+
+        if not self.optimizer:
+            if self.optimizer_cls_and_kwargs is not None:
+                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            else:
+                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
+                    self.args, opt_model
+                )
+
+            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
+                opt_model, optimizer_kwargs
+            )
+
+            if self.args.loraplus_lr_ratio is not None:
+                loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+                loraplus_lr_embedding = getattr(
+                    self.args, "loraplus_lr_embedding", 1e-6
+                )
+                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                    opt_model,
+                    optimizer_cls,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
+                )
+            else:
+                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for GaLore optimizer.
+                if "params" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
+
+                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for LOMO optimizer.
+                if "model" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
+
+                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
+                # to avoid arguments conflicts.
+                if "optimizer_dict" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop(
+                        "optimizer_dict"
+                    )
+
+                self.optimizer = optimizer_cls(
+                    optimizer_grouped_parameters, **optimizer_kwargs
+                )
+
+            if optimizer_cls.__name__ == "Adam8bit":
+                import bitsandbytes
+
+                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
+
+                skipped = 0
+                for module in opt_model.modules():
+                    if isinstance(module, nn.Embedding):
+                        skipped += sum(
+                            {
+                                p.data_ptr(): p.numel() for p in module.parameters()
+                            }.values()
+                        )
+                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        manager.register_module_override(
+                            module, "weight", {"optim_bits": 32}
+                        )
+                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
+                LOG.info(f"skipped: {skipped/2**20}M params")
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer

src/axolotl/core/trainers/mixins/rng_state_loader.py

@@ -0,0 +1,67 @@
+"""
+Temporary fix/override for bug in resume from checkpoint
+
+See https://github.com/huggingface/transformers/pull/37162
+
+TODO: Remove when upstream added PR to release
+"""
+
+import logging
+import os
+import random
+
+import numpy as np
+import torch
+from transformers import Trainer, is_torch_npu_available
+from transformers.trainer import safe_globals
+from transformers.trainer_pt_utils import set_rng_state_for_device
+from transformers.training_args import ParallelMode
+
+LOG = logging.getLogger(__name__)
+
+
+class RngLoaderMixin(Trainer):
+    """
+    mixin for method override to load RNG states from a checkpoint
+    """
+
+    def _load_rng_state(self, checkpoint):
+        # Load RNG states from `checkpoint`
+        if checkpoint is None:
+            return
+
+        if self.args.world_size > 1:
+            process_index = self.args.process_index
+            rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
+            if not os.path.isfile(rng_file):
+                LOG.info(
+                    f"Didn't find an RNG file for process {process_index}, if you are resuming a training that "
+                    "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
+                )
+                return
+        else:
+            rng_file = os.path.join(checkpoint, "rng_state.pth")
+            if not os.path.isfile(rng_file):
+                LOG.info(
+                    "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
+                    "fashion, reproducibility is not guaranteed."
+                )
+                return
+
+        # Use safe_globals to ensure numpy RNG states can be deserialized safely under PyTorch 2.6+,
+        # which requires allowlisted classes when loading with weights_only=True.
+        with safe_globals():
+            checkpoint_rng_state = torch.load(rng_file)  # nosec B614
+        random.setstate(checkpoint_rng_state["python"])
+        np.random.set_state(checkpoint_rng_state["numpy"])
+        torch.random.set_rng_state(checkpoint_rng_state["cpu"])
+
+        is_distributed = self.args.parallel_mode == ParallelMode.DISTRIBUTED
+        if torch.cuda.is_available():
+            set_rng_state_for_device(
+                "CUDA", torch.cuda, checkpoint_rng_state, is_distributed
+            )
+        if is_torch_npu_available():
+            set_rng_state_for_device(
+                "NPU", torch.npu, checkpoint_rng_state, is_distributed
+            )

src/axolotl/core/trainers/mixins/scheduler.py

@@ -0,0 +1,113 @@
+"""Module for Axolotl trainer scheduler mixin"""
+
+import logging
+
+import torch
+from torch.optim.lr_scheduler import OneCycleLR
+from transformers.trainer import Trainer
+
+from axolotl.utils.schedulers import (
+    RexLR,
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+    get_cosine_schedule_with_warmup_decay_constant,
+)
+
+LOG = logging.getLogger(__name__)
+
+
+class SchedulerMixin(Trainer):
+    """
+    Mixin class for scheduler setup in CausalTrainer.
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+    ):
+        """
+        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        passed as an argument.
+
+        Args:
+            num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
+        """
+        use_cosine_quadratic = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.lr_quadratic_warmup is True
+        )
+
+        use_cosine_min_lr = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.cosine_min_lr_ratio is not None
+        )
+
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if self.args.alternate_lr_scheduler_type == "one_cycle":
+                num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
+                pct_start = num_warmup_steps / num_training_steps
+                extra_lr_kwargs = {}
+                if "pct_start" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["pct_start"] = pct_start
+                if "anneal_strategy" not in self.args.lr_scheduler_kwargs:
+                    extra_lr_kwargs["anneal_strategy"] = "cos"
+
+                self.lr_scheduler = OneCycleLR(
+                    optimizer,
+                    max_lr=self.args.learning_rate,
+                    total_steps=num_training_steps,
+                    **extra_lr_kwargs,
+                    **self.args.lr_scheduler_kwargs,
+                )
+            elif self.args.alternate_lr_scheduler_type == "rex":
+                if use_cosine_min_lr:
+                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+
+                self.lr_scheduler = RexLR(
+                    optimizer=optimizer,
+                    max_lr=self.args.learning_rate,
+                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
+                    total_steps=num_training_steps,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                )
+            elif use_cosine_quadratic:
+                if use_cosine_min_lr:
+                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
+
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                )
+            elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                    constant_lr_ratio=self.args.cosine_constant_lr_ratio,
+                )
+            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
+                assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                    min_lr_ratio=self.args.cosine_min_lr_ratio,
+                )
+            else:
+                return super().create_scheduler(num_training_steps, optimizer=optimizer)
+        else:
+            if use_cosine_quadratic:
+                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
+
+            if use_cosine_min_lr:
+                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
+
+        return self.lr_scheduler

src/axolotl/core/trainers/mixins/sequence_parallel.py

@@ -0,0 +1,182 @@
+"""Module for Axolotl trainer sequence parallelism mixin"""
+
+import logging
+from typing import Any
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from datasets import Dataset
+from torch import nn
+from torch.utils.data import DistributedSampler, Sampler
+
+from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+LOG = logging.getLogger(__name__)
+
+try:
+    from ring_flash_attn import update_ring_flash_attn_params
+except ImportError:
+    # We pass silently here, but raise an ImportError in our Axolotl config validation
+    # if cfg.sequence_parallel_degree > 1 and `ring-flash-attn` is not installed.
+    pass
+
+
+class SequenceParallelMixin:
+    """
+    Mixin class for sequence parallelism support in trainers.
+
+    This mixin provides functionality for handling sequence parallelism,
+    including creating appropriate samplers, managing data partitioning,
+    and updating ring flash attention parameters during training.
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+
+    def _setup_sequence_parallel(self):
+        """Set up sequence parallelism environment."""
+        self.ring_attn_group = get_ring_attn_group()
+
+    def _create_sequence_parallel_sampler(
+        self,
+        dataset: Dataset,
+        shuffle: bool = True,
+        is_eval: bool = False,
+    ) -> DistributedSampler:
+        """
+        Helper method to create sampler for sequence parallelism (SP).
+
+        We create a distributed sampler with rank equal to the SP group ID, which
+        means that all ranks in the SP group receive the same sample / set of samples
+        per training step. We also set the number of replicas equal to the number of
+        SP groups, which is a bit of a hack / unintended use, but works!
+
+        Args:
+            dataset: Dataset to sample from.
+            shuffle: Whether to shuffle the dataset.
+            is_eval: Whether we are creating a sampler for evaluation or training.
+
+        Returns:
+            Distributed sampler.
+        """
+        num_sp_groups = self.args.world_size // self.args.sequence_parallel_degree
+        sp_group_id = dist.get_rank() // self.args.sequence_parallel_degree
+
+        return DistributedSampler(
+            dataset,
+            num_replicas=num_sp_groups,
+            rank=sp_group_id,
+            seed=self.args.seed if shuffle else None,
+            shuffle=shuffle,
+            drop_last=not is_eval,
+        )
+
+    def _sp_get_train_sampler(self, dataset) -> Sampler | None:
+        """
+        Get a training sampler configured for sequence parallelism.
+
+        Args:
+            dataset: The training dataset
+
+        Returns:
+            Configured sequence parallel sampler.
+        """
+        return self._create_sequence_parallel_sampler(
+            dataset,
+            shuffle=not self.args.curriculum_sampling,
+        )
+
+    def _sp_get_eval_sampler(self, eval_dataset) -> Sampler | None:
+        """
+        Get an evaluation sampler configured for sequence parallelism.
+
+        Args:
+            eval_dataset: The evaluation dataset.
+
+        Returns:
+            Configured sequence parallel sampler.
+        """
+        return self._create_sequence_parallel_sampler(
+            eval_dataset, shuffle=False, is_eval=True
+        )
+
+    def _update_ring_flash_attn_params(self, inputs: dict[str, torch.Tensor | Any]):
+        """
+        Calculate the cu_seqlens for the current forward pass and pass the value to
+        the substituted ring_flash_attn. This is accomplished by using the passed
+        `input_ids`.
+
+        Args:
+            inputs: Current batch of inputs.
+        """
+        # At this point, inputs should already be partitioned by the sequence
+        # parallel data collator
+        batch_size = inputs["input_ids"].shape[0]
+        seq_len = inputs["input_ids"].shape[1]
+        packed_seq_lens = [seq_len] * batch_size
+
+        # Calculate the full sequence length across all GPUs in this SP group
+        total_seq_len = seq_len * self.args.sequence_parallel_degree
+
+        cu_seqlens = torch.cumsum(
+            torch.tensor(
+                packed_seq_lens, device=torch.cuda.current_device(), dtype=torch.int32
+            ),
+            dim=-1,
+            dtype=torch.int32,
+        )
+        cu_seqlens = F.pad(
+            F.pad(cu_seqlens, (1, 0), value=0), (0, 1), value=total_seq_len
+        )
+
+        update_ring_flash_attn_params(cu_seqlens, self.ring_attn_group)
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        num_items_in_batch: int | None = None,
+    ) -> torch.Tensor:
+        """
+        Perform a training step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
+
+        Args:
+            model: Model to perform training step for.
+            inputs: Dictionary mapping.
+        """
+        # Set up sequence parallelism for this step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
+
+        # Proceed with normal training step
+        return super().training_step(model, inputs, num_items_in_batch)  # type: ignore
+
+    def prediction_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        prediction_loss_only: bool,
+        ignore_keys: list[str] | None = None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
+        """
+        Perform a prediction step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
+
+        Args:
+            model: Model to perform prediction step for.
+            inputs: Dictionary mapping of inputs.
+            prediction_loss_only: Whether to return only the loss.
+            ignore_keys: Keys to ignore in the inputs.
+
+        Returns:
+            Tuple of (loss, logits, labels).
+        """
+        # Set up sequence parallelism for this prediction step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
+
+        # Proceed with normal prediction step
+        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)  # type: ignore

src/axolotl/core/trainers/relora.py

@@ -0,0 +1,43 @@
+"""Module for ReLoRA trainer"""
+
+import torch
+
+from axolotl.core.trainers.base import AxolotlTrainer
+from axolotl.monkeypatch.relora import ReLoRAScheduler
+
+
+class ReLoRATrainer(AxolotlTrainer):
+    """Trainer subclass that uses the `OneCycleLR` scheduler"""
+
+    tag_names = ["axolotl", "relora"]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lr_scheduler = None
+
+    def create_scheduler(
+        self,
+        num_training_steps: int,
+        optimizer: torch.optim.Optimizer | None = None,
+    ):
+        optimizer = self.optimizer if optimizer is None else optimizer
+        lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
+
+        if self.args.relora_steps:
+            warmup_steps = (
+                self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
+            )
+            anneal_steps = (
+                self.args.relora_anneal_steps if self.args.relora_anneal_steps else 1
+            )
+            self.lr_scheduler = ReLoRAScheduler(
+                optimizer,
+                lr_scheduler,
+                self.args.relora_steps,
+                anneal_steps,
+                warmup_steps,
+            )
+        else:
+            self.lr_scheduler = lr_scheduler
+
+        return self.lr_scheduler

src/axolotl/core/trainers/trl.py

@@ -1,16 +1,26 @@
-"""
+"""Module for TRL PPO trainer"""
-module for TRL PPO training
+
-"""
+from typing import Literal, Union
 
 import torch
 from tqdm import tqdm
-from trl import PPOTrainer
+from trl import (
+    CPOTrainer,
+    KTOTrainer,
+    ORPOTrainer,
+    PPOTrainer,
+    PRMTrainer,
+    RewardTrainer,
+)
+
+from axolotl.core.trainers.mixins import RngLoaderMixin
+from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
 
 
 class TRLPPOTrainer(PPOTrainer):
-    """
+    """Wrapper for TRL PPO trainer to handle customizations"""
-    wrapper for ppo trainer to handle customizations
+
-    """
+    tag_names = ["axolotl", "ppo"]
 
     def train(
         self,
@@ -31,9 +41,7 @@ class TRLPPOTrainer(PPOTrainer):
             "batch_size": 16,
         }
 
-        for epoch, batch in tqdm(  # pylint: disable=unused-variable
+        for _, batch in tqdm(enumerate(self.dataloader)):
-            enumerate(self.dataloader)
-        ):
             query_tensors = batch["input_ids"]
 
             # generate model response
@@ -65,3 +73,189 @@ class TRLPPOTrainer(PPOTrainer):
                 rewards,
                 columns_to_log=["query", "response", "ref_response", "ref_rewards"],
             )
+
+
+class AxolotlORPOTrainer(RngLoaderMixin, SchedulerMixin, ORPOTrainer):
+    """
+    Extend the base ORPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "orpo"]
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+        train_eval: Literal["train", "eval"] = "train",
+    ):
+        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
+
+        # TODO remove once https://github.com/huggingface/trl/pull/3069 is included in a trl release
+
+        metrics = {}
+
+        forward_output = self.concatenated_forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+            policy_nll_loss,
+        ) = forward_output[:5]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[5]
+
+        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = (
+            self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps)
+        )
+        # full ORPO loss
+        loss = policy_nll_loss - losses.mean()
+
+        reward_accuracies = (chosen_rewards > rejected_rewards).float()
+
+        prefix = "eval_" if train_eval == "eval" else ""
+        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(
+            chosen_rewards
+        ).mean()
+        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(
+            rejected_rewards
+        ).mean()
+        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(
+            reward_accuracies
+        ).mean()
+        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
+            chosen_rewards - rejected_rewards
+        ).mean()
+        metrics[f"{prefix}logps/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
+        )
+        metrics[f"{prefix}logps/chosen"] = (
+            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
+        )
+        metrics[f"{prefix}logits/rejected"] = self.accelerator.gather_for_metrics(
+            policy_rejected_logits.detach().mean()
+        ).mean()
+        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(
+            policy_chosen_logits.detach().mean()
+        ).mean()
+        metrics[f"{prefix}nll_loss"] = (
+            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
+        )
+        metrics[f"{prefix}log_odds_ratio"] = (
+            self.accelerator.gather_for_metrics(log_odds_ratio).detach().mean()
+        )
+        metrics[f"{prefix}log_odds_chosen"] = (
+            self.accelerator.gather_for_metrics(log_odds_chosen).detach().mean()
+        )
+        for k, v in metrics.items():
+            metrics[k] = v.item()
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+
+class AxolotlKTOTrainer(RngLoaderMixin, SchedulerMixin, KTOTrainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+
+class AxolotlCPOTrainer(RngLoaderMixin, SchedulerMixin, CPOTrainer):
+    """
+    Extend the base CPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "cpo"]
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+        train_eval: Literal["train", "eval"] = "train",
+    ):
+        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
+        metrics = {}
+
+        forward_output = self.concatenated_forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+            policy_nll_loss,
+        ) = forward_output[:5]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[5]
+
+        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
+            policy_chosen_logps,
+            policy_rejected_logps,
+        )
+
+        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
+        reward_accuracies = (chosen_rewards > rejected_rewards).float()
+
+        prefix = "eval_" if train_eval == "eval" else ""
+        metrics[f"{prefix}rewards/chosen"] = (
+            self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
+        )
+        metrics[f"{prefix}rewards/rejected"] = (
+            self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
+        )
+        metrics[f"{prefix}rewards/accuracies"] = (
+            self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
+        )
+        metrics[f"{prefix}rewards/margins"] = (
+            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards)
+            .mean()
+            .item()
+        )
+        metrics[f"{prefix}logps/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logps)
+            .detach()
+            .mean()
+            .item()
+        )
+        metrics[f"{prefix}logps/chosen"] = (
+            self.accelerator.gather_for_metrics(policy_chosen_logps)
+            .detach()
+            .mean()
+            .item()
+        )
+        metrics[f"{prefix}logits/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logits.detach().mean())
+            .mean()
+            .item()
+        )
+        metrics[f"{prefix}logits/chosen"] = (
+            self.accelerator.gather_for_metrics(policy_chosen_logits.detach().mean())
+            .mean()
+            .item()
+        )
+        metrics[f"{prefix}nll_loss"] = (
+            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()
+        )
+
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+
+class AxolotlRewardTrainer(RngLoaderMixin, SchedulerMixin, RewardTrainer):
+    """
+    Extend the base RewardTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "reward"]
+
+
+class AxolotlPRMTrainer(RngLoaderMixin, SchedulerMixin, PRMTrainer):
+    """
+    Extend the base trl.PRMTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "prm"]

src/axolotl/core/trainers/utils.py

@@ -0,0 +1,33 @@
+"""Utils for Axolotl trainers"""
+
+
+def sanitize_kwargs_for_tagging(tag_names, kwargs=None):
+    if isinstance(tag_names, str):
+        tag_names = [tag_names]
+
+    if kwargs is not None:
+        if "tags" not in kwargs:
+            kwargs["tags"] = tag_names
+        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
+            kwargs["tags"].extend(tag_names)
+        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
+            tag_names.append(kwargs["tags"])
+            kwargs["tags"] = tag_names
+
+    return kwargs
+
+
+def sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
+    if isinstance(dataset_tags, str):
+        dataset_tags = [dataset_tags]
+
+    if (dataset_tags is not None) and (kwargs is not None):
+        if "dataset_tags" not in kwargs:
+            kwargs["dataset_tags"] = dataset_tags
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
+            kwargs["dataset_tags"].extend(dataset_tags)
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
+            dataset_tags.append(kwargs["dataset_tags"])
+            kwargs["dataset_tags"] = dataset_tags
+
+    return kwargs

src/axolotl/core/training_args.py

@@ -5,6 +5,7 @@ extra axolotl specific training args
 from dataclasses import dataclass, field
 from typing import Optional
 
+from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
@@ -33,6 +34,12 @@ class AxolotlTrainingMixins:
         default=False,
         metadata={"help": "Use sample packing for efficient training."},
     )
+    sample_packing_sequentially: bool = field(
+        default=False,
+        metadata={
+            "help": "Use next-fit sample packing that preserves the order of samples coming from the sampler. Use in combination with curriculum_sampling for fully sequential packing."
+        },
+    )
     multipack_real_batches: bool = field(
         default=False,
         metadata={"help": "Use real batches for efficient training."},
@@ -207,14 +214,33 @@ class AxolotlTrainingMixins:
         },
     )
 
+    sequence_parallel_degree: Optional[int] = field(
+        default=1,
+        metadata={"help": "The number of workers to use in sequence parallelism"},
+    )
+
+    # multi-modal section
+
+    image_size: int | tuple[int, int] | None = field(
+        default=None,
+        metadata={"help": "The size of the image to resize to"},
+    )
+
+    image_resize_algorithm: Resampling | None = field(
+        default=None,
+        metadata={"help": "The algorithm to use for image resizing"},
+    )
+
+    # end of multi-modal section
+
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
     """
     Training arguments for Causal trainer
 
-    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a
-    so it can't be used as a mixin.
+    default value so it can't be used as a mixin.
     """
 
 

src/axolotl/evaluate.py

@@ -8,11 +8,14 @@ from typing import Dict, Optional
 
 import torch
 from accelerate.logging import get_logger
+from datasets import Dataset
+from transformers.trainer import Trainer
 
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
@@ -25,18 +28,18 @@ LOG = get_logger("axolotl.evaluate")
 
 
 def evaluate_dataset(
-    trainer, dataset, dataset_type: str, flash_optimum: bool = False
+    trainer: Trainer, dataset: Dataset, dataset_type: str, flash_optimum: bool = False
 ) -> Optional[Dict[str, float]]:
-    """Helper function to evaluate a single dataset safely.
+    """Helper function to evaluate a single dataset.
 
     Args:
-        trainer: The trainer instance
+        trainer: The trainer instance.
-        dataset: Dataset to evaluate
+        dataset: Dataset to evaluate.
-        dataset_type: Type of dataset ('train' or 'eval')
+        dataset_type: Type of dataset ('train' or 'eval').
-        flash_optimum: Whether to use flash optimum
+        flash_optimum: Whether to use flash optimum.
 
     Returns:
-        Dictionary of metrics or None if dataset is None
+        Dictionary of metrics or None if dataset is None.
     """
     if dataset is None:
         return None
@@ -63,17 +66,14 @@ def evaluate_dataset(
 
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
-    Evaluate a model on training and validation datasets
+    Evaluate a model on training and validation datasets.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
         dataset_meta: Dataset metadata containing training and evaluation datasets.
 
     Returns:
-        Tuple containing:
+        Dictionary mapping metric names to their values.
-        - The model (either PeftModel or PreTrainedModel)
-        - The tokenizer
-        - Dictionary of evaluation metrics
     """
     # pylint: disable=duplicate-code
     # Enable expandable segments for cuda allocation to improve VRAM usage
@@ -160,4 +160,6 @@ def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, f
     del model
     del tokenizer
 
+    cleanup_distributed()
+
     return all_metrics

src/axolotl/integrations/config.py

@@ -11,19 +11,17 @@
 # the License.
 
 """
-module to handle merging the plugins' input arguments with the base configurations.
+Module to handle merging the plugins' input arguments with the base configurations.
 
-this was moved here to prevent circular imports
+This was moved here to prevent circular imports.
 """
 
 from typing import Any, Dict, List
 
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
-    AxolotlInputConfig as AxolotlInputConfigBase,
-)
 
 
 def merge_input_args():

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -1,6 +1,6 @@
 # Cut Cross Entropy
 
-Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
+Cut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
 
 See https://github.com/apple/ml-cross-entropy
 
@@ -29,6 +29,20 @@ plugins:
 cut_cross_entropy: true
 ```
 
+## Supported Models
+
+- llama
+- phi3
+- gemma
+- gemma2
+- gemma3
+- gemma3_text
+- mistral
+- mistral3
+- qwen2
+- cohere
+- cohere2
+
 ## Citation
 
 ```bib

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -25,8 +25,8 @@ import torch
 
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils import get_pytorch_version
+from axolotl.utils.distributed import zero_only
 
-from ...utils.distributed import zero_only
 from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
 
 LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
@@ -72,7 +72,9 @@ class CutCrossEntropyPlugin(BasePlugin):
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from cut_cross_entropy.transformers import cce_patch
+            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
+                cce_patch,
+            )
 
             with zero_only():
                 LOG.info(

src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py

@@ -0,0 +1,201 @@
+"""Cohere and Cohere2 CCE patch."""
+
+# This patch is based off transformers 4.50.0.
+# It patches the forward function for CohereForCausalLM and Cohere2ForCausalLM.
+# It scales the hidden states by the logit scale in advance instead of the logits as the
+# operation is done internally and should be mathematically equivalent.
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.cohere.modeling_cohere import (
+    _CONFIG_FOR_DOC,
+    COHERE_INPUTS_DOCSTRING,
+    KwargsForCausalLM,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs: Unpack[KwargsForCausalLM],
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >> from transformers import AutoTokenizer, CohereForCausalLM
+
+    >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
+    >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
+
+    >> prompt = "Hey, are you conscious? Can you talk to me?"
+    >> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >> # Generate
+    >> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        # scale weight by logit_scale in-place of logits
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight * self.logit_scale,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        logits = logits * self.logit_scale  # main diff from Llama
+
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def patch_cohere(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.cohere import modeling_cohere
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_cohere.CohereForCausalLM
+        ), f"Expected a CohereForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_cohere.CohereForCausalLM.forward = cce_forward
+    return None
+
+
+def patch_cohere2(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.cohere2 import modeling_cohere2
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_cohere2.Cohere2ForCausalLM
+        ), f"Expected a Cohere2ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_cohere2.Cohere2ForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py

@@ -0,0 +1,175 @@
+"""Gemma CCE patch"""
+
+# This patch is based off transformers 4.50.0.
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.gemma.modeling_gemma import (
+    _CONFIG_FOR_DOC,
+    GEMMA_INPUTS_DOCSTRING,
+    KwargsForCausalLM,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs: Unpack[KwargsForCausalLM],
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, GemmaForCausalLM
+
+    >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+
+    >>> prompt = "What is your favorite condiment?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "What is your favorite condiment?"
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def patch_gemma(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.gemma import modeling_gemma
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_gemma.GemmaForCausalLM
+        ), f"Expected a GemmaForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_gemma.GemmaForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py

@@ -0,0 +1,459 @@
+"""Gemma2 and Gemma3 (text and multimodal) CCE patch."""
+
+# Implementation originally adapted from https://github.com/apple/ml-cross-entropy/pull/29
+# and updated for transformers 4.50.0.
+# This is a modified version of the patch that allows for deferred logits calculation for gemma3 and works
+# with both gemma3 (text and multimodal) models.
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+)
+from torch import nn
+from transformers.cache_utils import Cache, HybridCache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.gemma3.modeling_gemma3 import (
+    _CONFIG_FOR_DOC,
+    GEMMA3_INPUTS_DOCSTRING,
+    Gemma3CausalLMOutputWithPast,
+    logger,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    is_torchdynamo_compiling,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+from axolotl.integrations.cut_cross_entropy.monkeypatch.utils import apply_lce
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[HybridCache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    defer_logits_calculation: bool = False,
+    **loss_kwargs,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        defer_logits_calculation (`bool`, *optional*):
+            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
+            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Gemma3ForCausalLM
+
+    >>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
+
+    >>> prompt = "What is your favorite condiment?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "What is your favorite condiment?"
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **loss_kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            softcap=getattr(self.config, "final_logit_softcapping", None),
+            **loss_kwargs,
+        )
+    elif _PATCH_OPTS is not None and defer_logits_calculation:
+        # defer logits calculation to the ConditionalGeneration forward
+        logits = hidden_states[:, slice_indices, :]
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if self.config.final_logit_softcapping is not None:
+            logits = logits / self.config.final_logit_softcapping
+            logits = torch.tanh(logits)
+            logits = logits * self.config.final_logit_softcapping
+
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward_multimodal(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    pixel_values: torch.FloatTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+    token_type_ids: Optional[torch.LongTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **lm_kwargs,
+) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+
+    >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
+    >>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
+
+    >>> prompt = "answer en Where is the cow standing?"
+    >>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(**inputs, max_length=30)
+    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "answer en Where is the cow standing?\nbeach"
+    ```"""
+
+    if (input_ids is None) ^ (inputs_embeds is not None):
+        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    is_training = token_type_ids is not None and labels is not None
+
+    # Replace image id woth PAD if the image token if OOV, to avoid index-errors
+    if input_ids is not None and self.config.image_token_index >= self.vocab_size:
+        special_image_mask = input_ids == self.config.image_token_index
+        llm_input_ids = input_ids.clone()
+        llm_input_ids[special_image_mask] = 0
+    else:
+        llm_input_ids = input_ids  # type: ignore
+
+    if inputs_embeds is None:
+        inputs_embeds = self.get_input_embeddings()(llm_input_ids)
+
+    if cache_position is None:
+        past_seen_tokens = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0  # type: ignore
+        )
+        cache_position = torch.arange(  # type: ignore
+            past_seen_tokens,
+            past_seen_tokens + inputs_embeds.shape[1],
+            device=inputs_embeds.device,
+        )
+
+    # Merge text and images
+    if pixel_values is not None:
+        image_features = self.get_image_features(pixel_values)
+
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(
+                    self.config.image_token_index,
+                    dtype=torch.long,
+                    device=inputs_embeds.device,
+                )
+            )
+        else:
+            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
+                -1
+            )
+            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
+                inputs_embeds.device
+            )
+
+        if (
+            not is_torchdynamo_compiling()
+            and inputs_embeds[special_image_mask].numel() != image_features.numel()
+        ):
+            image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
+            raise ValueError(
+                f"Number of images does not match number of special image tokens in the input text. "
+                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
+                "tokens from image embeddings."
+            )
+        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
+
+    # mask out pad-token-ids in labels for BC
+    if labels is not None and self.pad_token_id in labels:
+        logger.warning_once(
+            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
+            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
+        )
+        labels = torch.where(  # type: ignore
+            input_ids == self.pad_token_id, self.config.ignore_index, labels
+        )
+
+    causal_mask = self._update_causal_mask(  # pylint: disable=protected-access
+        attention_mask,
+        token_type_ids,
+        past_key_values,
+        cache_position,
+        inputs_embeds,
+        is_training,
+    )
+    outputs = self.language_model(
+        attention_mask=causal_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        defer_logits_calculation=True,  # enable deferred logits calculation
+        **lm_kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states,
+            self.language_model.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            softcap=getattr(self.config, "final_logit_softcapping", None),
+            **lm_kwargs,
+        )
+    else:
+        logits = hidden_states
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            shift_logits = logits[..., :-1, :]
+            shift_labels = labels[..., 1:]
+            if attention_mask is not None:
+                # we use the input attention mask to shift the logits and labels, because it is 2D.
+                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(
+                    logits.device
+                )
+                shift_logits = shift_logits[
+                    shift_attention_mask.to(logits.device) != 0
+                ].contiguous()
+                shift_labels = shift_labels[
+                    shift_attention_mask.to(shift_labels.device) != 0
+                ].contiguous()
+            else:
+                shift_logits = shift_logits.contiguous()
+                shift_labels = shift_labels.contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+
+            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
+            flat_labels = shift_labels.view(-1).to(shift_logits.device)
+            loss = loss_fct(flat_logits, flat_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Gemma3CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=image_features if pixel_values is not None else None,
+    )
+
+
+def patch_gemma2(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.gemma2 import modeling_gemma2
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_gemma2.Gemma2ForCausalLM
+        ), f"Expected a Gemma2ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_gemma2.Gemma2ForCausalLM.forward = cce_forward
+    return None
+
+
+def patch_gemma3_text(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.gemma3 import modeling_gemma3
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_gemma3.Gemma3ForCausalLM
+        ), f"Expected a Gemma3ForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
+    return None
+
+
+def patch_gemma3(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.gemma3 import modeling_gemma3
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_gemma3.Gemma3ForConditionalGeneration
+        ), f"Expected a Gemma3ForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        # patch the causal model to enable deferred logits calculation
+        maybe_model.language_model.forward = MethodType(
+            cce_forward, maybe_model.language_model
+        )
+        return maybe_model
+
+    modeling_gemma3.Gemma3ForConditionalGeneration.forward = cce_forward_multimodal
+    # patch the causal model to enable deferred logits calculation
+    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py

@@ -0,0 +1,392 @@
+"""Mistral and Mistral3 CCE patch."""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from torch import nn
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.mistral3.modeling_mistral3 import (
+    Mistral3CausalLMOutputWithPast,
+)
+from transformers.models.mistral.modeling_mistral import (
+    _CONFIG_FOR_DOC,
+    MISTRAL_INPUTS_DOCSTRING,
+    KwargsForCausalLM,
+)
+from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    is_torchdynamo_compiling,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] | None = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    defer_logits_calculation: bool = False,
+    **kwargs: Unpack[KwargsForCausalLM],
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        defer_logits_calculation (`bool`, *optional*):
+            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
+            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+    >>> model = MistralForCausalLM.from_pretrained("meta-mistral/Mistral-2-7b-hf")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-mistral/Mistral-2-7b-hf")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **kwargs,
+        )
+    elif _PATCH_OPTS is not None and defer_logits_calculation:
+        # defer logits calculation to the ConditionalGeneration forward
+        logits = hidden_states[:, slice_indices, :]
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def cce_forward_multimodal(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    pixel_values: torch.FloatTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    vision_feature_layer: Optional[Union[int, list[int]]] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    image_sizes: torch.Tensor | None = None,
+    **lm_kwargs,
+) -> Union[Tuple, Mistral3CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+
+    >>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+    >>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+
+    >>> prompt = "<s>[INST][IMG]What is the image?[/INST]"
+    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "What is the image?The image depicts two cats lying on a pink blanket."
+    ```"""
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+    vision_feature_layer = (
+        vision_feature_layer
+        if vision_feature_layer is not None
+        else self.config.vision_feature_layer
+    )
+
+    if (input_ids is None) ^ (inputs_embeds is not None):
+        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+    if pixel_values is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+        )
+
+    if inputs_embeds is None:
+        inputs_embeds = self.get_input_embeddings()(input_ids)
+
+    if pixel_values is not None:
+        image_features = self.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            image_sizes=image_sizes,
+        )
+
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
+        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
+            inputs_embeds.device
+        )
+        if (
+            not is_torchdynamo_compiling()
+            and inputs_embeds[special_image_mask].numel() != image_features.numel()
+        ):
+            n_image_tokens = (input_ids == self.config.image_token_index).sum()
+            n_image_features = image_features.shape[0] * image_features.shape[1]
+            raise ValueError(
+                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+            )
+        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
+
+    outputs = self.language_model(
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        defer_logits_calculation=True,  # enable deferred logits calculation
+        **lm_kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states,
+            self.language_model.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **lm_kwargs,
+        )
+    else:
+        logits = hidden_states
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                # we use the input attention mask to shift the logits and labels, because it is 2D.
+                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(
+                    logits.device
+                )
+                shift_logits = logits[..., :-1, :][
+                    shift_attention_mask.to(logits.device) != 0
+                ].contiguous()
+                shift_labels = labels[..., 1:][
+                    shift_attention_mask.to(labels.device) != 0
+                ].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1).to(shift_logits.device),
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Mistral3CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=image_features if pixel_values is not None else None,
+    )
+
+
+def patch_mistral(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.mistral import modeling_mistral
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_mistral.MistralForCausalLM
+        ), f"Expected a MistralForCausalLM model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward, maybe_model)
+        return maybe_model
+
+    modeling_mistral.MistralForCausalLM.forward = cce_forward
+    return None
+
+
+def patch_mistral3(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.mistral import modeling_mistral
+    from transformers.models.mistral3 import modeling_mistral3
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_mistral3.Mistral3ForConditionalGeneration
+        ), f"Expected a Mistral3ForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        # patch the causal model to enable deferred logits calculation
+        maybe_model.language_model.forward = MethodType(
+            cce_forward, maybe_model.language_model
+        )
+        return maybe_model
+
+    modeling_mistral3.Mistral3ForConditionalGeneration.forward = cce_forward_multimodal
+    # patch the causal model to enable deferred logits calculation
+    modeling_mistral.MistralForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mllama.py

@@ -0,0 +1,379 @@
+"""Mllama CCE patch."""
+
+# pylint: disable=duplicate-code
+
+from types import MethodType
+from typing import Optional, Tuple, Union
+
+import torch
+import transformers
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    TransformersModelT,
+    apply_lce,
+)
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.mllama.modeling_mllama import (
+    MLLAMA_INPUTS_DOCSTRING,
+    _prepare_cross_attention_mask,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+_PATCH_OPTS: PatchOptions | None = None
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig"
+)
+def cce_forward(
+    self,
+    input_ids: torch.LongTensor | None = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    cross_attention_states: Optional[torch.LongTensor] = None,
+    cross_attention_mask: Optional[torch.LongTensor] = None,
+    full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    defer_logits_calculation: bool = False,
+    **loss_kwargs,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        defer_logits_calculation (`bool`, *optional*):
+            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
+            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, MllamaForCausalLM
+
+    >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision")
+
+    >>> prompt = "If I had to write a haiku, it would be:"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6)
+    >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    >>> print(result)
+    If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful.
+    I love the idea of snowflakes gently falling, each one
+    ```
+    """
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        cross_attention_states=cross_attention_states,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        cross_attention_mask=cross_attention_mask,
+        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    slice_indices = (
+        slice(-logits_to_keep, None)
+        if isinstance(logits_to_keep, int)
+        else logits_to_keep
+    )
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states[:, slice_indices, :],
+            self.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **loss_kwargs,
+        )
+    elif _PATCH_OPTS is not None and defer_logits_calculation:
+        # defer logits calculation to the ConditionalGeneration forward
+        logits = hidden_states[:, slice_indices, :]
+    else:
+        logits = self.lm_head(hidden_states[:, slice_indices, :]).float()
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class="MllamaConfig"
+)
+def cce_forward_multimodal(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    pixel_values: Optional[torch.FloatTensor] = None,
+    aspect_ratio_mask: Optional[torch.Tensor] = None,
+    aspect_ratio_ids: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    cross_attention_mask: Optional[torch.Tensor] = None,
+    cross_attention_states: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[list[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **loss_kwargs,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, MllamaForConditionalGeneration
+
+    >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
+    >>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
+    >>> processor = AutoProcessor.from_pretrained(checkpoint)
+
+    >>> prompt = "<|image|>If I had to write a haiku for this one"
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+
+    >>> # Generate
+    >>> output = model.generate(**inputs, max_new_tokens=15)
+
+    >>> prompt_len = inputs.input_ids.shape[-1]
+    >>> generated_ids = output[:, prompt_len:]
+    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    >>> print(generated_text)
+    [', it would be:.\\nA stop sign in Chinatown.\\n']
+    ```
+    """
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    if (input_ids is None) ^ (inputs_embeds is not None):
+        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+    if pixel_values is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+        )
+
+    if pixel_values is not None and cross_attention_states is not None:
+        raise ValueError(
+            "`pixel_values` and `cross_attention_states` cannot be provided simultaneously"
+        )
+
+    if pixel_values is not None:
+        if aspect_ratio_ids is None:
+            raise ValueError(
+                "`aspect_ratio_ids` must be provided if `pixel_values` is provided"
+            )
+        # get vision tokens from vision model
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            aspect_ratio_ids=aspect_ratio_ids,
+            aspect_ratio_mask=aspect_ratio_mask,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=return_dict,
+        )
+        cross_attention_states = vision_outputs[0]
+        cross_attention_states = self.multi_modal_projector(
+            cross_attention_states
+        ).reshape(
+            -1, cross_attention_states.shape[-2], self.hidden_size  # type: ignore
+        )
+
+    if cross_attention_mask is not None:
+        cross_attention_mask, full_text_row_masked_out_mask = (
+            _prepare_cross_attention_mask(
+                cross_attention_mask,
+                num_vision_tokens=self.vision_model.num_patches,
+                dtype=self.dtype,
+            )
+        )
+    else:
+        full_text_row_masked_out_mask = None
+
+    if cross_attention_mask is not None and cache_position is not None:
+        cross_attention_mask = cross_attention_mask[:, :, cache_position]
+        full_text_row_masked_out_mask = full_text_row_masked_out_mask[
+            :, :, cache_position
+        ]
+
+    outputs = self.language_model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        cross_attention_states=cross_attention_states,
+        cross_attention_mask=cross_attention_mask,
+        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+        past_key_values=past_key_values,
+        use_cache=use_cache,
+        inputs_embeds=inputs_embeds,
+        output_hidden_states=output_hidden_states,
+        output_attentions=output_attentions,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        defer_logits_calculation=True,  # enable deferred logits calculation
+        **loss_kwargs,
+    )
+
+    hidden_states = outputs[0]
+    loss = None
+    logits = None
+
+    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+        assert labels is not None
+        loss = apply_lce(
+            hidden_states,
+            self.language_model.lm_head.weight,
+            labels,
+            _PATCH_OPTS,
+            **loss_kwargs,
+        )
+    else:
+        # Temporary fix to calculate the loss in main class, as the model's vocab size may be resized
+        logits = hidden_states
+
+        if labels is not None:
+            loss = self.loss_function(
+                logits, labels, self.config.get_text_config().vocab_size, **loss_kwargs
+            )
+
+    if not return_dict:
+        return (loss,) + outputs if loss is not None else outputs
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=outputs.logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def patch_mllama(
+    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
+    patch_options: PatchOptions,
+) -> TransformersModelT | None:
+
+    global _PATCH_OPTS  # pylint: disable=global-statement
+    from transformers.models.mllama import modeling_mllama
+
+    _PATCH_OPTS = patch_options
+
+    if isinstance(maybe_model, transformers.PreTrainedModel):
+        assert isinstance(
+            maybe_model, modeling_mllama.MllamaForConditionalGeneration
+        ), f"Expected a MllamaForConditionalGeneration model. Got {type(maybe_model)}."
+        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
+
+        # patch the language model
+        maybe_model.language_model.forward = MethodType(
+            cce_forward, maybe_model.language_model
+        )
+        return maybe_model
+
+    modeling_mllama.MllamaForConditionalGeneration.forward = cce_forward_multimodal
+
+    # patch the causal language model
+    modeling_mllama.MllamaForCausalLM.forward = cce_forward
+    return None

src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py

@@ -0,0 +1,85 @@
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+"""Cut Cross Entropy patcher"""
+
+import transformers
+from cut_cross_entropy.cce_utils import LinearCrossEntropyImpl
+from cut_cross_entropy.linear_cross_entropy import LCE_IMPL_DEFAULT
+from cut_cross_entropy.transformers.llama import patch_llama
+from cut_cross_entropy.transformers.phi3 import patch_phi3
+from cut_cross_entropy.transformers.qwen2 import patch_qwen2
+from cut_cross_entropy.transformers.utils import PatchOptions, TransformersModelT
+
+from axolotl.integrations.cut_cross_entropy.monkeypatch.cohere import (
+    patch_cohere,
+    patch_cohere2,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma import patch_gemma
+from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma3 import (
+    patch_gemma2,
+    patch_gemma3,
+    patch_gemma3_text,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.mistral3 import (
+    patch_mistral,
+    patch_mistral3,
+)
+from axolotl.integrations.cut_cross_entropy.monkeypatch.mllama import patch_mllama
+
+CUT_CROSS_ENTROPY_MODEL_MAPPING = {
+    "llama": patch_llama,
+    "mllama": patch_mllama,
+    "phi3": patch_phi3,
+    "gemma": patch_gemma,
+    "gemma2": patch_gemma2,
+    "gemma3": patch_gemma3,
+    "gemma3_text": patch_gemma3_text,
+    "mistral": patch_mistral,
+    "mistral3": patch_mistral3,
+    "qwen2": patch_qwen2,
+    "cohere": patch_cohere,
+    "cohere2": patch_cohere2,
+}
+
+
+def cce_patch(
+    model_type_or_model: str | TransformersModelT | transformers.PretrainedConfig,
+    impl: str | LinearCrossEntropyImpl = LCE_IMPL_DEFAULT,
+    reduction: str = "mean",
+    filter_eps: float | str | None = "auto",
+    accum_e_fp32: bool = False,
+    accum_c_fp32: bool = False,
+    filter_e_grad: bool = True,
+    filter_c_grad: bool = True,
+    train_only: bool = False,
+) -> TransformersModelT | None:
+    if isinstance(impl, LinearCrossEntropyImpl):
+        impl = impl.name.lower()
+
+    if impl not in (v.name.lower() for v in LinearCrossEntropyImpl):
+        raise ValueError(f"Unknown {impl=}")
+
+    if isinstance(model_type_or_model, transformers.PreTrainedModel):
+        model_type = model_type_or_model.config.model_type
+    elif isinstance(model_type_or_model, transformers.PretrainedConfig):
+        model_type = model_type_or_model.model_type
+    else:
+        model_type = model_type_or_model
+
+    patch_options = PatchOptions(
+        impl=impl,
+        reduction=reduction,
+        filter_eps=filter_eps,
+        accum_e_fp32=accum_e_fp32,
+        accum_c_fp32=accum_c_fp32,
+        filter_e_grad=filter_e_grad,
+        filter_c_grad=filter_c_grad,
+        train_only=train_only,
+    )
+
+    if model_type in CUT_CROSS_ENTROPY_MODEL_MAPPING:
+        return CUT_CROSS_ENTROPY_MODEL_MAPPING[model_type](
+            model_type_or_model, patch_options
+        )
+
+    raise RuntimeError(f"Unknown model type {model_type}")

src/axolotl/integrations/cut_cross_entropy/monkeypatch/utils.py

@@ -0,0 +1,40 @@
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+"""Monkeypatch for apply_lce to add softcap."""
+
+import torch
+from cut_cross_entropy import linear_cross_entropy
+from cut_cross_entropy.transformers.utils import PatchOptions
+
+
+def apply_lce(
+    e: torch.Tensor,
+    c: torch.Tensor,
+    labels: torch.Tensor,
+    opts: PatchOptions,
+    bias: torch.Tensor | None = None,
+    softcap: float | None = None,
+    **loss_kwargs,
+) -> torch.Tensor:
+    """Monkey patch for apply_lce to support softcap kwarg."""
+    num_items_in_batch = loss_kwargs.get("num_items_in_batch", None)
+    cce_kwargs = opts.to_kwargs()
+    if num_items_in_batch is not None and cce_kwargs["reduction"] == "mean":
+        cce_kwargs["reduction"] = "sum"
+    else:
+        num_items_in_batch = None
+
+    loss = linear_cross_entropy(
+        e,
+        c,
+        labels.to(e.device),
+        bias=bias,
+        shift=True,
+        softcap=softcap,
+        **cce_kwargs,
+    )
+
+    if num_items_in_batch is not None:
+        loss = loss / num_items_in_batch
+
+    return loss

src/axolotl/integrations/liger/README.md

@@ -20,6 +20,26 @@ liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 ```
 
+## Supported Models
+
+- deepseek_v2
+- gemma
+- gemma2
+- gemma3 (partial support, no support for FLCE yet)
+- granite
+- jamba
+- llama
+- mistral
+- mixtral
+- mllama
+- mllama_text_model
+- olmo2
+- paligemma
+- phi3
+- qwen2
+- qwen2_5_vl
+- qwen2_vl
+
 ## Citation
 
 ```bib

src/axolotl/integrations/liger/__init__.py

@@ -21,6 +21,7 @@ It is designed to be performant, correct, and light-weight.
 import inspect
 import logging
 import sys
+from functools import partial
 
 from axolotl.integrations.base import BasePlugin
 
@@ -41,11 +42,18 @@ class LigerPlugin(BasePlugin):
     def pre_model_load(self, cfg):
         from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
         from liger_kernel.transformers.functional import liger_cross_entropy
+        from liger_kernel.transformers.geglu import LigerGEGLUMLP
+        from liger_kernel.transformers.layer_norm import LigerLayerNorm
         from liger_kernel.transformers.monkey_patch import MODEL_TYPE_TO_APPLY_LIGER_FN
         from liger_kernel.transformers.rms_norm import LigerRMSNorm
         from liger_kernel.transformers.rope import liger_rotary_pos_emb
         from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
+        if cfg.liger_cross_entropy and cfg.liger_fused_linear_cross_entropy:
+            raise ValueError(
+                "Cannot have both `liger_cross_entropy` and `liger_fused_linear_cross_entropy` set."
+            )
+
         if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
             apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
             liger_fn_sig = inspect.signature(apply_liger_fn)
@@ -82,6 +90,8 @@ class LigerPlugin(BasePlugin):
                 modeling_jamba.JambaRMSNorm = LigerRMSNorm
             if cfg.liger_glu_activation:
                 modeling_jamba.JambaMLP = LigerSwiGLUMLP
+            if cfg.liger_layer_norm:
+                modeling_jamba.nn.LayerNorm = LigerLayerNorm
             if cfg.liger_cross_entropy:
                 from transformers.loss.loss_utils import nn
 
@@ -104,13 +114,51 @@ class LigerPlugin(BasePlugin):
                 # The DeepseekV2 version of RoPE is different than upstream LLaMA.
                 # See https://github.com/linkedin/Liger-Kernel/issues/129#issuecomment-2313763528
                 logging.warning("Fused liger_rope is not supported for DeepseekV2.")
+            if cfg.liger_glu_activation:
+                logging.warning("liger_glu_activation is not supported for DeepseekV2.")
             if cfg.liger_rms_norm:
                 modeling_mod.DeepseekV2RMSNorm = LigerRMSNorm
             if cfg.liger_glu_activation:
                 modeling_mod.DeepseekV2MLP.forward = LigerSwiGLUMLP.forward
+            if cfg.liger_layer_norm:
+                modeling_mod.DeepseekV2MLP.forward = LigerLayerNorm.forward
             if cfg.liger_cross_entropy:
                 # We do not patch `nn.functional.cross_entropy` for DeepseekV2 as it still uses
                 # nn.CrossEntropyLoss in the forward method.
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+        elif cfg.model_config_type in ["gemma3", "gemma3_text"]:
+            from transformers.models.gemma3 import modeling_gemma3
+
+            if cfg.liger_rope:
+                modeling_gemma3.apply_rotary_pos_emb = liger_rotary_pos_emb
+            if cfg.liger_rms_norm:
+
+                def _liger_rms_norm_wrapper(dim, **kwargs):
+                    "Convert 'dim' keyword to 'hidden_size' to pass to LigerRMSNorm"
+                    return LigerRMSNorm(hidden_size=dim, **kwargs)
+
+                modeling_gemma3.Gemma3RMSNorm = partial(
+                    _liger_rms_norm_wrapper,
+                    offset=1.0,
+                    casting_mode="gemma",
+                    init_fn="zeros",
+                    in_place=False,
+                )
+            if cfg.liger_glu_activation:
+                modeling_gemma3.Gemma3MLP = LigerGEGLUMLP
+            if cfg.liger_layer_norm:
+                modeling_gemma3.nn.LayerNorm = LigerLayerNorm
+
+            if cfg.liger_cross_entropy:
+                from transformers.loss.loss_utils import nn
+
+                nn.functional.cross_entropy = liger_cross_entropy
+
+            if cfg.liger_fused_linear_cross_entropy:
+                raise NotImplementedError(
+                    "Fused linear cross entropy is not yet supported for Gemma3."
+                )
+        elif cfg.model_config_type in ["deepseek_v3"]:
+            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")

src/axolotl/monkeypatch/attention/ring_attn.py

@@ -0,0 +1,100 @@
+"""
+Ring attention group registration and flash attention patching.
+
+Make use of the `ring-flash-attn` (https://github.com/zhuzilin/ring-flash-attention)
+package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patch in
+their sequence parallel version of Flash Attention 2.
+"""
+
+import torch.distributed as dist
+from accelerate.logging import get_logger
+
+from axolotl.logging_config import configure_logging
+
+configure_logging()
+LOG = get_logger(__name__)
+
+RING_ATTN_GROUP = None
+
+
+def get_ring_attn_group() -> dist.ProcessGroup:
+    """
+    Getter for ring attention group on this rank.
+
+    Returns:
+        The process group for ring attention for this rank.
+    """
+    return RING_ATTN_GROUP
+
+
+def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
+    """
+    Setter for ring attention group on this rank.
+
+    Args:
+        Process group for ring attention.
+    """
+    global RING_ATTN_GROUP  # pylint: disable=global-statement
+    RING_ATTN_GROUP = ring_attn_group
+
+
+def register_ring_attn(sequence_parallel_degree: int, heads_k_stride: int | None):
+    """
+    Create ring attention group and substitute flash attn with ring flash attn.
+
+    Args:
+        sequence_parallel_degree: Sequence parallelism factor.
+        heads_k_stride: Sequence parallelism K head stride size. Passed
+            through to `ring_flash_attn.substitute_hf_flash_attn`.
+    """
+    if get_ring_attn_group() is not None:
+        LOG.info("Ring attention already registered, exiting early...")
+        return
+
+    LOG.info(
+        "Enabling ring attention sequence parallelism: "
+        f"each sequence will be processed across {sequence_parallel_degree} GPUs"
+    )
+
+    world_size = dist.get_world_size()
+    assert sequence_parallel_degree <= world_size, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must be less than or equal to world_size ({world_size})"
+    )
+    assert world_size % sequence_parallel_degree == 0, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must evenly divide world_size ({world_size})"
+    )
+
+    # Detailed logging of group formation
+    rank = dist.get_rank()
+    group_assignments = {}
+
+    for i in range(world_size // sequence_parallel_degree):
+        ring_attn_ranks = list(
+            range(
+                i * sequence_parallel_degree,
+                (i + 1) * sequence_parallel_degree,
+            )
+        )
+        group = dist.new_group(ranks=ring_attn_ranks, backend="nccl")
+
+        # Track which GPUs are in which groups
+        for r in ring_attn_ranks:
+            group_assignments[r] = i
+
+        if rank in ring_attn_ranks:
+            set_ring_attn_group(group)
+
+    # Log the GPU group assignments
+    if rank == 0:
+        LOG.info(f"Sequence parallel group assignments: {group_assignments}")
+
+    if heads_k_stride is None:
+        heads_k_stride = 1
+
+    from ring_flash_attn import substitute_hf_flash_attn
+
+    substitute_hf_flash_attn(
+        process_group=get_ring_attn_group(), heads_k_stride=heads_k_stride
+    )

src/axolotl/monkeypatch/gemma3.py

@@ -0,0 +1,238 @@
+"""Monkeypatch for gemma3 conditional generation forward to fix loss exploding"""
+
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple, Union
+
+import torch
+from transformers.cache_utils import Cache
+from transformers.models.gemma3.modeling_gemma3 import (
+    _CONFIG_FOR_DOC,
+    GEMMA3_INPUTS_DOCSTRING,
+    Gemma3CausalLMOutputWithPast,
+    logger,
+)
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    is_torchdynamo_compiling,
+    replace_return_docstrings,
+)
+from transformers.utils.deprecation import deprecate_kwarg
+
+
+@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def new_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    pixel_values: torch.FloatTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+    token_type_ids: Optional[torch.LongTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **lm_kwargs,
+) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
+    r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+
+    >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
+    >>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
+
+    >>> prompt = "answer en Where is the cow standing?"
+    >>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(**inputs, max_length=30)
+    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "answer en Where is the cow standing?\nbeach"
+    ```"""
+
+    if (input_ids is None) ^ (inputs_embeds is not None):
+        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    is_training = token_type_ids is not None and labels is not None
+
+    # Replace image id with PAD if the image token is OOV, to avoid index-errors
+    if input_ids is not None and self.config.image_token_index >= self.vocab_size:
+        special_image_mask = input_ids == self.config.image_token_index
+        llm_input_ids = input_ids.clone()
+        llm_input_ids[special_image_mask] = 0
+    else:
+        llm_input_ids = input_ids
+
+    if inputs_embeds is None:
+        inputs_embeds = self.get_input_embeddings()(llm_input_ids)
+
+    if cache_position is None:
+        past_seen_tokens = (
+            past_key_values.get_seq_length() if past_key_values is not None else 0
+        )
+        cache_position = torch.arange(
+            past_seen_tokens,
+            past_seen_tokens + inputs_embeds.shape[1],
+            device=inputs_embeds.device,
+        )
+
+    # Merge text and images
+    if pixel_values is not None:
+        image_features = self.get_image_features(pixel_values)
+
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(
+                    self.config.image_token_index,
+                    dtype=torch.long,
+                    device=inputs_embeds.device,
+                )
+            )
+        else:
+            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
+                -1
+            )
+            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
+                inputs_embeds.device
+            )
+
+        if (
+            not is_torchdynamo_compiling()
+            and inputs_embeds[special_image_mask].numel() != image_features.numel()
+        ):
+            image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
+            raise ValueError(
+                f"Number of images does not match number of special image tokens in the input text. "
+                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
+                "tokens from image embeddings."
+            )
+        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+    # mask out pad-token-ids in labels for BC
+    if labels is not None and self.pad_token_id in labels:
+        logger.warning_once(
+            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
+            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
+        )
+        labels = torch.where(
+            input_ids == self.pad_token_id, self.config.ignore_index, labels
+        )
+
+    causal_mask = self._update_causal_mask(  # pylint: disable=protected-access
+        attention_mask,
+        token_type_ids,
+        past_key_values,
+        cache_position,
+        inputs_embeds,
+        is_training,
+    )
+    outputs = self.language_model(
+        attention_mask=causal_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        **lm_kwargs,
+    )
+
+    logits = outputs[0]
+    loss = None
+    if labels is not None:
+        if attention_mask is not None:
+            # Get the shifted attention mask
+            shift_attention_mask = attention_mask[:, -logits.shape[1] + 1 :].to(
+                logits.device
+            )  # +1 for shift
+
+            # Filter logits and labels based on attention mask
+            valid_indices = shift_attention_mask != 0
+            filtered_logits = logits[..., :-1, :][valid_indices]
+            filtered_labels = labels[..., 1:][valid_indices.to(labels.device)]
+
+            # TODO: do we need to handle num_items_in_batch given we filter the logits and labels?
+
+            loss = self.loss_function(
+                logits=filtered_logits,
+                labels=None,  # we pass shift_labels
+                shift_labels=filtered_labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **lm_kwargs,
+            )
+        else:
+            # Standard case without filtering
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **lm_kwargs,
+            )
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return Gemma3CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=image_features if pixel_values is not None else None,
+    )
+
+
+def patch_gemma3conditionalgeneration_forward():
+    from transformers.models.gemma3.modeling_gemma3 import (
+        Gemma3ForConditionalGeneration,
+    )
+
+    Gemma3ForConditionalGeneration.forward = new_forward

src/axolotl/monkeypatch/lora_kernels.py

@@ -252,12 +252,38 @@ def apply_lora_kernel_patches(
     LOG.setLevel(logging.INFO)
 
     # Choose activation based on model type
-    activation = model.config.hidden_act
+    activation = None
+    text_config = (
+        model.config.get_text_config()
+        if hasattr(model.config, "get_text_config")
+        else model.config
+    )
+    if hasattr(text_config, "hidden_act"):
+        activation = text_config.hidden_act
+    elif hasattr(text_config, "hidden_activation"):
+        activation = text_config.hidden_activation
+
+    # map activation to supported activation
+    if "gelu" in activation:
+        # gemma3 uses gelu_pytorch_tanh
+        activation = "gelu"
+
     if activation not in SUPPORTED_ACTIVATIONS:
         raise NotImplementedError(f"Activation {activation} is not supported")
 
+    layers = []
+    # check for multimodal models first
+    if hasattr(model, "language_model"):
+        layers = model.language_model.model.layers
+    elif hasattr(model, "model"):
+        layers = model.model.model.layers
+    else:
+        raise NotImplementedError(
+            f"Model type {model.config.model_type} is not supported yet. Please create an Issue."
+        )
+
     # Patch each layer
-    for layer in model.model.model.layers:
+    for layer in layers:
         # Add QKV, O fallback implementations to start
         # These will be overwritten later (if some conditions apply)
         layer.self_attn.apply_qkv = types.MethodType(

src/axolotl/monkeypatch/multipack.py

@@ -22,6 +22,10 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "phi3",
     "gemma",
     "gemma2",
+    "gemma3",
+    "gemma3_text",
+    "cohere",
+    "cohere2",
     "gemmoe",
     "starcoder2",
     "deepseek_v2",

src/axolotl/processing_strategies.py

@@ -0,0 +1,278 @@
+"""Module containing ProcessingStrategy classes and its derivative for different MultiModal Model types"""
+
+from copy import deepcopy
+from typing import Optional
+
+from PIL import Image, ImageOps
+from PIL.Image import Resampling
+from torch import Tensor
+from transformers import ProcessorMixin
+from transformers.image_utils import load_image
+
+
+class ProcessingStrategy:
+    """Base Processing Strategy class"""
+
+    def __init__(
+        self,
+        processor: ProcessorMixin,
+        chat_template: Optional[str] = None,
+        image_size: int | tuple[int, int] | None = None,
+        image_resize_algorithm: Resampling | None = None,
+    ):
+        self.processor = processor
+        self.chat_template = chat_template
+        self.image_token = None
+        self.image_token_id = None
+
+        self.image_size = image_size
+        self.image_resize_algorithm = (
+            image_resize_algorithm or Image.Resampling.BILINEAR
+        )
+
+        if hasattr(processor, "image_token"):
+            self.image_token = processor.image_token
+            self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
+                self.image_token
+            )
+
+    def __call__(self, examples: list[dict]) -> list[dict]:
+        """
+        Preprocess conversation examples to ensure consistent format.
+        Converts different conversation formats to OpenAI format with 'messages'.
+        Supports two formats:
+        1. OpenAI format with 'messages'
+        2. Legacy format with 'conversations'
+
+        Args:
+            examples: list of conversation dictionaries
+
+        Returns:
+            list of dicts in OpenAI format with 'messages' key
+
+        Raises:
+            ValueError: If the conversation format is not supported
+        """
+        role_mapping = {
+            "human": "user",
+            "gpt": "assistant",
+        }
+
+        def normalize_role(role: str) -> str:
+            """Normalize role names to OpenAI format. Default to original role if not found."""
+            return role_mapping.get(role, role)
+
+        def convert_legacy_format(example: dict) -> dict:
+            """Convert legacy 'conversations' format to OpenAI 'messages' format."""
+            messages = [
+                {"role": normalize_role(convo["from"]), "content": convo["value"]}
+                for convo in example["conversations"]
+            ]
+
+            # Create new dict without 'conversations' key
+            result = deepcopy(example)
+            result.pop("conversations")
+            result["messages"] = messages
+            return result
+
+        def convert_messages_to_multimedia_messages(messages: list[dict]) -> list[dict]:
+            """Convert regular messages format to Messages format with content type"""
+
+            new_messages = []
+            for message in messages:
+                if isinstance(message["content"], str):
+                    new_messages.append(
+                        {
+                            "role": message["role"],
+                            "content": [
+                                {
+                                    "type": "text",
+                                    "text": message["content"],
+                                }
+                            ],
+                        }
+                    )
+                elif isinstance(message["content"], list):
+                    content = message["content"]
+
+                    new_messages.append(
+                        {
+                            "role": message["role"],
+                            "content": content,
+                        }
+                    )
+
+            return new_messages
+
+        processed_examples = []
+        for example in examples:
+            if not ("messages" in example or "conversations" in example):
+                raise ValueError(
+                    "Only `messages` and `conversations` message keys are currently supported."
+                )
+
+            processed_example = None
+            if "messages" in example:  # OpenAI format
+                processed_example = example
+            else:  # Legacy format
+                processed_example = convert_legacy_format(example)
+
+            # convert regular messages format to Messages format with content type
+            # for compatibility with apply_chat_template
+            processed_example["messages"] = convert_messages_to_multimedia_messages(
+                processed_example["messages"]
+            )
+
+            # find the image key if it exists
+            possible_image_keys = ["images", "image"]
+            image_key = None
+            for key in possible_image_keys:
+                if key in processed_example:
+                    image_key = key
+                    break
+
+            # if the image key exists, add the image to the first message
+            if image_key is not None:
+                # TODO: check if it's normal to be single image only for common datasets
+                # From observation, it's usually a list of single image but some datasets may have several columns for images
+                # Temporary solution: take the first image and suggest people convert their datasets to use multi-content Messages
+                image_value = processed_example[image_key][0]
+
+                # Handle image loading (Image, url, path, base64)
+                image_value = load_image(image_value)
+
+                if self.image_size is not None:
+                    assert hasattr(
+                        image_value, "resize"
+                    ), "Image does not have a resize method"
+
+                    if isinstance(self.image_size, tuple):
+                        image_value = image_value.resize(
+                            self.image_size, self.image_resize_algorithm
+                        )
+                    else:
+                        # Set the padding value; here we use black (0, 0, 0) for RGB images
+                        padding_color = (0, 0, 0)
+
+                        # When image_size is an int (square target), preserve aspect ratio then pad
+                        # This is to prevent aspect ratio distortion when resizing to square
+                        image_value = ImageOps.pad(
+                            image_value,
+                            (self.image_size, self.image_size),
+                            method=self.image_resize_algorithm,
+                            color=padding_color,
+                        )
+
+                # Look for any image type in the first message
+                # some dataset have an {type: "image"} in the first message
+                ind_to_add = None
+
+                for i, content in enumerate(
+                    processed_example["messages"][0]["content"]
+                ):
+                    # Usually datasets created with image columns, don't have it in the messages itself
+                    if content["type"] == "image" and all(
+                        k not in content for k in ["image", "url", "path", "base64"]
+                    ):
+                        ind_to_add = i
+                        break
+
+                # If an image type is found, add the image to that index
+                if ind_to_add is not None:
+                    processed_example["messages"][0]["content"][ind_to_add][
+                        "image"
+                    ] = image_value
+                else:
+                    # if no image type is found, add it to end of the first message
+                    processed_example["messages"][0]["content"].append(
+                        {
+                            "type": "image",
+                            "image": image_value,
+                        }
+                    )
+
+            processed_examples.append(processed_example)
+
+        return processed_examples
+
+    def process_labels(self, input_ids: Tensor) -> Tensor:
+        labels = input_ids.clone()
+
+        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+        labels[labels == self.processor.tokenizer.pad_token_id] = -100
+
+        # Ignore the image token index in the loss computation (model specific)
+        labels[labels == self.image_token_id] = -100
+
+        return labels
+
+
+class Qwen2VLProcessingStrategy(ProcessingStrategy):
+    """Processing Strategy class for Qwen2-VL"""
+
+    def __init__(
+        self,
+        processor: ProcessorMixin,
+        chat_template: Optional[str] = None,
+        image_size: int | tuple[int, int] | None = None,
+        image_resize_algorithm: Resampling | None = None,
+    ):
+        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
+        self.image_token = "<|image_pad|>"  # nosec
+        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            self.image_token
+        )
+
+
+class Gemma3ProcessingStrategy(ProcessingStrategy):
+    """Processing Strategy class for Gemma3"""
+
+    def __init__(
+        self,
+        processor: ProcessorMixin,
+        chat_template: Optional[str] = None,
+        image_size: int | tuple[int, int] | None = None,
+        image_resize_algorithm: Resampling | None = None,
+    ):
+        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
+        self.image_token = processor.tokenizer.special_tokens_map["boi_token"]
+        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            self.image_token
+        )
+
+    def process_labels(self, input_ids):
+        labels = input_ids.clone()
+
+        # Follows https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora
+        labels[labels == self.processor.tokenizer.pad_token_id] = -100
+        labels[labels == self.image_token_id] = -100
+        labels[labels == 262144] = -100  # corresponds to <image_soft_token>
+
+        return labels
+
+
+def get_processing_strategy(
+    processor: ProcessorMixin,
+    chat_template,
+    chat_template_type,
+    image_size: int | tuple[int, int] | None = None,
+    image_resize_algorithm: Resampling | None = None,
+):
+    if chat_template_type == "qwen2_vl":
+        return Qwen2VLProcessingStrategy(
+            processor, chat_template, image_size, image_resize_algorithm
+        )
+    if chat_template_type == "gemma3":
+        return Gemma3ProcessingStrategy(
+            processor, chat_template, image_size, image_resize_algorithm
+        )
+    if chat_template_type in [
+        "llama3_2_vision",
+        "llava",
+        "mistral_v7_tekken",
+        "pixtral",
+    ]:
+        return ProcessingStrategy(
+            processor, chat_template, image_size, image_resize_algorithm
+        )
+    raise ValueError(f"Unsupported chat template type: {chat_template_type}")

src/axolotl/prompt_strategies/chat_template.py

@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
+from axolotl.utils.schemas.datasets import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")
@@ -411,11 +411,15 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         if turn_idx >= len(turns):
             raise ValueError(f"Turn index {turn_idx} out of range")
 
-        # mistral does not output message if it contains only system message
+        # mistral/gemma3 does not output message if it contains only system message
         if (
             turn_idx == 0
             and turns[0].get("role") == "system"
-            and "mistral" in self.tokenizer.name_or_path.lower()
+            and (
+                "mistral" in self.tokenizer.name_or_path.lower()
+                # gemma3 uses gemma tokenizer
+                or "gemma" in self.tokenizer.name_or_path.lower()
+            )
         ):
             return -1, -1
 

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -3,7 +3,7 @@ DPO prompt strategies for using tokenizer chat templates.
 """
 
 from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
-from axolotl.utils.config.models.input.v0_4_1 import handle_legacy_message_fields_logic
+from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
 
 
 def default(

src/axolotl/train.py

@@ -14,6 +14,7 @@ import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
+from huggingface_hub.errors import OfflineModeIsEnabled
 from peft import PeftConfig, PeftModel
 from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
@@ -26,6 +27,7 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
@@ -156,6 +158,8 @@ def setup_signal_handler(
                 _model.save_pretrained(
                     cfg.output_dir, safe_serialization=safe_serialization
                 )
+
+            cleanup_distributed()
             sys.exit(0)
 
         _model_weakref = weakref.ref(model)
@@ -169,7 +173,7 @@ def execute_training(
     cfg: DictDefault, trainer: Any, resume_from_checkpoint: str | None
 ):
     """
-    Execute the training process with appropriate backend configurations.
+    Execute the training process with appropriate SDP kernel configurations.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -177,9 +181,6 @@ def execute_training(
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
     LOG.info("Starting trainer...")
-    if cfg.group_by_length:
-        LOG.info("hang tight... sorting dataset for group_by_length")
-
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
@@ -305,7 +306,7 @@ def create_model_card(cfg: DictDefault, trainer: Trainer):
                     model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
-        except (AttributeError, UnicodeDecodeError):
+        except (AttributeError, UnicodeDecodeError, OfflineModeIsEnabled):
             pass
     elif cfg.hub_model_id:
         # Defensively push to the hub to ensure the model card is updated
@@ -317,6 +318,7 @@ def save_initial_configs(
     tokenizer: PreTrainedTokenizer,
     model: PreTrainedModel,
     peft_config: PeftConfig | None,
+    processor: ProcessorMixin | None,
 ):
     """
     Save initial configurations before training.
@@ -344,6 +346,10 @@ def save_initial_configs(
         LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
         model.config.save_pretrained(str(output_dir))
 
+    if processor:
+        LOG.info(f"Pre-saving processor to {cfg.output_dir}...")
+        processor.save_pretrained(str(output_dir))
+
 
 def setup_model_card(cfg: DictDefault):
     """
@@ -411,6 +417,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,
+    ProcessorMixin | None,
 ]:
     """
     Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
@@ -426,6 +433,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
             - Model
             - Tokenizer
             - PEFT config
+            - Processor
     """
     # Load tokenizer, processor and model
     model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
@@ -456,6 +464,7 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
         model,
         tokenizer,
         peft_config,
+        processor,
     )
 
 
@@ -472,42 +481,35 @@ def train(
     Returns:
         Tuple of (model, tokenizer) after training
     """
-    # Setup model, tokenizer, (causal or RLHF) trainer etc.
+    # Setup model, tokenizer, (causal or RLHF) trainer, etc.
     (
         trainer,
         model,
         tokenizer,
         peft_config,
+        processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
-    # Determine if we need to resume from a checkpoint
-    resume_from_checkpoint = determine_resume_checkpoint(cfg)
-
-    # Configuration for saving
-    safe_serialization = cfg.save_safetensors is True
-
     # Handle untrained tokens if configured
+    safe_serialization = cfg.save_safetensors is True
     train_dataset = dataset_meta.train_dataset
     handle_untrained_tokens_fix(
         cfg, model, tokenizer, train_dataset, safe_serialization
     )
 
-    # Save initial configs
+    # Additional setup
-    save_initial_configs(cfg, tokenizer, model, peft_config)
+    save_initial_configs(cfg, tokenizer, model, peft_config, processor)
-
-    # Set up signal handler for graceful termination
     setup_signal_handler(cfg, model, safe_serialization)
-
-    # Set up badges and config info for model card
     setup_model_card(cfg)
 
     # Execute the training
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
     execute_training(cfg, trainer, resume_from_checkpoint)
 
-    # Save the trained model
+    # Save the trained model and cleanup
     save_trained_model(cfg, trainer, model, safe_serialization)
-
-    # Create model card
     create_model_card(cfg, trainer)
+    if not cfg.use_ray:
+        cleanup_distributed()
 
     return model, tokenizer, trainer

src/axolotl/utils/callbacks/__init__.py

@@ -33,7 +33,6 @@ from trl.models import unwrap_model_for_generation
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.callbacks.perplexity import Perplexity
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 from axolotl.utils.distributed import (
     barrier,
     broadcast_dict,
@@ -43,6 +42,7 @@ from axolotl.utils.distributed import (
     is_main_process,
     zero_first,
 )
+from axolotl.utils.schemas.config import AxolotlInputConfig
 
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments
@@ -816,27 +816,6 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
         return control
 
 
-class SaveModelCallback(TrainerCallback):
-    """Callback to save model on train end"""
-
-    def on_step_end(  # pylint: disable=unused-argument
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        # Save
-        if state.global_step >= state.max_steps:
-            control.should_save = True
-
-    def on_train_end(  # pylint: disable=unused-argument
-        self, args, state, control, **kwargs
-    ):
-        control.should_save = True
-        return control
-
-
 class GCCallback(TrainerCallback):
     """Callback to garbage collect torch cache"""
 

src/axolotl/utils/collators/batching.py

@@ -1,14 +1,59 @@
 """
-DataCollator for axolotl to pad labels and position_ids for packed sequences
+Data collators for axolotl to pad labels and position_ids for packed sequences. Also
+includes logic for handling sequence parallelism collation.
 """
 
+import logging
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
 import numpy as np
+import torch
+import torch.distributed as dist
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
+logger = logging.getLogger(__name__)
+
+
+def adjust_position_ids_for_slice(
+    position_ids: torch.Tensor, start_idx: int
+) -> torch.Tensor:
+    """
+    Adjust position IDs for a sliced sequence to maintain proper relative positions.
+    This handles the case where position IDs might not be contiguous due to sample
+    packing.
+    """
+    # Convert to tensor if not already
+    # Find the boundaries between samples (where position_ids reset)
+    adjusted_pos_ids = position_ids.clone()
+
+    # Process each sequence in the batch
+    for i in range(position_ids.shape[0]):
+        seq = position_ids[i]
+
+        # Find sample boundaries
+        boundaries = []
+        for j in range(1, len(seq)):
+            if seq[j] < seq[j - 1]:
+                boundaries.append(j)
+
+        # No need to adjust if there are no boundaries or this is a single sample
+        if not boundaries:
+            adjusted_pos_ids[i] = seq - start_idx
+            continue
+
+        # Adjust each segment separately
+        prev_boundary = 0
+        for boundary in boundaries:
+            adjusted_pos_ids[i, prev_boundary:boundary] -= start_idx
+            prev_boundary = boundary
+
+        # Last segment
+        adjusted_pos_ids[i, prev_boundary:] -= start_idx
+
+    return adjusted_pos_ids
+
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -43,6 +88,8 @@ class DataCollatorForSeq2Seq:
             The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
         return_tensors (`str`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
+        sequence_parallel_degree (`int`):
+            The degree of sequence parallelism. Default to 1 for no sequence parallelism.
     """
 
     tokenizer: PreTrainedTokenizerBase
@@ -53,8 +100,19 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
+    sequence_parallel_degree: int = 1
+
+    def __post_init__(self):
+        if self.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+            # Get information about our position in the SP group
+            sp_group = get_ring_attn_group()
+            self.local_rank = dist.get_rank(group=sp_group)
+            self.local_world_size = dist.get_world_size(group=sp_group)
 
     def __call__(self, features, return_tensors=None):
+        has_attn_mask = "attention_mask" in features[0].keys()
         labels = None
         if return_tensors is None:
             return_tensors = self.return_tensors
@@ -107,6 +165,8 @@ class DataCollatorForSeq2Seq:
             pad_to_multiple_of=self.pad_to_multiple_of,
             return_tensors=return_tensors,
         )
+        if not has_attn_mask:
+            del features["attention_mask"]
 
         # prepare decoder_input_ids
         if (
@@ -119,8 +179,43 @@ class DataCollatorForSeq2Seq:
             )
             features["decoder_input_ids"] = decoder_input_ids
 
+        if self.sequence_parallel_degree > 1:
+            features = self.apply_sequence_parallelism(features)
+
         return features
 
+    def apply_sequence_parallelism(
+        self, batch: dict[str, torch.Tensor]
+    ) -> torch.Tensor:
+        """
+        Apply sequence parallelism slicing to a batch.
+
+        Args:
+            batch: Batch dictionary from parent collator.
+
+        Returns:
+            Sliced batch dictionary.
+        """
+        keys_to_slice = ["input_ids", "attention_mask", "labels", "position_ids"]
+
+        for key in keys_to_slice:
+            if key in batch:
+                seq_len = batch[key].shape[1]
+                slice_size = seq_len // self.local_world_size
+                start_idx = self.local_rank * slice_size
+                end_idx = (
+                    start_idx + slice_size
+                    if self.local_rank < self.local_world_size - 1
+                    else seq_len
+                )
+                batch[key] = batch[key][:, start_idx:end_idx]
+
+                # Special handling for position_ids
+                if key == "position_ids" and self.local_rank > 0:
+                    batch[key] = adjust_position_ids_for_slice(batch[key], start_idx)
+
+        return batch
+
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
@@ -148,6 +243,7 @@ class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                         np.array(item[feature]) for item in features_ if feature in item
                     ]
                     out_features[i][feature] = np.concatenate(arrays)
+
         return super().__call__(out_features, return_tensors=return_tensors)
 
 
@@ -177,6 +273,7 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                         np.array(item[feature]) for item in features_ if feature in item
                     ]
                     out_features[i][feature] = np.concatenate(arrays)
+
         return super().__call__(out_features, return_tensors=return_tensors)
 
 

src/axolotl/utils/collators/mm_chat.py

@@ -2,15 +2,17 @@
 Collators for multi-modal chat messages and packing
 """
 
-from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
-from PIL import Image
+import torch
-from transformers import PreTrainedTokenizerBase, ProcessorMixin
+from torch import Tensor
+from transformers import PreTrainedTokenizerBase
 from transformers.data.data_collator import DataCollatorMixin
 from transformers.utils import PaddingStrategy
 
+from axolotl.processing_strategies import ProcessingStrategy
+
 
 @dataclass
 class MultiModalChatDataCollator(DataCollatorMixin):
@@ -19,11 +21,9 @@ class MultiModalChatDataCollator(DataCollatorMixin):
     """
 
     tokenizer: PreTrainedTokenizerBase
-    processor: ProcessorMixin
+    processing_strategy: ProcessingStrategy
-    return_tensors: str = "pt"
-    chat_template: Optional[str] = None
     packing: bool = False
-    max_images: int = -1
+    return_tensors: str = "pt"
     padding: Union[bool, str, PaddingStrategy] = True
     pad_to_multiple_of: Optional[int] = None
 
@@ -31,162 +31,62 @@ class MultiModalChatDataCollator(DataCollatorMixin):
         if self.packing:
             raise ValueError("Packing is currently not supported.")
 
-    def torch_call(
+    def torch_call(self, examples: list[dict]) -> dict[str, Any]:
-        self, examples: list[Union[list[int], Any, dict[str, Any]]]
+        return self.process_rows(examples)
-    ) -> dict[str, Any]:
-        # Handle dict or lists with proper padding and conversion to tensor.
-
-        return self.__class__.process_rows(
-            examples, self.processor, self.chat_template, self.max_images
-        )
-
-    @staticmethod
-    def process_rows(examples, processor, chat_template, max_images, length_only=False):
-        # HINT: use `_torch_collate_batch` to stack and pad tensors
-        # see also DataCollatorWithFlattening and DefaultDataCollator
-
-        # *** This is COPIED from the trl example sft_vlm.py code ***
-        # use this as a starting point
-
-        def _preprocess(examples: list[dict]) -> list[dict]:
-            """
-            Preprocess conversation examples to ensure consistent format.
-
-            Converts different conversation formats to OpenAI format with 'messages'.
-            Supports two formats:
-            1. OpenAI format with 'messages'
-            2. Legacy format with 'conversations'
-
-            Args:
-                examples: list of conversation dictionaries
-
-            Returns:
-                dict in OpenAI format with 'messages' key
-
-            Raises:
-                ValueError: If the conversation format is not supported
-            """
-            role_mapping = {
-                "human": "user",
-                "gpt": "assistant",
-            }
-
-            def normalize_role(role: str) -> str:
-                """Normalize role names to OpenAI format. Default to original role if not found."""
-                return role_mapping.get(role, role)
-
-            def convert_legacy_format(example: dict) -> dict:
-                """Convert legacy 'conversations' format to OpenAI 'messages' format."""
-                messages = [
-                    {
-                        "role": normalize_role(convo["from"]),
-                        "content": convo["value"],
-                    }
-                    for convo in example["conversations"]
-                ]
-
-                # Create new dict without 'conversations' key
-                result = deepcopy(example)
-                result.pop("conversations")
-                return {"messages": messages, **result}
-
-            processed_examples = []
-            for example in examples:
-                # OpenAI format
-                if "messages" in example:
-                    processed_examples.append(example)
-
-                # Legacy format
-                elif "conversations" in example:
-                    processed_examples.append(convert_legacy_format(example))
-
-                else:
-                    raise ValueError(
-                        "Only `messages` and `conversations` message keys are currently supported."
-                    )
-
-            return processed_examples
-
-        def _process_images(examples, max_images):
-            """
-            Process images from examples, ensuring consistency in image presence and applying max_images limit.
-
-            Args:
-                examples: List of dictionaries that may contain 'images' key
-                max_images: Maximum number of images to keep per example (0 means no limit)
-
-            Returns:
-                Either None (if no images) or List[Image objects] (if all examples have images)
-
-            Raises:
-                ValueError: If there's a mix of None and non-None images
-            """
-
-            def get_image(example):
-                if "images" not in example:
-                    return None
-                images = example["images"]
-                if isinstance(images, str):
-                    return Image.open(images)
-                return images
-
-            images = [get_image(example) for example in examples]
-
-            # Count None and non-None images
-            none_count = sum(1 for img in images if img is None)
-
-            # All images are None
-            if none_count == len(images):
-                return None
-
-            # Mix of None and non-None images
-            if none_count > 0:
-                raise ValueError(
-                    "All images should be either None or not None. "
-                    "Please provide images for all examples or None."
-                )
-
-            # Apply max_images limit if specified
-            if max_images > 0:
-                images = [
-                    (
-                        img_batch[:max_images]
-                        if isinstance(img_batch, (list, tuple))
-                        else img_batch
-                    )
-                    for img_batch in images
-                ]
-
-            return images
 
+    def process_rows(
+        self,
+        examples: list[dict],
+    ) -> dict[str, Tensor]:
         # Preprocess the examples
-        examples = _preprocess(examples)
+        examples = self.processing_strategy(examples)
 
-        # Get the texts and images, and apply the chat template
+        # Initialize batch
-        texts = [
+        batch: dict[str, Any] = {}
-            processor.apply_chat_template(
+
-                example["messages"], chat_template=chat_template, tokenize=False
+        # Process each example
+        for example in examples:
+            # Apply chat template to process the example
+            # This method requires transformers>=4.49.0
+            result = self.processing_strategy.processor.apply_chat_template(
+                example["messages"],
+                add_generation_prompt=True,
+                tokenize=True,
+                return_tensors="pt",
+                padding=True,
+                return_dict=True,
+                chat_template=self.processing_strategy.chat_template,
             )
-            for example in examples
-        ]
 
-        images = _process_images(examples, max_images=max_images)
+            # TODO: Check if need handling for len(input_ids) > sequence_len
 
-        # Tokenize the texts and process the images
+            # Add the processed tensors to our batch
-        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
+            for key in result.keys():
+                if key not in batch:
+                    batch[key] = []
 
-        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+                batch[key].append(result[key].squeeze(0))
-        labels = batch["input_ids"].clone()
+
-        labels[labels == processor.tokenizer.pad_token_id] = -100  #
+        # Pad sequences to the same length
-        # Ignore the image token index in the loss computation (model specific)
+        input_ids = torch.nn.utils.rnn.pad_sequence(
-        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            batch["input_ids"],
-            processor.image_token
+            batch_first=True,
+            padding_value=self.tokenizer.pad_token_id,
         )
-        labels[labels == image_token_id] = -100
-        batch["labels"] = labels
 
-        if length_only:
+        attention_mask = torch.nn.utils.rnn.pad_sequence(
-            return {
+            batch["attention_mask"], batch_first=True, padding_value=0
-                "length": [len(sample["input_ids"]) for sample in batch["input_ids"]]
+        )
-            }
+
-        return batch
+        # Create the final batch
+        final_batch = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+
+        # Process the labels
+        final_batch["labels"] = self.processing_strategy.process_labels(
+            final_batch["input_ids"]
+        )
+
+        return final_batch

src/axolotl/utils/config/__init__.py

@@ -12,19 +12,13 @@ from transformers.utils.import_utils import is_torch_npu_available
 from axolotl.integrations.base import PluginManager
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import MULTIMODAL_AUTO_MODEL_MAPPING, load_model_config
+from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
-    AxolotlInputConfig as AxolotlInputConfigBase,
+from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
-)
-from axolotl.utils.config.models.input.v0_4_1 import (
-    DPODataset,
-    KTODataset,
-    SFTDataset,
-)
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model_config
 
 LOG = logging.getLogger("axolotl")
 
@@ -84,6 +78,7 @@ def resolve_dtype(cfg):
         cfg.bf16 = False
     else:
         torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
+        torch.backends.cudnn.allow_tf32 = cfg.tf32 or False
         if cfg.bf16:
             cfg.fp16 = False
 
@@ -131,6 +126,9 @@ def normalize_config(cfg):
             with open(ds_config_path, encoding="utf-8") as f:
                 cfg.deepspeed = json.load(f)
 
+    if cfg.sequence_parallel_degree is None:
+        cfg.sequence_parallel_degree = 1
+
     if cfg.saves_per_epoch:
         save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
         if save_steps < 1.0:  # prevent saves on every step
@@ -161,7 +159,7 @@ def normalize_config(cfg):
 
     cfg.is_multimodal = (
         hasattr(model_config, "model_type")
-        and model_config.model_type in ["llava", "mllama"]
+        and model_config.model_type in MULTIMODAL_AUTO_MODEL_MAPPING
         or any(
             multimodal_name in cfg.base_model.lower()
             for multimodal_name in [
@@ -174,7 +172,6 @@ def normalize_config(cfg):
         cfg.processor_config = (
             cfg.processor_config or cfg.base_model_config or cfg.base_model
         )
-        model_config = model_config.text_config
 
     cfg.model_config_type = model_config.model_type
 

src/axolotl/utils/config/models/input/v0_4_1/trl.py

@@ -1,91 +0,0 @@
-"""
-GRPO specific configuration args
-"""
-
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class TRLConfig(BaseModel):
-    """
-    Input args for TRL.
-    """
-
-    beta: Optional[float] = Field(
-        default=None,
-        json_schema_extra={"description": "Beta for RL training"},
-    )
-    max_completion_length: Optional[int] = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Maximum length of the completion for RL training"
-        },
-    )
-
-    # GRPO specific args
-    # Ref: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/grpo_config.py#L22
-    use_vllm: Optional[bool] = Field(
-        default=False,
-        json_schema_extra={"description": "Whether to use VLLM for RL training"},
-    )
-    vllm_device: Optional[str] = Field(
-        default="auto",
-        json_schema_extra={"description": "Device to use for VLLM"},
-    )
-    vllm_gpu_memory_utilization: Optional[float] = Field(
-        default=0.9,
-        json_schema_extra={"description": "GPU memory utilization for VLLM"},
-    )
-    vllm_dtype: Optional[str] = Field(
-        default="auto",
-        json_schema_extra={"description": "Data type for VLLM"},
-    )
-    vllm_max_model_len: Optional[int] = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Maximum length of the model context for VLLM"
-        },
-    )
-
-    reward_funcs: Optional[list[str]] = Field(
-        default=None,
-        json_schema_extra={"description": "List of reward functions to load"},
-    )
-    reward_weights: Optional[list[float]] = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Weights for each reward function. Must match the number of reward functions."
-        },
-    )
-    num_generations: Optional[int] = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Number of generations to sample. The global batch size (num_processes * per_device_batch_size) must be divisible by this value."
-        },
-    )
-    log_completions: Optional[bool] = Field(
-        default=False,
-        json_schema_extra={"description": "Whether to log completions"},
-    )
-    sync_ref_model: Optional[bool] = Field(
-        default=False,
-        json_schema_extra={
-            "description": (
-                "Whether to sync the reference model every `ref_model_sync_steps` "
-                "steps, using the `ref_model_mixup_alpha` parameter."
-            )
-        },
-    )
-    ref_model_mixup_alpha: Optional[float] = Field(
-        default=0.9,
-        json_schema_extra={
-            "description": "Mixup alpha for the reference model. Requires `sync_ref_model=True`."
-        },
-    )
-    ref_model_sync_steps: Optional[int] = Field(
-        default=64,
-        json_schema_extra={
-            "description": "Sync steps for the reference model. Requires `sync_ref_model=True`."
-        },
-    )

src/axolotl/utils/data/shared.py

@@ -6,8 +6,12 @@ from pathlib import Path
 from typing import Optional, Union
 
 from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, snapshot_download
-from huggingface_hub.errors import HFValidationError
+from huggingface_hub.errors import (
+    HFValidationError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+)
 
 from axolotl.utils.dict import DictDefault
 
@@ -70,20 +74,25 @@ def load_dataset_w_config(
     # pylint: disable=invalid-name
     ds: Optional[Union[Dataset, DatasetDict]] = None  # pylint: disable=invalid-name
     ds_from_hub = False
-    ds_trust_remote_code = config_dataset.trust_remote_code
     try:
         # this is just a basic check to see if the path is a
         # valid HF dataset that's loadable
-        load_dataset(
+        snapshot_download(
-            config_dataset.path,
+            repo_id=config_dataset.path,
-            name=config_dataset.name,
+            repo_type="dataset",
-            streaming=True,
             token=use_auth_token,
             revision=config_dataset.revision,
-            trust_remote_code=ds_trust_remote_code,
+            ignore_patterns=["*"],
         )
         ds_from_hub = True
-    except (FileNotFoundError, ConnectionError, HFValidationError, ValueError):
+    except (
+        RepositoryNotFoundError,
+        RevisionNotFoundError,
+        FileNotFoundError,
+        ConnectionError,
+        HFValidationError,
+        ValueError,
+    ):
         pass
 
     ds_from_cloud = False
@@ -229,7 +238,8 @@ def load_dataset_w_config(
             trust_remote_code=config_dataset.trust_remote_code,
             **load_ds_kwargs,
         )
-    else:
+    elif config_dataset.data_files:
+        fp: str | list[str] | None = None
         if isinstance(config_dataset.data_files, str):
             fp = hf_hub_download(
                 repo_id=config_dataset.path,

src/axolotl/utils/distributed.py

@@ -71,8 +71,8 @@ def barrier():
 
 def is_main_process():
     """
-    Check if the current process is the main process.
+    Check if the current process is the main process. If not in distributed mode,
-    If not in distributed mode, always return True.
+    always return `True`.
     """
     if not is_distributed():
         return True
@@ -87,6 +87,18 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
+def cleanup_distributed():
+    """
+    Destroy process group if torch distributed is initialized. Called in training early
+    termination or when training successfully completes.
+    """
+    # Ensure that all operations are completed before destroying the process group
+    torch.cuda.synchronize()
+    # Destroy the process group
+    if torch.distributed.is_initialized():
+        torch.distributed.destroy_process_group()
+
+
 @contextmanager
 def zero_only():
     """

src/axolotl/utils/models.py

@@ -8,7 +8,7 @@ import math
 import os
 import types
 from functools import cached_property
-from typing import Any, Dict, Optional, Tuple, Union  # noqa: F401
+from typing import Any, Dict, Optional, Tuple
 
 import addict
 import bitsandbytes as bnb
@@ -25,7 +25,7 @@ from peft import (
     prepare_model_for_kbit_training,
 )
 from torch import nn
-from transformers import (  # noqa: F401
+from transformers import (
     AddedToken,
     AutoConfig,
     AutoModelForCausalLM,
@@ -34,12 +34,17 @@ from transformers import (  # noqa: F401
     AutoTokenizer,
     AwqConfig,
     BitsAndBytesConfig,
+    Gemma3ForConditionalGeneration,
     GPTQConfig,
     LlavaForConditionalGeneration,
+    Mistral3ForConditionalGeneration,
     MllamaForConditionalGeneration,
+    PretrainedConfig,
     PreTrainedModel,
     PreTrainedTokenizerBase,
     ProcessorMixin,
+    Qwen2_5_VLForConditionalGeneration,
+    Qwen2VLForConditionalGeneration,
 )
 from transformers.integrations.deepspeed import (
     HfTrainerDeepSpeedConfig,
@@ -67,7 +72,16 @@ from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrap
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
 
-LOG = logging.getLogger("axolotl")
+LOG = logging.getLogger(__name__)
+
+MULTIMODAL_AUTO_MODEL_MAPPING = {
+    "mllama": MllamaForConditionalGeneration,
+    "llava": LlavaForConditionalGeneration,
+    "qwen2_vl": Qwen2VLForConditionalGeneration,
+    "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
+    "mistral3": Mistral3ForConditionalGeneration,
+    "gemma3": Gemma3ForConditionalGeneration,
+}
 
 
 # copied from accelerator.FullyShardedDataParallelPlugin
@@ -94,9 +108,30 @@ def get_module_class_from_name(module, name):
     return None
 
 
-def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
+def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
+    # Set use_cache to False
+    if hasattr(model_config, "use_cache"):
+        model_config.use_cache = False
+
     if cfg.is_multimodal:
-        model_config = model_config.text_config
+        # For multimodal configs, use_cache is set in the text_config
+        if hasattr(model_config, "get_text_config"):
+            text_config = model_config.get_text_config()
+            if hasattr(text_config, "use_cache"):
+                text_config.use_cache = False
+        else:
+            raise ValueError(
+                "No text config found for multimodal model. Please raise an Issue with model details."
+            )
+
+        # check if image_size is not set and load image size from model config if available
+        if (
+            cfg.image_size is None
+            and hasattr(model_config, "vision_config")
+            and hasattr(model_config.vision_config, "image_size")
+        ):
+            cfg.image_size = model_config.vision_config.image_size
+            LOG.debug(f"Loaded image size: {cfg.image_size} from model config")
 
     quant_config_exists = (
         hasattr(model_config, "quantization_config")
@@ -435,6 +470,31 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
         **processor_kwargs,
     )
 
+    # Attempt to load image size from processor if available
+    if (
+        cfg.image_size is None
+        and hasattr(processor, "size")
+        and any(dim in processor.size for dim in ["width", "height"])
+    ):
+        im_width = None
+        im_height = None
+        if "width" in processor.size:
+            im_width = processor.size["width"]
+        if "height" in processor.size:
+            im_height = processor.size["height"]
+
+        # If both width and height are set, use a tuple
+        if im_width is not None and im_height is not None:
+            cfg.image_size = (im_width, im_height)
+        # If only width is set, use as integer
+        elif im_width is not None:
+            cfg.image_size = im_width
+        # If only height is set, use as integer
+        elif im_height is not None:
+            cfg.image_size = im_height
+
+        LOG.debug(f"Loaded image size: {cfg.image_size} from processor")
+
     return processor
 
 
@@ -471,14 +531,19 @@ class ModelLoader:
 
         # init model config
         self.model_config = load_model_config(cfg)
-        if cfg.is_multimodal:
-            self.text_model_config = self.model_config.text_config
-        else:
-            self.text_model_config = self.model_config
 
-        self.AutoModelLoader = AutoModelForCausalLM  # pylint: disable=invalid-name
+        self.auto_model_loader = AutoModelForCausalLM  # pylint: disable=invalid-name
 
     def apply_patches(self) -> None:
+        # patch gemma3 conditional generation forward before loading plugins
+        # as it could be overridden by plugins
+        if self.cfg.model_config_type == "gemma3":
+            from axolotl.monkeypatch.gemma3 import (
+                patch_gemma3conditionalgeneration_forward,
+            )
+
+            patch_gemma3conditionalgeneration_forward()
+
         # load any patches from plugins
         from axolotl.integrations.base import PluginManager
 
@@ -547,6 +612,17 @@ class ModelLoader:
 
             patch_self_attn_lora(self.cfg)
 
+        if self.cfg.sequence_parallel_degree and self.cfg.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.attention.ring_attn import register_ring_attn
+
+            # Initialize ring attn for sequence parallelism. This must be done after
+            # model init but before the first forward pass, since it modifies flash
+            # attn to use ring comm for SP training across multiple GPUs.
+            register_ring_attn(
+                sequence_parallel_degree=self.cfg.sequence_parallel_degree,
+                heads_k_stride=self.cfg.heads_k_stride,
+            )
+
     def patch_attention(self) -> None:
         if hasattr(self.model_config, "model_type"):
             if self.model_config.model_type == "mllama" and self.cfg.flash_attention:
@@ -603,7 +679,7 @@ class ModelLoader:
 
             patch_self_attn_lora()
 
-    def patch_llama_derived_model(self) -> None:
+    def patch_llama_derived_model(self):
         """Modify all llama derived models in one block"""
         self.patch_loss_llama()
 
@@ -653,25 +729,16 @@ class ModelLoader:
                 "Shifted-sparse attention not currently implemented without flash attention."
             )
 
-    def set_auto_model_loader(self) -> None:
+    def set_auto_model_loader(self):
-        """set self.AutoModelLoader
+        """
-        - default value: AutoModelForCausalLM (set at __init__)
+        Set self.auto_model_loader. Defaults to `transformers.AutoModelForCausalLM`
-        - when using a multi modality model, self.AutoModelLoader should
+        (set at `__init__`). When using a multimodal model, `self.auto_model_loader`
-          be set according to model type of the model
+        should be set according to the type of the model.
         """
         if self.cfg.is_multimodal:
-            if self.model_config.model_type == "llava":
+            self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
-                self.AutoModelLoader = (  # pylint: disable=invalid-name
+                self.model_config.model_type, AutoModelForVision2Seq
-                    LlavaForConditionalGeneration
+            )
-                )
-            elif self.model_config.model_type == "mllama":
-                self.AutoModelLoader = (  # pylint: disable=invalid-name
-                    MllamaForConditionalGeneration
-                )
-            else:
-                self.AutoModelLoader = (
-                    AutoModelForVision2Seq  # pylint: disable=invalid-name
-                )
 
     def set_device_map_config(self) -> None:
         device_map = self.cfg.device_map
@@ -695,7 +762,7 @@ class ModelLoader:
             from accelerate import infer_auto_device_map
 
             with init_empty_weights():
-                model_canvas = self.AutoModelLoader.from_config(
+                model_canvas = self.auto_model_loader.from_config(
                     self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
                 )
@@ -892,8 +959,6 @@ class ModelLoader:
             quantization_config = (
                 quantization_config or self.model_kwargs["quantization_config"]
             )
-            if self.cfg.is_multimodal:
-                self.model_config.text_config = self.text_model_config
             self.model = load_sharded_model_quant(
                 self.base_model,
                 self.model_config,
@@ -914,13 +979,26 @@ class ModelLoader:
 
             _ = _configure_zero3_memory_efficient_loading()
 
-            if self.cfg.is_multimodal:
+            # Load model with random initialization if specified
-                self.model_config.text_config = self.text_model_config
+            if self.cfg.random_init_weights:
-            self.model = self.AutoModelLoader.from_pretrained(
+                # AutoModel classes support the from_config method
-                self.base_model,
+                if self.auto_model_loader in [
-                config=self.model_config,
+                    AutoModelForCausalLM,
-                **self.model_kwargs,
+                    AutoModelForVision2Seq,
-            )
+                ]:
+                    self.model = self.auto_model_loader.from_config(
+                        config=self.model_config,
+                    )
+                else:
+                    self.model = self.auto_model_loader(
+                        config=self.model_config,
+                    )
+            else:
+                self.model = self.auto_model_loader.from_pretrained(
+                    self.base_model,
+                    config=self.model_config,
+                    **self.model_kwargs,
+                )
 
             #  TODO (MengqingCao) split these patches seperately
             if self.cfg.flash_attention and not self.inference:
@@ -955,10 +1033,8 @@ class ModelLoader:
             and self.model_type != "AutoModelForCausalLM"
             and not self.cfg.trust_remote_code
         ):
-            if self.cfg.is_multimodal:
-                self.model_config.text_config = self.text_model_config
             if self.cfg.gptq:
-                self.model = self.AutoModelLoader.from_pretrained(
+                self.model = self.auto_model_loader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -972,26 +1048,8 @@ class ModelLoader:
                     **self.model_kwargs,
                 )
         else:
-            # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
-            # when training starts
-            if (
-                hasattr(self.text_model_config, "max_seq_len")
-                and self.text_model_config.max_seq_len
-                and self.cfg.sequence_len > self.text_model_config.max_seq_len
-            ):
-                self.text_model_config.max_seq_len = self.cfg.sequence_len
-                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
-            elif (
-                hasattr(self.text_model_config, "max_sequence_length")
-                and self.text_model_config.max_sequence_length
-                and self.cfg.sequence_len > self.text_model_config.max_sequence_length
-            ):
-                self.text_model_config.max_sequence_length = self.cfg.sequence_len
-                LOG.warning(f"increasing context length to {self.cfg.sequence_len}")
             if self.cfg.gptq:
-                if self.cfg.is_multimodal:
+                self.model = self.auto_model_loader.from_pretrained(
-                    self.model_config.text_config = self.text_model_config
-                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1009,9 +1067,7 @@ class ModelLoader:
 
                 _ = _configure_zero3_memory_efficient_loading()
 
-                if self.cfg.is_multimodal:
+                self.model = self.auto_model_loader.from_pretrained(
-                    self.model_config.text_config = self.text_model_config
-                self.model = self.AutoModelLoader.from_pretrained(
                     self.base_model,
                     config=self.model_config,
                     trust_remote_code=self.cfg.trust_remote_code or False,
@@ -1174,7 +1230,9 @@ class ModelLoader:
             )
         ):
             resize_kwargs = {}
-            if self.cfg.mean_resizing_embeddings is not None:
+            if self.cfg.mean_resizing_embeddings is not None and not (
+                self.model_config.model_type == "llava"
+            ):
                 resize_kwargs["mean_resizing"] = self.cfg.mean_resizing_embeddings
             self.model.resize_token_embeddings(embeddings_len, **resize_kwargs)
         else:
@@ -1273,8 +1331,6 @@ class ModelLoader:
                 requires_grad.append(f"{name}: {param.requires_grad}")
         if len(requires_grad) == 0:
             LOG.warning("there are no parameters that require gradient updates")
-        if hasattr(self.model, "config"):
-            self.model.config.use_cache = False
 
         if self.cfg.flash_optimum:
             from optimum.bettertransformer import BetterTransformer
@@ -1307,7 +1363,7 @@ def load_model(
     """
     Load a model for a given configuration and tokenizer.
     """
-    loader = ModelLoader(
+    model_loader = ModelLoader(
         cfg,
         tokenizer,
         processor=processor,
@@ -1315,7 +1371,7 @@ def load_model(
         reference_model=reference_model,
         **kwargs,
     )
-    return loader.load_model()
+    return model_loader.load_model()
 
 
 def load_adapter(model, cfg, adapter, inference=False):

src/axolotl/utils/samplers/multipack.py

@@ -8,11 +8,13 @@ from typing import Any, Iterable, List, Union
 
 import numba
 import numpy as np
-from torch.utils.data import BatchSampler, Sampler
+from torch.utils.data import BatchSampler, Sampler, SequentialSampler
 
 from axolotl.utils.distributed import reduce_and_broadcast
 
-LOG = logging.getLogger("axolotl.utils.samplers.multipack")
+LOG = logging.getLogger(__name__)
+
+LOG.setLevel(logging.INFO)
 
 
 @numba.njit
@@ -103,10 +105,57 @@ def allocate(
     return result, s, len(result) * c * n
 
 
+@numba.njit
+def allocate_sequentially(lengths: np.ndarray, rank: int, c: int, n: int):
+    """
+    Sequential allocator that preserves example order
+
+    Parameters:
+    - lengths: The lengths of all examples
+    - rank: The current rank (for distributed training)
+    - c: The capacity of each bin (maximum sequence length)
+    - n: Number of ranks
+
+    Returns:
+    - result: List of batches for the current rank
+    - total_used: Number of actual example tokens
+    - total_slots: Maximum theoretical number of example tokens (number of bins * bin capacity)
+    """
+    result = []
+    total_used = 0
+
+    # First, do sequential packing into bins
+    all_bins = []
+    current_bin = [0 for i in range(0)]  # numba hint
+    remaining_capacity = c
+
+    for idx, size in enumerate(lengths):
+        if size <= remaining_capacity:
+            # Example fits in current bin
+            current_bin.append(idx)
+            remaining_capacity -= size
+            total_used += size
+        else:
+            # Example doesn't fit, start a new bin
+            if current_bin:  # Add non-empty bin to all_bins
+                all_bins.append(current_bin)
+            current_bin = [idx]
+            remaining_capacity = c - size
+            total_used += size
+
+    # Add the last bin if not empty
+    if current_bin:
+        all_bins.append(current_bin)
+
+    # Assign bins to ranks - each rank gets every n-th bin
+    for bin_idx in range(rank, len(all_bins), n):
+        result.append(all_bins[bin_idx])
+
+    return result, total_used, len(all_bins) * c
+
+
 class MultipackBatchSampler(BatchSampler):
-    """
+    """Batch sampler class for multipack"""
-    Batch Sampler class for multipack
-    """
 
     def __init__(
         self,
@@ -117,6 +166,7 @@ class MultipackBatchSampler(BatchSampler):
         packing_efficiency_estimate: float = 1.0,
         drop_last: bool = False,
         num_count_samples: int = 16,
+        sequential: bool = False,
         **kwargs,
     ):
         super().__init__(sampler, batch_size, drop_last)
@@ -124,6 +174,7 @@ class MultipackBatchSampler(BatchSampler):
         self.batch_max_len = batch_max_len
         self.lengths: np.ndarray = lengths
         self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+        self.sequential = sequential
 
         assert isinstance(self.lengths, np.ndarray)
 
@@ -138,6 +189,11 @@ class MultipackBatchSampler(BatchSampler):
         # the minimum packed dataset length across all ranks determined by a gather/broadcast
         self.len_across_ranks = None
 
+        if self.sequential and not isinstance(sampler, SequentialSampler):
+            LOG.warn(
+                "using sequential sample packing with non-sequential sampler, did you want to also enable curriculum_sampling?"
+            )
+
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -147,13 +203,21 @@ class MultipackBatchSampler(BatchSampler):
         lengths = self.lengths[indices]
         lengths_cumsum = np.cumsum(lengths)
 
-        batches, total_used, total_slots = allocate(
+        if self.sequential:
-            lengths=lengths,
+            batches, total_used, total_slots = allocate_sequentially(
-            lengths_cumsum=lengths_cumsum,
+                lengths=lengths,
-            rank=0,
+                rank=0,
-            c=self.batch_max_len,
+                c=self.batch_max_len,
-            n=1,
+                n=1,
-        )
+            )
+        else:
+            batches, total_used, total_slots = allocate(
+                lengths=lengths,
+                lengths_cumsum=lengths_cumsum,
+                rank=0,
+                c=self.batch_max_len,
+                n=1,
+            )
 
         batches = [
             [

src/axolotl/utils/schemas/datasets.py

@@ -0,0 +1,165 @@
+"""Pydantic models for datasets-related configuration"""
+
+from pydantic import BaseModel, model_validator
+
+from axolotl.utils.schemas.enums import ChatTemplate
+from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
+
+
+class UserDefinedPrompterType(BaseModel):
+    """Structure for user defined prompt types"""
+
+    system_prompt: str | None = None
+    system_format: str | None = None
+    field_system: str | None = None
+    field_instruction: str | None = None
+    field_input: str | None = None
+    field_output: str | None = None
+
+    format: str | None = None
+    no_input_format: str | None = None
+    field: str | None = None
+
+
+class SFTDataset(BaseModel):
+    """SFT configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: str | UserDefinedPrompterType | None = None
+    input_transform: str | None = None
+    shards: int | None = None
+    shards_idx: int | None = None
+    preprocess_shards: int | None = None
+    conversation: str | None = None
+    # Do not make this too strict or it will break the validator to choose different dataset class
+    chat_template: ChatTemplate | str | None = None
+    chat_template_jinja: str | None = None
+    data_files: str | list[str] | None = None
+    input_format: str | None = None
+    name: str | None = None
+    ds_type: str | None = None
+    train_on_split: str | None = None
+    field: str | None = None
+    field_human: str | None = None
+    field_model: str | None = None
+    field_messages: str | None = None
+    # deprecated, use message_property_mappings
+    message_field_role: str | None = None
+    # deprecated, use message_property_mappings
+    message_field_content: str | None = None
+    message_property_mappings: dict[str, str] | None = None
+    message_field_training: str | None = None
+    message_field_training_detail: str | None = None
+    logprobs_field: str | None = None
+    temperature: float | None = None
+    roles_to_train: list[str] | None = None
+    train_on_eos: str | None = None
+    roles: dict[str, list[str]] | None = None
+    drop_system_message: bool | None = None
+    trust_remote_code: bool | None = False
+    revision: str | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def handle_legacy_message_fields(cls, data):
+        """Handle backwards compatibility between legacy message field mapping and new property mapping system."""
+        return handle_legacy_message_fields_logic(data)
+
+    @model_validator(mode="before")
+    @classmethod
+    # pylint: disable=duplicate-code
+    def check_chat_template_config(cls, data):
+        if isinstance(data, BaseModel):
+            data = data.model_dump()
+
+        # Set chat_template to tokenizer_default if not set
+        if data.get("type") == "chat_template" and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.tokenizer_default
+
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
+
+class PretrainingDataset(BaseModel):
+    """Pretraining dataset configuration subset"""
+
+    name: str | None = None
+    path: str | None = None
+    split: str | None = "train"
+    text_column: str | None = "text"
+    type: str | None = "pretrain"
+    trust_remote_code: bool | None = False
+    data_files: str | None = None
+    skip: int | None = None
+
+
+class UserDefinedDPOType(BaseModel):
+    """User defined typing for DPO"""
+
+    field_system: str | None = None
+    field_prompt: str | None = None
+    field_chosen: str | None = None
+    field_rejected: str | None = None
+    prompt_format: str | None = None
+    chosen_format: str | None = None
+    rejected_format: str | None = None
+
+
+class DPODataset(BaseModel):
+    """DPO configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: UserDefinedDPOType | str | None = None
+    data_files: list[str] | None = None
+    revision: str | None = None
+    field_messages: str | None = None
+
+
+class StepwiseSupervisedDataset(BaseModel):
+    """Stepwise supervised dataset configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    data_files: list[str] | None = None
+    revision: str | None = None
+    step_separator: str | None = None
+    max_completion_length: int | None = None
+    train_on_last_step_only: bool | None = None
+
+
+class UserDefinedKTOType(BaseModel):
+    """User defined typing for KTO"""
+
+    field_system: str | None = None
+    field_prompt: str | None = None
+    field_completion: str | None = None
+    field_label: bool | None = None
+    prompt_format: str | None = None
+    completion_format: str | None = None
+
+
+class KTODataset(BaseModel):
+    """KTO configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: UserDefinedKTOType | str | None = None
+    data_files: list[str] | None = None
+    trust_remote_code: bool | None = False
+    revision: str | None = None
+
+
+DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset

src/axolotl/utils/schemas/deprecated.py

@@ -0,0 +1,68 @@
+"""Pydantic models for deprecated and remapped configuration parameters"""
+
+import logging
+from typing import Any
+
+from pydantic import BaseModel, Field, field_validator
+
+LOG = logging.getLogger(__name__)
+
+
+class DeprecatedParameters(BaseModel):
+    """configurations that are deprecated"""
+
+    max_packed_sequence_len: int | None = None
+    rope_scaling: Any | None = None
+    noisy_embedding_alpha: float | None = None
+    dpo_beta: float | None = None
+    evaluation_strategy: str | None = None
+
+    @field_validator("max_packed_sequence_len")
+    @classmethod
+    def validate_max_packed_sequence_len(cls, max_packed_sequence_len):
+        if max_packed_sequence_len:
+            raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
+        return max_packed_sequence_len
+
+    @field_validator("rope_scaling")
+    @classmethod
+    def validate_rope_scaling(cls, rope_scaling):
+        if rope_scaling:
+            raise DeprecationWarning(
+                "`rope_scaling` is no longer supported, it should now be be a key under `model_config`"
+            )
+        return rope_scaling
+
+    @field_validator("noisy_embedding_alpha")
+    @classmethod
+    def validate_noisy_embedding_alpha(cls, noisy_embedding_alpha):
+        if noisy_embedding_alpha:
+            LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
+        return noisy_embedding_alpha
+
+    @field_validator("dpo_beta")
+    @classmethod
+    def validate_dpo_beta(cls, dpo_beta):
+        if dpo_beta is not None:
+            LOG.warning("dpo_beta is deprecated, use rl_beta instead")
+        return dpo_beta
+
+    @field_validator("evaluation_strategy")
+    @classmethod
+    def validate_evaluation_strategy(cls, evaluation_strategy):
+        if evaluation_strategy is not None:
+            LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
+        return evaluation_strategy
+
+
+class RemappedParameters(BaseModel):
+    """Parameters that have been remapped to other names"""
+
+    overrides_of_model_config: dict[str, Any] | None = Field(
+        default=None, alias="model_config"
+    )
+    overrides_of_model_kwargs: dict[str, Any] | None = Field(
+        default=None, alias="model_kwargs"
+    )
+    type_of_model: str | None = Field(default=None, alias="model_type")
+    revision_of_model: str | None = Field(default=None, alias="model_revision")