fix(test): replace jackfram llama with smollm

.github/workflows/base.yml

@@ -40,12 +40,6 @@ 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: "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"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -67,7 +61,7 @@ jobs:
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './docker/Dockerfile-base' }}
+          file: ./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

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

.github/workflows/main.yml

@@ -88,11 +88,6 @@ jobs:
             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:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/nightlies.yml

@@ -80,11 +80,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/precommit-autoupdate.yml

@@ -1,49 +0,0 @@
-name: Pre-commit auto-update
-
-on:
-  schedule:
-    - cron: '0 0 * * 0'  # Run weekly
-  workflow_dispatch:  # Manual kickoff
-
-jobs:
-  auto-update:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Update pre-commit hooks
-        id: update
-        run: |
-          pip install pre-commit
-          pre-commit autoupdate
-          if [[ -n $(git status --porcelain) ]]; then
-            echo "changes=true" >> $GITHUB_OUTPUT
-            git diff .pre-commit-config.yaml > pre-commit-update.diff
-          fi
-
-      - name: Create Pull Request
-        if: steps.update.outputs.changes == 'true'
-        uses: peter-evans/create-pull-request@v6
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          branch: update/pre-commit-hooks
-          delete-branch: true
-          title: "chore: update pre-commit hooks"
-          commit-message: "chore: update pre-commit hooks"
-          body: |
-            Automated PR to update pre-commit hooks to their latest versions.
-
-            <details>
-            <summary>Changes:</summary>
-
-            ```diff
-            ${{ steps.update.outputs.diff }}
-            ```
-            </details>

.github/workflows/pypi.yml

@@ -40,7 +40,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging==23.2
+          pip3 install wheel packaging
           pip3 install --no-build-isolation -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 

.github/workflows/tests-nightly.yml

@@ -42,7 +42,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging setuptools wheel
 
       - name: Install PyTorch
         run: |
@@ -59,7 +59,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2
+          pip3 install --upgrade packaging
           pip3 install --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh

.github/workflows/tests.yml

@@ -74,7 +74,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging setuptools wheel
 
       - name: Install PyTorch
         run: |
@@ -98,9 +98,8 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
           pytest -v tests/patched/
-          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |
@@ -148,7 +147,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel
+          pip3 install --upgrade packaging setuptools setuptools_scm build wheel
 
       - name: Install PyTorch
         run: |
@@ -173,9 +172,8 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ tests/
           pytest -v tests/patched/
-          pytest -v tests/cli/
 
       - name: cleanup pip cache
         run: |

.gitignore

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

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v4.4.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -11,23 +11,23 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/psf/black
-    rev: 25.1.0
+    rev: 23.3.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 6.0.1
+    rev: 5.12.0
     hooks:
       - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 7.1.2
+    rev: 6.1.0
     hooks:
     - id: flake8
--   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.6
+-   repo: https://github.com/PyCQA/pylint
+    rev: v3.3.0
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.3.0
     hooks:
     - id: mypy
       additional_dependencies:
@@ -36,7 +36,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.8.3
+    rev: 1.7.5
     hooks:
     -   id: bandit
         args: [

README.md

@@ -19,6 +19,9 @@
     <br/>
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
+    <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
+    <img alt="phorm.ai" src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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">
+  </a>
 </p>
 
 Axolotl is a tool designed to streamline post-training for various AI models.
@@ -55,7 +58,6 @@ Features:
 ### Installation
 
 ```bash
-pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # Download example axolotl configs, deepspeed configs
@@ -97,7 +99,6 @@ 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

@@ -1,179 +1,6 @@
 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.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"
@@ -205,18 +32,14 @@ website:
           contents:
             - docs/getting-started.qmd
             - docs/installation.qmd
-            - docs/inference.qmd
             - docs/cli.qmd
-            - docs/config.qmd
-            - text: "API Reference"
-              href: docs/api
+            - docs/inference.qmd
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
 
         - section: "Deployments"
           contents:
-            - docs/docker.qmd
             - docs/multi-gpu.qmd
             - docs/multi-node.qmd
             - docs/ray-integration.qmd
@@ -250,27 +73,12 @@ website:
             - docs/debugging.qmd
             - docs/nccl.qmd
 
+        - section: "Reference"
+          contents:
+            - docs/config.qmd
+
 format:
   html:
     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

@@ -31,11 +31,10 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN pip install packaging==23.2 setuptools==75.8.0
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

cicd/cicd.sh

@@ -3,10 +3,9 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli /workspace/axolotl/tests/
+pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ /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 /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/
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

cicd/multigpu.py

@@ -1,7 +1,6 @@
 """
-modal application to run axolotl gpu tests in Modal
-"""
-
+ modal application to run axolotl gpu tests in Modal
+ """
 # pylint: disable=duplicate-code
 
 import os

cicd/tests.py

@@ -1,5 +1,4 @@
 """Modal app to run axolotl GPU tests"""
-
 # pylint: disable=duplicate-code
 
 import os

docker/Dockerfile-base

@@ -28,7 +28,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
+RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/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"

docker/Dockerfile-base-nightly

@@ -1,39 +0,0 @@
-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="nightly"
-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 --extra-index-url https://download.pytorch.org/whl/nightly/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 && \
-    # The base image ships with `pydantic==1.8.2` which is not working
-    pip3 install -U --no-cache-dir pydantic==1.10.10

docker/Dockerfile-cloud

@@ -14,7 +14,7 @@ COPY scripts/motd /etc/motd
 
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
-RUN apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
+RUN apt install --yes --no-install-recommends openssh-server tmux && \
     mkdir -p ~/.ssh && \
     chmod 700 ~/.ssh && \
     printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \

docs/.gitignore

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

docs/cli.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Command Line Interface (CLI)"
+title: "CLI Reference"
 format:
   html:
     toc: true

docs/config.qmd

@@ -1,5 +1,5 @@
 ---
-title: Config Reference
+title: Config options
 description: A complete list of all configuration options.
 ---
 
@@ -30,11 +30,6 @@ tokenizer_legacy:
 # Resize the model embeddings when new tokens are added to multiples of 32
 # This is reported to improve training speed on some models
 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
@@ -88,12 +83,6 @@ gpu_memory_limit: 20GiB
 # Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
 lora_on_cpu: true
 
-# List[str]. Add plugins to extend the pipeline.
-# See `src/axolotl/integrations` for the available plugins or doc below for more details.
-# https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
-plugins:
-  # - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
 # A list of one or more datasets to finetune the model with
 datasets:
   # HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
@@ -165,6 +154,8 @@ datasets:
       content: value
       # ...
 
+    message_property_mappings:
+
     # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
       user: ["human", "user"]
@@ -172,12 +163,6 @@ datasets:
       system: ["system"]
       tool: ["tool"]
 
-    # Optional[bool]. Whether to drop the system turn from the dataset. Only works with chat_template.
-    # This does not drop the default system message from chat_template if it exists. If you wish to,
-    # we recommend using a custom jinja template with the default system message removed or
-    # adding a system turn with empty content.
-    drop_system_message:
-
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
@@ -216,46 +201,10 @@ test_datasets:
     data_files:
       - /workspace/data/eval.jsonl
 
-# use RL training: 'dpo', 'ipo', 'kto', 'simpo', 'orpo', 'grpo'
+# use RL training: 'dpo', 'ipo', 'kto'
 rl:
-rl_beta:  # Optional[float]. The beta parameter for the RL training.
-
-# dpo
-dpo_use_weighting:  # Optional[bool]. Whether to perform weighting.
-rpo_alpha: # Optional[float]. Weighting of NLL term in loss from RPO paper.
-
-# orpo
-orpo_alpha: 0.1  # Parameter controlling the relative ratio loss weight in the ORPO loss. Passed to `beta` in `ORPOConfig` due to trl mapping.
-
-# kto
-kto_desirable_weight: # Optional[float]. Factor for desirable loss term in KTO loss.
-kto_undesirable_weight: # Optional[float]. Factor for undesirable loss term in KTO loss.
-
-# simpo
-cpo_alpha: 1.0  # Weight of the BC regularizer
-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_gpu_memory_utilization: # Optional[float]. GPU memory utilization for VLLM.
-  vllm_max_model_len: # Optional[int]. Maximum length of the model for VLLM.
-  vllm_dtype: # Optional[str]. Data type for VLLM.
-
-  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.
-
-  reward_funcs: # Optional[list[str]]. List of reward functions to load. Paths must be importable from current dir.
-  reward_weights: # Optional[list[float]]. List of reward weights for the reward functions.
-
-  num_generations: # Optional[int]. Number of generations to sample.
-  log_completions: # Optional[bool]. Whether to log completions.
-
-  sync_ref_model: # Optional[bool]. Whether to sync the reference model.
-  ref_model_mixup_alpha: # Optional[float]. Mixup alpha for the reference model.
-  ref_model_sync_steps: # Optional[int]. Sync steps for the reference model.
-
+# whether to perform weighting if doing DPO training. Boolean.
+dpo_use_weighting:
 
 # reward modelling: `True` or `False`
 reward_model:
@@ -273,13 +222,13 @@ process_reward_model:
 chat_template: tokenizer_default
 # custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
 chat_template_jinja: null
-# Changes the default system message. Currently only supports chatml.
-default_system_message: You are a helpful assistant. Please give a long and detailed answer.
+# Changes the default system message
+default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
 # Push prepared dataset to hub
-push_dataset_to_hub: # Optional[str] repo_org/repo_name
+push_dataset_to_hub: # repo path
 # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # if not set.
 dataset_processes: # defaults to os.cpu_count() if not set
@@ -466,7 +415,6 @@ 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.
@@ -497,7 +445,7 @@ gradient_checkpointing: false
 early_stopping_patience: 3
 
 # Specify a scheduler and kwargs to use with the optimizer
-lr_scheduler: # 'one_cycle' | 'rex' | 'log_sweep' | empty for cosine
+lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:
 cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
 cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
@@ -507,58 +455,36 @@ 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/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
+# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
 #
 # 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:
@@ -602,22 +528,12 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 sdp_attention:
 # 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
 resume_from_checkpoint:
 # 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:
 
@@ -632,13 +548,6 @@ special_tokens:
 # Add extra tokens.
 tokens:
 
-# Mapping token_id to new_token_string to override reserved added_tokens in the tokenizer.
-# Only works for tokens that are not part of the base vocab (aka are added_tokens).
-# Can be checked if they exist in tokenizer.json added_tokens.
-added_tokens_overrides:  # Dict[int, str]
-#  128041: "<|im_start|>"
-#  128042: "<|im_end|>"
-
 # FSDP
 fsdp:
 fsdp_config:
@@ -651,14 +560,6 @@ 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:
-
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:
 

docs/custom_integrations.qmd

@@ -55,47 +55,3 @@ sections = [
 for section_name, folder_name in sections:
     print(print_section(section_name, folder_name))
 ```
-
-## Adding a new integration
-
-Plugins can be used to customize the behavior of the training pipeline through [hooks](https://en.wikipedia.org/wiki/Hooking). See [`axolotl.integrations.BasePlugin`](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/integrations/base.py) for the possible hooks.
-
-To add a new integration, please follow these steps:
-
-1. Create a new folder in the `src/axolotl/integrations` directory.
-2. Add any relevant files (`LICENSE`, `README.md`, `ACKNOWLEDGEMENTS.md`, etc.) to the new folder.
-3. Add `__init__.py` and `args.py` files to the new folder.
-  - `__init__.py` should import the integration and hook into the appropriate functions.
-  - `args.py` should define the arguments for the integration.
-4. (If applicable) Add CPU tests under `tests/integrations` or GPU tests under `tests/e2e/integrations`.
-
-::: {.callout-tip}
-
-See [src/axolotl/integrations/cut_cross_entropy](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/cut_cross_entropy) for a minimal integration example.
-
-:::
-
-::: {.callout-warning}
-
-If you could not load your integration, please ensure you are pip installing in editable mode.
-
-```bash
-pip install -e .
-```
-
-and correctly spelled the integration name in the config file.
-
-```yaml
-plugins:
-  - axolotl.integrations.your_integration_name.YourIntegrationPlugin
-```
-
-:::
-
-::: {.callout-note}
-
-It is not necessary to place your integration in the `integrations` folder. It can be in any location, so long as it's installed in a package in your python env.
-
-See this repo for an example: [https://github.com/axolotl-ai-cloud/diff-transformer](https://github.com/axolotl-ai-cloud/diff-transformer)
-
-:::

docs/dataset-formats/conversation.qmd

@@ -74,10 +74,6 @@ datasets:
     train_on_eos:
 ```
 
-::: {.callout-tip}
-If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
-:::
-
 2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
 
 ```yaml

docs/dataset-formats/index.qmd

@@ -129,7 +129,6 @@ You can mix and match within each approach or across approaches to train a model
 We suggest this approach when you want to bring your own tokenized dataset.
 
 Axolotl expects the dataset to have three keys:
-
 - `input_ids`: from tokenizing formatted prompt
 - `attention_mask`: for masking padding. If you don't add padding, it would be equal to `len(input_ids) * [1]`
 - `labels`: this is the same as `input_ids`, however, if you want to mask certain tokens, you would set those indices to `-100`.

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](dataset-formats) and prompt strategies to:
+the [dataset format](docs/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

@@ -1,139 +0,0 @@
----
-title: "Docker"
-format:
-  html:
-    toc: true
-    toc-depth: 4
----
-
-This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
-
-## Base
-
-The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
-
-#### Image
-
-```
-axolotlai/axolotl-base
-```
-
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-base)
-
-#### Tags format
-
-```bash
-main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
-```
-
-Tags examples:
-
-- `main-base-py3.11-cu124-2.6.0`
-- `main-base-py3.11-cu124-2.5.1`
-- `main-base-py3.11-cu124-2.4.1`
-
-## Main
-
-The main image is the image that is used to run Axolotl. It is based on the `axolotlai/axolotl-base` image and includes the Axolotl codebase, dependencies, and more.
-
-#### Image
-
-```
-axolotlai/axolotl
-```
-
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
-
-#### Tags format {#sec-main-tags}
-
-```bash
-# on push to main
-main-py{python_version}-cu{cuda_version}-{pytorch_version}
-
-# latest main (currently torch 2.5.1, python 3.11, cuda 12.4)
-main-latest
-
-# nightly build
-{branch}-{date_in_YYYYMMDD}-py{python_version}-cu{cuda_version}-{pytorch_version}
-
-# tagged release
-{version}
-```
-
-:::{.callout-tip}
-
-There may be some extra tags appended to the image, like `-vllm` which installs those packages.
-
-:::
-
-Tags examples:
-
-- `main-py3.11-cu124-2.6.0`
-- `main-py3.11-cu124-2.5.1`
-- `main-py3.11-cu124-2.4.1`
-- `main-latest`
-- `main-20250303-py3.11-cu124-2.6.0`
-- `main-20250303-py3.11-cu124-2.5.1`
-- `main-20250303-py3.11-cu124-2.4.1`
-- `0.7.1`
-
-## Cloud
-
-The cloud image is the image that is used to run Axolotl in the cloud. It is based on the `axolotlai/axolotl` image and sets ENV variables like HuggingFace cache directories for volume mounts, tmux, and more for different cloud providers.
-
-:::{.callout-tip}
-
-Jupyter lab is run by default. Set `JUPYTER_DISABLE=1` in the environment variables to disable it.
-
-:::
-
-#### Image
-
-```
-axolotlai/axolotl-cloud
-```
-
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud)
-
-#### Tags format
-
-This uses the same tags as the [`main` image](#sec-main-tags).
-
-#### Environment variables
-
-- `JUPYTER_DISABLE`: Disable Jupyter lab.
-- `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
-- `PUBLIC_KEY` / `SSH_KEY`: Add a public key for the SSH service.
-
-#### Volume mounts
-
-:::{.callout-tip}
-
-We recommend mounting volumes to `/workspace/data` for data persistence. `/workspace/axolotl` contains the source code and is ephemeral.
-
-:::
-
-- `/workspace/data/axolotl-artifacts`: Directory to store Axolotl artifacts.
-- `/workspace/data/huggingface-cache`: Directory to store HuggingFace cache.
-
-## Cloud-no-tmux
-
-This is the same as the [`cloud` image](#sec-cloud) but without tmux.
-
-#### Image
-
-```
-axolotlai/axolotl-cloud-term
-```
-
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud-term)
-
-:::{.callout-note}
-
-The naming may be a bit confusing as it has `-term` appended to the end.
-
-:::
-
-#### Tags format
-
-This uses the same tags as the [`cloud` image](#sec-cloud-tags).

docs/faq.qmd

@@ -19,28 +19,12 @@ description: Frequently asked questions
 
 **Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
 
-**Q: ModuleNotFoundError: No module named 'mpi4py' using single GPU with deepspeed**
-
-> A: You may be using deepspeed with single gpu. Please remove the `deepspeed:` section in the yaml file or `--deepspeed` CLI flag.
+> A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
 
 **Q: The codes is stuck on saving preprocessed datasets.**
 
 > A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it.
 
-**Q: Received mismatch error on merge adapters / loading adapters between torch.Size of checkpoint and model.**
-
-> A: This is likely due to vocab size mismatch. By default, Axolotl expands the model's embeddings if the tokenizer has more tokens than the model. Please use the `axolotl merge-lora` command to merge the adapters instead of using your own scripts.
-
-> On the other hand, if the model has more tokens than the tokenizer, Axolotl does not shrink the model's embeddings unless `shrink_embeddings: true` is set in the config.
-
-**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.
-
-**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.
-
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
@@ -66,7 +50,3 @@ description: Frequently asked questions
 **Q: The EOS/EOT token is incorrectly being masked or not being masked.**
 
 > A: This is because of the mismatch between `tokenizer.eos_token` and EOS/EOT token in template. Please make sure to set `eos_token` under `special_tokens` to the same EOS/EOT token as in template.
-
-**Q: "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null. Please add a `chat_template` in tokenizer config"**
-
-> A: This is because the tokenizer does not have a chat template. Please add a chat template in the tokenizer config. See [chat_template](dataset-formats/conversation.qmd#chat-template) for more details.

docs/getting-started.qmd

@@ -36,9 +36,7 @@ The YAML configuration file controls everything about your training. Here's what
 
 ```yaml
 base_model: NousResearch/Llama-3.2-1B
-
-load_in_8bit: true
-adapter: lora
+# hub_model_id: username/custom_model_name
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
@@ -46,15 +44,11 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
 output_dir: ./outputs/lora-out
+
+adapter: lora
+lora_model_dir:
 ```
 
-::: {.callout-tip}
-`load_in_8bit: true` and `adapter: lora` enables LoRA adapter finetuning.
-
-- To perform Full finetuning, remove these two lines.
-- To perform QLoRA finetuning, replace with `load_in_4bit: true` and `adapter: qlora`.
-:::
-
 See our [Config options](config.qmd) for more details.
 
 ### Training {#sec-training}
@@ -62,7 +56,7 @@ See our [Config options](config.qmd) for more details.
 When you run `axolotl train`, Axolotl:
 
 1. Downloads the base model
-2. (If specified) applies QLoRA/LoRA adapter layers
+2. (If specified) applies LoRA adapter layers
 3. Loads and processes the dataset
 4. Runs the training loop
 5. Saves the trained model and / or LoRA weights
@@ -75,8 +69,6 @@ Let's modify the example for your own data:
 
 ```yaml
 base_model: NousResearch/Nous-Hermes-llama-1b-v1
-
-load_in_8bit: true
 adapter: lora
 
 # Training settings
@@ -112,6 +104,8 @@ format):
 {"instruction": "Classify this text", "input": "Not good at all", "output": "negative"}
 ```
 
+Please consult the supported [Dataset Formats](dataset-formats/) for more details.
+
 3. Run the training:
 
 ```bash

docs/inference.qmd

@@ -1,5 +1,5 @@
 ---
-title: "Inference and Merging"
+title: "Inference"
 format:
   html:
     toc: true
@@ -9,14 +9,10 @@ execute:
   enabled: false
 ---
 
-This guide covers how to use your trained models for inference, including model loading, interactive testing, merging adapters, and common troubleshooting steps.
+This guide covers how to use your trained models for inference, including model loading, interactive testing, and common troubleshooting steps.
 
 ## Quick Start {#sec-quickstart}
 
-::: {.callout-tip}
-Use the same config used for training on inference/merging.
-:::
-
 ### Basic Inference {#sec-basic}
 
 ::: {.panel-tabset}

docs/installation.qmd

@@ -22,7 +22,6 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 ### PyPI Installation (Recommended) {#sec-pypi}
 
 ```{.bash}
-pip3 install -U packaging setuptools wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
@@ -38,7 +37,7 @@ For the latest features between releases:
 ```{.bash}
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip3 install -U packaging setuptools wheel ninja
+pip3 install packaging ninja
 pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
@@ -66,8 +65,6 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
 ```
 :::
 
-Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.
-
 ## Cloud Environments {#sec-cloud}
 
 ### Cloud GPU Providers {#sec-cloud-gpu}
@@ -79,7 +76,6 @@ For providers supporting Docker:
   - [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
   - [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
   - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-  - [Novita](https://novita.ai/gpus-console?templateId=311)
 
 ### Google Colab {#sec-colab}
 
@@ -109,7 +105,7 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
 2. Install PyTorch: https://pytorch.org/get-started/locally/
 3. Install Axolotl:
    ```{.bash}
-   pip3 install -U packaging setuptools wheel ninja
+   pip3 install packaging
    pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
    ```
 4. (Optional) Login to Hugging Face:

docs/lora_optims.qmd

@@ -66,10 +66,6 @@ logic to be compatible with more of them.
 
 </details>
 
-::: {.callout-tip}
-Check out our [LoRA optimizations blog](https://axolotlai.substack.com/p/accelerating-lora-fine-tuning-with).
-:::
-
 ## Usage
 
 These optimizations can be enabled in your Axolotl config YAML file. The

docs/multimodal.qmd

@@ -1,171 +1,28 @@
----
-title: MultiModal / Vision Language Models (BETA)
-format:
-  html:
-    toc: true
-    toc-depth: 3
----
+# MultiModal / Vision Language Models (BETA)
 
-## Supported Models
+### Supported 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)
+- Mllama, i.e. llama with vision models
 
-## Usage
+### Usage
 
-Multimodal support is limited and doesn't have full feature parity.
-
-Here are the hyperparams you'll need to use to finetune a multimodal model.
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
 
 ```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
 processor_type: AutoProcessor
-
 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
+chat_template: llama3_2_vision
 datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
     field_messages: messages
+remove_unused_columns: false
+sample_packing: false
 
-# (optional) if doing lora, only finetune the Language model,
-# leave the vision model and vision tower frozen
-# load_in_8bit: true
-adapter: lora
+# only finetune the Language model, leave the vision model and vision tower frozen
 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/reward_modelling.qmd

@@ -28,23 +28,8 @@ val_set_size: 0.1
 eval_steps: 100
 ```
 
-Bradley-Terry chat templates expect single-turn conversations in the following format:
-
-```json
-{
-    "system": "...", // optional
-    "input": "...",
-    "chosen": "...",
-    "rejected": "..."
-}
-```
-
 ### Process Reward Models (PRM)
 
-::: {.callout-tip}
-Check out our [PRM blog](https://axolotlai.substack.com/p/process-reward-models).
-:::
-
 Process reward models are trained using data which contains preference annotations for each step in a series of interactions. Typically, PRMs are trained to provide reward signals over each step of a reasoning trace and are used for downstream reinforcement learning.
 ```yaml
 base_model: Qwen/Qwen2.5-3B
@@ -60,5 +45,3 @@ datasets:
 val_set_size: 0.1
 eval_steps: 100
 ```
-
-Please see [stepwise_supervised](dataset-formats/stepwise_supervised.qmd) for more details on the dataset format.

docs/rlhf.qmd

@@ -3,7 +3,6 @@ title: "RLHF (Beta)"
 description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
 back-to-top-navigation: true
 toc: true
-toc-expand: 2
 toc-depth: 4
 ---
 
@@ -298,7 +297,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### IPO
 
-As IPO is just DPO with a different loss function, all supported dataset formats for [DPO](#dpo) are also supported for IPO.
+As IPO is just DPO with a different loss function, all supported options for DPO works here.
 
 ```yaml
 rl: ipo
@@ -344,9 +343,8 @@ ORPO supports the following types with the following dataset format:
 
 ```yaml
 rl: kto
-rl_beta: 0.1  # default
-kto_desirable_weight: 1.0  # default
-kto_undesirable_weight: 1.0  # default
+rl_beta: 0.5
+kto_desirable_weight: 0.2
 
 remove_unused_columns: false
 
@@ -498,10 +496,6 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### GRPO
 
-::: {.callout-tip}
-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).
-:::
-
 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:
@@ -534,7 +528,6 @@ trl:
     vllm_gpu_memory_utilization: 0.15
     num_generations: 4
     reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
-    reward_weights: [1.0]
 datasets:
   - path: openai/gsm8k
     name: main
@@ -543,21 +536,6 @@ datasets:
 
 To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).
 
-To see description of the configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/models/input/v0_4_1/trl.py).
-
-### SimPO
-
-SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.
-
-```yaml
-rl: simpo
-rl_beta: 0.1  # default in CPOTrainer
-cpo_alpha: 1.0  # default in CPOTrainer
-simpo_gamma: 0.5  # default in CPOTrainer
-```
-
-This method uses the same dataset format as [DPO](#dpo).
-
 ### Using local dataset files
 
 ```yaml

docs/sequence_parallelism.qmd

@@ -1,90 +0,0 @@
----
-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
-```
-
-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
-# Example config with sequence parallelism
-base_model: meta-llama/Llama-3-8B-Instruct
-sequence_len: 8192
-sequence_parallel_degree: 2  # Split each sequence into 4 parts
-flash_attention: true  # Required with sequence parallelism
-...
-```
-
-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

@@ -1,71 +0,0 @@
-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-3-4b-lora.yml

@@ -1,63 +0,0 @@
-base_model: google/gemma-3-4b-it
-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: 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: 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: 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
-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/qlora.yml

@@ -1,74 +0,0 @@
-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
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma3_text
-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/llama-3/qlora-1b-kto.yaml

@@ -55,7 +55,7 @@ tf32: true
 
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
-  use_reentrant: false
+  use_reentrant: true
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:

examples/llava/lora-7b.yaml

@@ -1,63 +0,0 @@
-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

@@ -1,66 +0,0 @@
-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

@@ -1,65 +0,0 @@
-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

@@ -1,63 +0,0 @@
-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:

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -8,7 +8,6 @@ dynamic = ["version", "dependencies", "optional-dependencies"]
 description = "LLM Trainer"
 readme = "README.md"
 requires-python = ">=3.10"
-# license = "Apache-2.0"
 
 [project.scripts]
 axolotl = "axolotl.cli.main:main"

requirements-dev.txt

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

requirements.txt

@@ -1,9 +1,10 @@
 --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==0.45.2
 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
@@ -11,12 +12,12 @@ liger-kernel==0.5.3
 
 packaging==23.2
 
-peft==0.15.0
-transformers==4.50.0
-tokenizers>=0.21.1
-accelerate==1.5.2
-datasets==3.4.1
-deepspeed==0.16.4
+peft==0.14.0
+transformers==4.49.0
+tokenizers>=0.21.0
+accelerate==1.3.0
+datasets==3.2.0
+deepspeed==0.16.1
 trl==0.15.1
 
 optimum==1.16.2
@@ -35,7 +36,6 @@ einops
 colorama
 numba
 numpy>=1.24.4,<=2.0.1
-
 # qlora things
 evaluate==0.4.1
 scipy
@@ -62,5 +62,4 @@ antlr4-python3-runtime==4.13.2
 torchao==0.7.0
 schedulefree==1.3.0
 
-axolotl-contribs-lgpl==0.0.6
-axolotl-contribs-mit==0.0.3
+axolotl-contribs-lgpl==0.0.3

requirements_env.txt

@@ -0,0 +1,315 @@
+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

scripts/chat_datasets.py

@@ -1,7 +1,6 @@
 """
 helper script to parse chat datasets into a usable yaml
 """
-
 import click
 import yaml
 from datasets import load_dataset

scripts/cutcrossentropy_install.py

@@ -1,5 +1,4 @@
 """Script to output the correct installation command for cut-cross-entropy."""
-
 import importlib.util
 import sys
 
@@ -25,5 +24,5 @@ if cce_spec:
 
 print(
     UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"'
+    + 'pip install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
 )

setup.py

@@ -16,7 +16,13 @@ def parse_requirements():
     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 = "deepspeed" in line or "mamba-ssm" in line
+            is_extras = (
+                "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()
@@ -33,6 +39,7 @@ def parse_requirements():
                 "bitsandbytes",
                 "triton",
                 "mamba-ssm",
+                "flash-attn",
                 "xformers",
                 "autoawq",
                 "liger-kernel",
@@ -117,10 +124,11 @@ setup(
         ],
     },
     extras_require={
-        "flash-attn": ["flash-attn==2.7.4.post1"],
-        "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
+        "flash-attn": [
+            "flash-attn==2.7.4.post1",
+        ],
         "deepspeed": [
-            "deepspeed==0.16.4",
+            "deepspeed==0.16.1",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [
@@ -133,15 +141,15 @@ setup(
         "mlflow": [
             "mlflow",
         ],
+        "lion-pytorch": [
+            "lion-pytorch==0.1.2",
+        ],
         "galore": [
             "galore_torch",
         ],
-        "apollo": [
-            "apollo-torch",
-        ],
         "optimizers": [
             "galore_torch",
-            "apollo-torch",
+            "lion-pytorch==0.1.2",
             "lomo-optim==0.1.1",
             "torch-optimi==0.2.1",
         ],

src/axolotl/cli/cloud/__init__.py

@@ -1,7 +1,6 @@
 """
 launch axolotl in supported cloud platforms
 """
-
 from pathlib import Path
 from typing import Union
 

src/axolotl/cli/cloud/base.py

@@ -1,7 +1,6 @@
 """
 base class for cloud platforms from cli
 """
-
 from abc import ABC, abstractmethod
 
 

src/axolotl/cli/cloud/modal_.py

@@ -1,7 +1,6 @@
 """
 Modal Cloud support from CLI
 """
-
 import copy
 import json
 import os
@@ -114,7 +113,7 @@ class ModalCloud(Cloud):
                 [
                     # Random id for cache busting of branch commits
                     f"RUN echo '{str(randint(0, 1000000))}'",  # nosec B311
-                    f"RUN cd /workspace/axolotl && git fetch && git checkout {self.config.branch} && git pull",
+                    f"RUN cd /workspace/axolotl && git fetch && git checkout {self.config.branch}",
                 ]
             )
 
@@ -271,7 +270,6 @@ def _preprocess(config_yaml: str, volumes=None):
 
 
 def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
-    Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
         f_out.write(config_yaml)
     run_folder = "/workspace/mounts"
@@ -290,7 +288,6 @@ def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
 
 
 def _lm_eval(config_yaml: str, volumes=None):
-    Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
         f_out.write(config_yaml)
     run_folder = "/workspace/mounts"

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

src/axolotl/cli/main.py

@@ -1,5 +1,4 @@
 """Click CLI definitions for various axolotl commands."""
-
 # pylint: disable=redefined-outer-name
 
 import logging
@@ -25,7 +24,7 @@ from axolotl.cli.utils import (
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
-from axolotl.utils.schemas.config import AxolotlInputConfig
+from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 
 
 @click.group()

src/axolotl/cli/merge_lora.py

@@ -27,7 +27,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     print_axolotl_text_art()
 
-    model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg)
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
@@ -44,9 +44,6 @@ 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:
     """

src/axolotl/cli/train.py

@@ -1,7 +1,6 @@
 """CLI to run training on a model."""
 
 import logging
-import os
 from pathlib import Path
 from typing import Union
 
@@ -17,14 +16,13 @@ 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):
+def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     """
     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
@@ -34,27 +32,25 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
         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:
-        check_user_token()
+    check_user_token()
 
     if cfg.rl:
         dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
-    del model, tokenizer, trainer
-
+    model, tokenizer = train(cfg=cfg, dataset_meta=dataset_meta)
     plugin_manager = PluginManager.get_instance()
+
+    del model
+    del tokenizer
+
     plugin_manager.post_train_unload(cfg)
 
 
-def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
+def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
     """
     Parses `axolotl` config, CLI args, and calls `do_train`.
 

src/axolotl/cli/utils.py

@@ -5,6 +5,7 @@ import dataclasses
 import hashlib
 import json
 import logging
+import typing
 from functools import wraps
 from pathlib import Path
 from types import NoneType
@@ -13,22 +14,17 @@ 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,
-    ProcessorMixin,
-)
+from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.models import load_model, load_tokenizer
 
 configure_logging()
 LOG = logging.getLogger(__name__)
 
 
-def strip_optional_type(field_type: type | str | None):
+def strip_optional_type(field_type: type | typing._SpecialForm | None):
     """
     Extracts the non-`None` type from an `Optional` / `Union` type.
 
@@ -300,13 +296,9 @@ def load_model_and_tokenizer(
     *,
     cfg: DictDefault,
     inference: bool = False,
-) -> tuple[
-    PreTrainedModel,
-    PreTrainedTokenizer | PreTrainedTokenizerFast | Any,
-    ProcessorMixin | None,
-]:
+) -> tuple[PreTrainedModel, PreTrainedTokenizer | PreTrainedTokenizerFast | Any]:
     """
-    Helper function for loading a model, tokenizer, and processor specified in the given `axolotl`
+    Helper function for loading a model and tokenizer specified in the given `axolotl`
     config.
 
     Args:
@@ -314,7 +306,7 @@ def load_model_and_tokenizer(
         inference: Boolean denoting inference mode.
 
     Returns:
-        Tuple of (PreTrainedModel, PreTrainedTokenizer, ProcessorMixin).
+        `transformers` model and tokenizer.
     """
     LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
     tokenizer = load_tokenizer(cfg)
@@ -322,9 +314,4 @@ def load_model_and_tokenizer(
     LOG.info("loading model...")
     model, _ = load_model(cfg, tokenizer, inference=inference)
 
-    processor = None
-    if cfg.is_multimodal:
-        LOG.info("loading processor...")
-        processor = load_processor(cfg, tokenizer)
-
-    return model, tokenizer, processor
+    return model, tokenizer

src/axolotl/common/datasets.py

@@ -24,8 +24,8 @@ class TrainDatasetMeta:
     """Dataclass with fields for training and validation datasets and metadata."""
 
     train_dataset: Dataset
-    eval_dataset: Dataset | None = None
-    total_num_steps: int | None = None
+    eval_dataset: Optional[Dataset] = None
+    total_num_steps: Optional[int] = None
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:

src/axolotl/convert.py

@@ -1,5 +1,6 @@
 """Module containing File Reader, File Writer, Json Parser, and Jsonl Serializer classes"""
 
+
 import json
 import sys
 

src/axolotl/core/chat/format/chatml.py

@@ -1,7 +1,6 @@
 """
 ChatML transformation functions for MessageContents
 """
-
 from typing import Optional
 
 from ..messages import MessageContents, Messages

src/axolotl/core/chat/format/llama3x.py

@@ -1,7 +1,6 @@
 """
 Llama 3.x chat formatting functions for MessageContents
 """
-
 from typing import Optional
 
 from ..messages import MessageContents, Messages

src/axolotl/core/chat/format/shared.py

@@ -1,7 +1,6 @@
 """
 shared functions for format transforms
 """
-
 from axolotl.core.chat.messages import MessageContents, Messages
 
 

src/axolotl/core/chat/messages.py

@@ -1,7 +1,6 @@
 """
 internal message representations of chat messages
 """
-
 import json
 from enum import Enum
 from typing import Any, Callable, List, Optional, Union

src/axolotl/core/datasets/chat.py

@@ -1,7 +1,6 @@
 """
 chat dataset module
 """
-
 import os
 from typing import Callable, Optional, Union
 
@@ -44,7 +43,7 @@ class TokenizedChatDataset(Dataset):
         process_or_cpu_count: int = (
             process_count or os.cpu_count()  # type: ignore[assignment]
         )
-        num_proc = min(32, process_or_cpu_count)
+        num_proc = min(64, process_or_cpu_count)
         features = data.features.keys()
         tokenized_data = data.map(
             map_fn,

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -1,7 +1,6 @@
 """
 This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
 """
-
 from typing import Any, Mapping, Union
 
 

src/axolotl/core/trainer_builder.py

@@ -13,7 +13,9 @@
 # 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
@@ -33,10 +35,9 @@ from transformers import (
     EarlyStoppingCallback,
     TrainerCallback,
 )
-from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
-from axolotl.core.trainers import (
+from axolotl.core.trainers.base import (
     AxolotlCPOTrainer,
     AxolotlKTOTrainer,
     AxolotlMambaTrainer,
@@ -60,7 +61,6 @@ 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,
@@ -85,18 +85,19 @@ from axolotl.utils.collators import (
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
 except ImportError:
     pass
 
-LOG = logging.getLogger(__name__)
+LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
 class TrainerBuilderBase(abc.ABC):
-    """Base class for trainer builder."""
+    """
+    Base class for trainer builder
+    """
 
     _train_dataset = None
     _eval_dataset = None
@@ -109,9 +110,9 @@ class TrainerBuilderBase(abc.ABC):
         self.tokenizer = tokenizer
         self.processor = processor
 
-        # If the model supports tagging, add the axolotl tag.
+        # in case the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
-        # model.push_to_hub instead of trainer.push_to_hub.
+        # model.push_to_hub instad of  trainer.push_to_hub.
         if hasattr(model, "add_model_tags"):
             model.add_model_tags(["axolotl"])
 
@@ -226,8 +227,8 @@ class TrainerBuilderBase(abc.ABC):
 
 class HFCausalTrainerBuilder(TrainerBuilderBase):
     """
-    Build the HuggingFace training args/trainer for causal models and reward modeling
-    using TRL.
+    Build the HuggingFace training args/trainer for causal models
+    and reward modelling using TRL.
     """
 
     def get_callbacks(self):
@@ -331,9 +332,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs = {}
 
         if self.cfg.include_tokens_per_second is not None:
-            training_arguments_kwargs["include_tokens_per_second"] = (
-                self.cfg.include_tokens_per_second
-            )
+            training_arguments_kwargs[
+                "include_tokens_per_second"
+            ] = self.cfg.include_tokens_per_second
 
         if self.cfg.bf16 == "full":
             training_arguments_kwargs["bf16_full_eval"] = True
@@ -350,13 +351,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["seed"] = self.cfg.seed
 
         if self.cfg.gradient_checkpointing:
-            training_arguments_kwargs["gradient_checkpointing"] = (
-                self.cfg.gradient_checkpointing
-            )
+            training_arguments_kwargs[
+                "gradient_checkpointing"
+            ] = self.cfg.gradient_checkpointing
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_arguments_kwargs["gradient_checkpointing_kwargs"] = (
-                    self.cfg.gradient_checkpointing_kwargs
-                )
+                training_arguments_kwargs[
+                    "gradient_checkpointing_kwargs"
+                ] = self.cfg.gradient_checkpointing_kwargs
         if self.cfg.fsdp:
             training_arguments_kwargs["fsdp"] = self.cfg.fsdp
             if self.cfg.fsdp_config:
@@ -372,9 +373,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["deepspeed"] = self.cfg.deepspeed
 
         if self.cfg.lr_quadratic_warmup is not None:
-            training_arguments_kwargs["lr_quadratic_warmup"] = (
-                self.cfg.lr_quadratic_warmup
-            )
+            training_arguments_kwargs[
+                "lr_quadratic_warmup"
+            ] = self.cfg.lr_quadratic_warmup
 
         if self.cfg.adam_beta1:
             training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
@@ -398,28 +399,28 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_arguments_kwargs["dataloader_pin_memory"] = (
-                self.cfg.dataloader_pin_memory
-            )
+            training_arguments_kwargs[
+                "dataloader_pin_memory"
+            ] = self.cfg.dataloader_pin_memory
         if self.cfg.dataloader_num_workers is not None:
-            training_arguments_kwargs["dataloader_num_workers"] = (
-                self.cfg.dataloader_num_workers
-            )
+            training_arguments_kwargs[
+                "dataloader_num_workers"
+            ] = self.cfg.dataloader_num_workers
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_arguments_kwargs["dataloader_prefetch_factor"] = (
-                self.cfg.dataloader_prefetch_factor
-            )
+            training_arguments_kwargs[
+                "dataloader_prefetch_factor"
+            ] = self.cfg.dataloader_prefetch_factor
         if self.cfg.dataloader_drop_last is not None:
-            training_arguments_kwargs["dataloader_drop_last"] = (
-                self.cfg.dataloader_drop_last
-            )
+            training_arguments_kwargs[
+                "dataloader_drop_last"
+            ] = self.cfg.dataloader_drop_last
         elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
             training_arguments_kwargs["dataloader_drop_last"] = True
 
         if self.cfg.remove_unused_columns is not None:
-            training_arguments_kwargs["remove_unused_columns"] = (
-                self.cfg.remove_unused_columns
-            )
+            training_arguments_kwargs[
+                "remove_unused_columns"
+            ] = self.cfg.remove_unused_columns
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
@@ -451,9 +452,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.do_causal_lm_eval:
             training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
         if self.cfg.metric_for_best_model:
-            training_arguments_kwargs["metric_for_best_model"] = (
-                self.cfg.metric_for_best_model
-            )
+            training_arguments_kwargs[
+                "metric_for_best_model"
+            ] = self.cfg.metric_for_best_model
         if self.cfg.greater_is_better:
             training_arguments_kwargs["greater_is_better"] = self.cfg.greater_is_better
 
@@ -466,13 +467,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 )
                 training_arguments_kwargs["torch_compile"] = self.cfg.torch_compile
                 if self.cfg.torch_compile_backend:
-                    training_arguments_kwargs["torch_compile_backend"] = (
-                        self.cfg.torch_compile_backend
-                    )
+                    training_arguments_kwargs[
+                        "torch_compile_backend"
+                    ] = self.cfg.torch_compile_backend
                 if self.cfg.torch_compile_mode:
-                    training_arguments_kwargs["torch_compile_mode"] = (
-                        self.cfg.torch_compile_mode
-                    )
+                    training_arguments_kwargs[
+                        "torch_compile_mode"
+                    ] = self.cfg.torch_compile_mode
 
         # DDP Config
         if self.cfg.ddp_timeout:
@@ -481,32 +482,32 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.ddp_bucket_cap_mb:
             training_arguments_kwargs["ddp_bucket_cap_mb"] = self.cfg.ddp_bucket_cap_mb
         if self.cfg.ddp_broadcast_buffers is not None:
-            training_arguments_kwargs["ddp_broadcast_buffers"] = (
-                self.cfg.ddp_broadcast_buffers
-            )
+            training_arguments_kwargs[
+                "ddp_broadcast_buffers"
+            ] = self.cfg.ddp_broadcast_buffers
 
         # these are all the "standard" kwargs that are def used
         training_arguments_kwargs["max_steps"] = (
             total_num_steps if self.cfg.max_steps else -1
         )
         training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
-        training_arguments_kwargs["per_device_train_batch_size"] = (
-            self.cfg.micro_batch_size
-        )
+        training_arguments_kwargs[
+            "per_device_train_batch_size"
+        ] = self.cfg.micro_batch_size
         if self.cfg.eval_batch_size:
-            training_arguments_kwargs["per_device_eval_batch_size"] = (
-                self.cfg.eval_batch_size
-            )
+            training_arguments_kwargs[
+                "per_device_eval_batch_size"
+            ] = self.cfg.eval_batch_size
         if self.cfg.auto_find_batch_size is not None:
-            training_arguments_kwargs["auto_find_batch_size"] = (
-                self.cfg.auto_find_batch_size
-            )
-        training_arguments_kwargs["gradient_accumulation_steps"] = (
-            self.cfg.gradient_accumulation_steps
-        )
-        training_arguments_kwargs["eval_accumulation_steps"] = (
-            self.cfg.gradient_accumulation_steps
-        )
+            training_arguments_kwargs[
+                "auto_find_batch_size"
+            ] = self.cfg.auto_find_batch_size
+        training_arguments_kwargs[
+            "gradient_accumulation_steps"
+        ] = self.cfg.gradient_accumulation_steps
+        training_arguments_kwargs[
+            "eval_accumulation_steps"
+        ] = self.cfg.gradient_accumulation_steps
         training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
         training_arguments_kwargs["learning_rate"] = self.cfg.learning_rate
         training_arguments_kwargs["output_dir"] = self.cfg.output_dir
@@ -550,12 +551,34 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
         else:
             training_arguments_kwargs["run_name"] = None
+        training_arguments_kwargs["optim"] = (
+            self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
+        )
+        if self.cfg.optim_args:
+            if isinstance(self.cfg.optim_args, dict):
+                optim_args = ",".join(
+                    [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                )
+            else:
+                optim_args = self.cfg.optim_args
+            training_arguments_kwargs["optim_args"] = optim_args
+        if self.cfg.optim_target_modules:
+            training_arguments_kwargs[
+                "optim_target_modules"
+            ] = self.cfg.optim_target_modules
+        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
+        training_arguments_kwargs[
+            "loraplus_lr_embedding"
+        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
+        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
-        if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
+        if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
-            training_arguments_kwargs["alternate_lr_scheduler_type"] = (
-                self.cfg.lr_scheduler
-            )
+            training_arguments_kwargs[
+                "alternate_lr_scheduler_type"
+            ] = self.cfg.lr_scheduler
         else:
             training_arguments_kwargs["lr_scheduler_type"] = (
                 self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
@@ -564,9 +587,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
-        training_arguments_kwargs["cosine_constant_lr_ratio"] = (
-            self.cfg.cosine_constant_lr_ratio
-        )
+        training_arguments_kwargs[
+            "cosine_constant_lr_ratio"
+        ] = self.cfg.cosine_constant_lr_ratio
         training_arguments_kwargs["weight_decay"] = (
             self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
         )
@@ -579,40 +602,40 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.eval_sample_packing
         )
         if self.cfg.sample_packing_bin_size is not None:
-            training_arguments_kwargs["sample_packing_bin_size"] = (
-                self.cfg.sample_packing_bin_size
-            )
+            training_arguments_kwargs[
+                "sample_packing_bin_size"
+            ] = self.cfg.sample_packing_bin_size
         if self.cfg.sample_packing_group_size is not None:
-            training_arguments_kwargs["sample_packing_group_size"] = (
-                self.cfg.sample_packing_group_size
-            )
+            training_arguments_kwargs[
+                "sample_packing_group_size"
+            ] = self.cfg.sample_packing_group_size
         if self.cfg.sample_packing_eff_est:
-            training_arguments_kwargs["sample_packing_efficiency"] = (
-                self.cfg.sample_packing_eff_est
-            )
+            training_arguments_kwargs[
+                "sample_packing_efficiency"
+            ] = self.cfg.sample_packing_eff_est
 
         if self.cfg.relora_steps:
             training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
-            training_arguments_kwargs["relora_warmup_steps"] = (
-                self.cfg.relora_warmup_steps
-            )
+            training_arguments_kwargs[
+                "relora_warmup_steps"
+            ] = self.cfg.relora_warmup_steps
             if self.cfg.relora_anneal_steps:
-                training_arguments_kwargs["relora_anneal_steps"] = (
-                    self.cfg.relora_anneal_steps
-                )
+                training_arguments_kwargs[
+                    "relora_anneal_steps"
+                ] = self.cfg.relora_anneal_steps
             if self.cfg.relora_prune_ratio:
-                training_arguments_kwargs["relora_prune_ratio"] = (
-                    self.cfg.relora_prune_ratio
-                )
+                training_arguments_kwargs[
+                    "relora_prune_ratio"
+                ] = self.cfg.relora_prune_ratio
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             training_arguments_kwargs["lisa_n_layers"] = self.cfg.lisa_n_layers
-            training_arguments_kwargs["lisa_step_interval"] = (
-                self.cfg.lisa_step_interval
-            )
-            training_arguments_kwargs["lisa_layers_attribute"] = (
-                self.cfg.lisa_layers_attribute
-            )
+            training_arguments_kwargs[
+                "lisa_step_interval"
+            ] = self.cfg.lisa_step_interval
+            training_arguments_kwargs[
+                "lisa_layers_attribute"
+            ] = self.cfg.lisa_layers_attribute
 
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
@@ -626,134 +649,60 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             )
 
         if self.cfg.neftune_noise_alpha is not None:
-            training_arguments_kwargs["neftune_noise_alpha"] = (
-                self.cfg.neftune_noise_alpha
-            )
+            training_arguments_kwargs[
+                "neftune_noise_alpha"
+            ] = self.cfg.neftune_noise_alpha
 
         trainer_kwargs = {}
 
         if self.cfg.reward_model:
             training_arguments_kwargs["max_length"] = self.cfg.sequence_len
 
-        # Handle custom optimizer
-        custom_supported_optimizers = [opt.value for opt in CustomSupportedOptimizers]
-        if self.cfg.optimizer in custom_supported_optimizers:
-            # Common optimizer kwargs
-            optimizer_kwargs = {
-                "lr": training_arguments_kwargs.get("learning_rate"),
-                "weight_decay": training_arguments_kwargs.get("weight_decay"),
-            }
+        # pylint: disable=duplicate-code
+        if self.cfg.optimizer in [
+            "optimi_adamw",
+            "ao_adamw_4bit",
+            "ao_adamw_8bit",
+            "ao_adamw_fp8",
+            "adopt_adamw",
+        ]:
+            # Set default so transformers doesn't throw
+            training_arguments_kwargs["optim"] = "adamw_hf"
+            training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer
 
-            # Adam-specific kwargs
-            adam_kwargs = {}
-            if training_arguments_kwargs.get(
-                "adam_beta1"
-            ) and training_arguments_kwargs.get("adam_beta2"):
-                adam_kwargs["betas"] = (
-                    training_arguments_kwargs.get("adam_beta1"),
-                    training_arguments_kwargs.get("adam_beta2"),
-                )
-            if training_arguments_kwargs.get("adam_epsilon"):
-                adam_kwargs["eps"] = training_arguments_kwargs.get("adam_epsilon")
+        if self.cfg.optimizer == "lion_pytorch":
+            from lion_pytorch import Lion
 
-            if self.cfg.optimizer == "muon":
-                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
-                    MuonOptimizerFactory,
+            lion_kwargs = {"lr": training_arguments_kwargs["learning_rate"]}
+            if "weight_decay" in training_arguments_kwargs:
+                lion_kwargs["weight_decay"] = training_arguments_kwargs["weight_decay"]
+
+            if (
+                "adam_beta1" in training_arguments_kwargs
+                and "adam_beta2" in training_arguments_kwargs
+            ):
+                lion_kwargs["betas"] = (
+                    training_arguments_kwargs["adam_beta1"],
+                    training_arguments_kwargs["adam_beta2"],
                 )
 
-                optimizer_cls = MuonOptimizerFactory
-                optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer == "optimi_adamw":
-                from optimi import AdamW
-
-                optimizer_kwargs["foreach"] = False
-                optimizer_cls = AdamW
-                optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer == "ao_adamw_4bit":
-                # TODO remove 20250401
-                from torchao.prototype.low_bit_optim import AdamW4bit
-
-                optimizer_cls = AdamW4bit
-                optimizer_kwargs.update(adam_kwargs)
-
-                LOG.warning(
-                    f"`ao_adamw_4bit` will be deprecated soon. Please use `{OptimizerNames.ADAMW_TORCH_4BIT}` instead."
-                )
-            elif self.cfg.optimizer == "ao_adamw_8bit":
-                from torchao.prototype.low_bit_optim import AdamW8bit
-
-                optimizer_cls = AdamW8bit
-                optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer == "ao_adamw_fp8":
-                from torchao.prototype.low_bit_optim import AdamWFp8
-
-                optimizer_cls = AdamWFp8
-                optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
-
-                optimizer_cls = ADOPT
-                adam_kwargs["decouple"] = True
-                optimizer_kwargs.update(adam_kwargs)
-
-            # Parse any additional optimizer args from config
-            if self.cfg.optim_args:
-                if isinstance(self.cfg.optim_args, dict):
-                    optimizer_kwargs.update(self.cfg.optim_args)
-                else:
-                    # Parse string format "key1=value1,key2=value2"
-                    for mapping in self.cfg.optim_args.replace(" ", "").split(","):
-                        key, value = mapping.split("=")
-                        optimizer_kwargs[key] = value
-
-            trainer_kwargs["optimizer_cls_and_kwargs"] = (
-                optimizer_cls,
-                optimizer_kwargs,
+            trainer_kwargs["optimizers"] = (
+                Lion(params=self.model.parameters(), **lion_kwargs),
+                None,
             )
-        else:
-            # Use transformers' optimizer
-            training_arguments_kwargs["optim"] = self.cfg.optimizer
-
-            # Parse any additional optimizer args from config
-            if self.cfg.optim_args:
-                if isinstance(self.cfg.optim_args, dict):
-                    optim_args = ",".join(
-                        [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
-                    )
-                else:
-                    optim_args = self.cfg.optim_args
-                training_arguments_kwargs["optim_args"] = optim_args
+            # Set default so transformers doesn't throw
+            training_arguments_kwargs["optim"] = "adamw_hf"
 
         if self.cfg.optimizer == "adamw_anyprecision":
             if Path(self.cfg.torchdistx_path).exists():
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
-        if self.cfg.optim_target_modules:
-            training_arguments_kwargs["optim_target_modules"] = (
-                self.cfg.optim_target_modules
-            )
-
-        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
-        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
-
-        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs["loraplus_lr_embedding"] = (
-            self.cfg.loraplus_lr_embedding
-        )
-        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
-
         if self.cfg.accelerator_config:
-            training_arguments_kwargs["accelerator_config"] = (
-                self.cfg.accelerator_config
-            )
+            training_arguments_kwargs[
+                "accelerator_config"
+            ] = 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:
@@ -761,17 +710,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.kd_temperature is not None:
             training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
         if self.cfg.kd_zscore_base_temp is not None:
-            training_arguments_kwargs["kd_zscore_base_temp"] = (
-                self.cfg.kd_zscore_base_temp
-            )
+            training_arguments_kwargs[
+                "kd_zscore_base_temp"
+            ] = self.cfg.kd_zscore_base_temp
         if self.cfg.kd_top_k_before_softmax is not None:
-            training_arguments_kwargs["kd_top_k_before_softmax"] = (
-                self.cfg.kd_top_k_before_softmax
-            )
-
-        training_arguments_kwargs["sequence_parallel_degree"] = (
-            self.cfg.sequence_parallel_degree
-        )
+            training_arguments_kwargs[
+                "kd_top_k_before_softmax"
+            ] = self.cfg.kd_top_k_before_softmax
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -856,10 +801,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
     ):
         if training_args.pretraining:
-            if (
-                self.cfg.pretraining_sample_concatenation is False
-                or self.cfg.micro_batch_size > 1
-            ):
+            if self.cfg.pretraining_sample_concatenation is False:
+                return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
+            if self.cfg.micro_batch_size > 1:
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
             return None
 
@@ -887,7 +831,9 @@ 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 or (
+            if self.cfg.model_config_type in SUPPORTED_MULTIPACK_MODEL_TYPES:
+                collator = V2BatchSamplerDataCollatorForSeq2Seq
+            elif (
                 self.cfg.model_config_type in ["llama"]
                 and self.cfg.flash_attention is not True
             ):
@@ -897,13 +843,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         else:
             if self.cfg.processor_type and self.processor:
                 collator = MultiModalChatDataCollator
-                kwargs["processing_strategy"] = get_processing_strategy(
-                    self.processor,
-                    training_args.chat_template,
-                    self.cfg.chat_template,
-                    image_size=training_args.image_size,
-                    image_resize_algorithm=training_args.image_resize_algorithm,
-                )
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
             elif self.cfg.batch_flattening:
                 collator = DataCollatorWithFlattening
                 collator_args.pop(0)
@@ -923,8 +864,6 @@ 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,
@@ -933,7 +872,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
 
 class HFRLTrainerBuilder(TrainerBuilderBase):
-    """Trainer factory class for TRL-based RLHF trainers (e.g. DPO)"""
+    """
+    Trainer factory class for TRL-based RLHF trainers (e.g. DPO)
+    """
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
@@ -987,32 +928,32 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         if self.cfg.remove_unused_columns is not None:
-            training_args_kwargs["remove_unused_columns"] = (
-                self.cfg.remove_unused_columns
-            )
+            training_args_kwargs[
+                "remove_unused_columns"
+            ] = self.cfg.remove_unused_columns
         else:
             training_args_kwargs["remove_unused_columns"] = False
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_args_kwargs["dataloader_pin_memory"] = (
-                self.cfg.dataloader_pin_memory
-            )
+            training_args_kwargs[
+                "dataloader_pin_memory"
+            ] = self.cfg.dataloader_pin_memory
         if self.cfg.dataloader_num_workers is not None:
-            training_args_kwargs["dataloader_num_workers"] = (
-                self.cfg.dataloader_num_workers
-            )
+            training_args_kwargs[
+                "dataloader_num_workers"
+            ] = self.cfg.dataloader_num_workers
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_args_kwargs["dataloader_prefetch_factor"] = (
-                self.cfg.dataloader_prefetch_factor
-            )
+            training_args_kwargs[
+                "dataloader_prefetch_factor"
+            ] = self.cfg.dataloader_prefetch_factor
         if self.cfg.gradient_checkpointing:
-            training_args_kwargs["gradient_checkpointing"] = (
-                self.cfg.gradient_checkpointing
-            )
+            training_args_kwargs[
+                "gradient_checkpointing"
+            ] = self.cfg.gradient_checkpointing
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_args_kwargs["gradient_checkpointing_kwargs"] = (
-                    self.cfg.gradient_checkpointing_kwargs
-                )
+                training_args_kwargs[
+                    "gradient_checkpointing_kwargs"
+                ] = self.cfg.gradient_checkpointing_kwargs
             else:
                 training_args_kwargs["gradient_checkpointing_kwargs"] = {
                     "use_reentrant": False
@@ -1086,9 +1027,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.dpo_use_weighting is not None:
                 training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
             if self.cfg.dpo_use_logits_to_keep is not None:
-                training_args_kwargs["use_logits_to_keep"] = (
-                    self.cfg.dpo_use_logits_to_keep
-                )
+                training_args_kwargs[
+                    "use_logits_to_keep"
+                ] = self.cfg.dpo_use_logits_to_keep
 
         for blocklist_key in blocklist_args_kwargs:
             if blocklist_key in training_args_kwargs:
@@ -1123,9 +1064,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.adapter and self.peft_config:
             dpo_trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs["precompute_ref_log_probs"] = (
-                self.cfg.precompute_ref_log_probs
-            )
+            dpo_trainer_kwargs[
+                "precompute_ref_log_probs"
+            ] = self.cfg.precompute_ref_log_probs
         if self.cfg.rl == "grpo":
             trainer_cls = GRPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model]

src/axolotl/core/trainers/__init__.py

@@ -1,18 +0,0 @@
-"""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

@@ -1,47 +1,163 @@
-"""Module for customized trainers"""
-
-# pylint: disable=too-many-lines
+"""
+module for customized trainers
+"""
 
 from __future__ import annotations
 
+# pylint: disable=too-many-lines
 import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Any, Literal
+from typing import Dict, Literal, Optional
 
-import datasets
 import torch
 from datasets import Dataset
-from torch import nn
-from torch.utils.data import (
-    BatchSampler,
-    DataLoader,
-    RandomSampler,
-    Sampler,
-    SequentialSampler,
-)
+from peft.optimizers import create_loraplus_optimizer
+from torch.optim.lr_scheduler import OneCycleLR
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, 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.core.trainers.mixins import (
-    OptimizerMixin,
-    SchedulerMixin,
-    SequenceParallelMixin,
-)
-from axolotl.core.trainers.utils import (
-    sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
-)
+from axolotl.monkeypatch.relora import ReLoRAScheduler
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
+from axolotl.utils.schedulers import (
+    get_cosine_schedule_with_min_lr,
+    get_cosine_schedule_with_quadratic_warmup,
+    get_cosine_schedule_with_warmup_decay_constant,
+)
 
-LOG = logging.getLogger(__name__)
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
-class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trainer):
-    """Extend the base Trainer for axolotl helpers"""
+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
+
+
+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 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 AxolotlTrainer(SchedulerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
@@ -58,18 +174,12 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         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
@@ -82,247 +192,316 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
             )
         return super()._wrap_model(model, training=training, dataloader=dataloader)
 
-    def _create_multipack_sampler(
-        self, base_sampler: Sampler, dataset: Dataset
-    ) -> MultipackBatchSampler:
-        """
-        Helper method to create a `MultipackBatchSampler` for multipacking sequences
-        for training.
-
-        Args:
-            base_sampler: Sampler to wrap with `MultipackBatchSampler`.
-            dataset: Dataset to sample from.
-
-        Returns:
-            Multipack (sample packing) batch sampler.
-        """
-        if self.args.multipack_real_batches:
-            batch_size = self.args.per_device_train_batch_size
-            batch_max_len = self.args.max_seq_length
-        else:
-            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
-
-        return MultipackBatchSampler(
-            base_sampler,
-            lengths=get_dataset_lengths(dataset),
-            packing_efficiency_estimate=self.args.sample_packing_efficiency,
-            batch_max_len=batch_max_len,
-            batch_size=batch_size,
-            drop_last=True,
-        )
-
-    def _get_train_sampler(self) -> Sampler | None:
-        """
-        Helper method to get the sampler for training. Handles cases for sequence
-        parallelism, sample packing, and curriculum sampling (sequential).
-
-        Returns:
-            If the dataset is non-empty, a sampler is returned, the type of which
-                depends on the passed training args.
-        """
-        use_sample_packing = self.args.sample_packing and not self.args.pretraining
-
-        # 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 base_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
-        )
-
+    def create_optimizer_grouped_parameters(self, opt_model, optimizer_kwargs):
+        decay_parameters = self.get_decay_parameter_names(opt_model)
         params = {
-            "batch_size": batch_size,
-            "collate_fn": self.data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
+            "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}"] = {}
 
-        # 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
+        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,
+                    }
+                )
 
-        # Add prefetch factor if specified
-        if self.args.dataloader_prefetch_factor:
-            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+        return optimizer_grouped_parameters
 
-        return params
+    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.args.alternate_optimizer
+            not in [
+                "optimi_adamw",
+                "ao_adamw_8bit",
+                "ao_adamw_4bit",
+                "ao_adamw_fp8",
+                "adopt_adamw",
+            ]
+        ):
+            return super().create_optimizer()
 
-    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)
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+                self.args,
+                opt_model,
+            )
+            optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
+                opt_model, optimizer_kwargs
+            )
 
-        # Add sampler configuration
-        if not isinstance(dataset, torch.utils.data.IterableDataset):
+            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,
+                )
+            elif (
+                self.args.embedding_lr_scale is not None
+                or self.args.embedding_lr is not None
+                or self.args.lr_groups is not None
+            ):
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW(
+                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
+                    )
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_4bit":
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
+            elif self.args.alternate_optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    ADOPT(
+                        optimizer_grouped_parameters,
+                        decouple=True,
+                        **optimizer_kwargs,
+                    )
+                )
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and not self.args.pretraining:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_train_batch_size
+                batch_max_len = self.args.max_seq_length
+            else:
+                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:
+                sampler = SequentialSampler(self.train_dataset)
+            else:
+                sampler = RandomSampler(self.train_dataset)
+
+            return MultipackBatchSampler(
+                sampler,
+                lengths=get_dataset_lengths(self.train_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
+            )
+        if self.args.curriculum_sampling:
+            return SequentialSampler(self.train_dataset)
+        return super()._get_train_sampler()
+
+    def _get_eval_sampler(
+        self, eval_dataset: Dataset
+    ) -> Optional[torch.utils.data.Sampler]:
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            if self.args.multipack_real_batches:
+                batch_size = self.args.per_device_eval_batch_size
+                batch_max_len = self.args.max_seq_length
+            else:
+                batch_size = 1
+                batch_max_len = (
+                    self.args.per_device_eval_batch_size * self.args.max_seq_length
+                )
+            return MultipackBatchSampler(
+                SequentialSampler(eval_dataset),
+                lengths=get_dataset_lengths(self.eval_dataset),
+                packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                batch_max_len=batch_max_len,
+                batch_size=batch_size,
+                group_size=self.args.sample_packing_group_size,
+                bin_size=self.args.sample_packing_bin_size,
+                drop_last=True,
+            )
+        return super()._get_eval_sampler(eval_dataset)
+
+    def get_train_dataloader(self) -> DataLoader:
+        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()
             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 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",
+            return self.accelerator.prepare_data_loader(
+                DataLoader(train_dataset, **dataloader_params)
             )
+        return super().get_train_dataloader()
 
-        # 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
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
         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 "length" in eval_dataset.column_names:
+            if eval_dataset:
                 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
 
-        # Handle sample packing or sequence parallelism
-        if (
-            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
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
+            eval_dataset = (
+                eval_dataset if eval_dataset is not None else self.eval_dataset
             )
 
-            return dataloader
+            eval_sampler = self._get_eval_sampler(eval_dataset)
+            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
-    ) -> torch.utils.data.Sampler | None:
+    ) -> Optional[torch.utils.data.Sampler]:
         if self.args.world_size <= 1:
             return SequentialSampler(bench_dataset)
         return None
@@ -347,7 +526,6 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         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
     ):
@@ -364,7 +542,6 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
                 return_outputs=return_outputs,
                 num_items_in_batch=num_items_in_batch,
             )
-
         return super().compute_loss(
             model,
             inputs,
@@ -539,10 +716,10 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         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)
 
@@ -559,13 +736,15 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
 
         return res
 
-    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
+    def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
         """
         Log `logs` on the various objects watching training, including stored metrics.
 
         Args:
-            logs: The values to log.
-            start_time: The start of training.
+            logs (`Dict[str, float]`):
+                The values to log.
+            start_time (`Optional[float]`):
+                The start of training.
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
@@ -577,7 +756,7 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         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)
@@ -590,26 +769,110 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, SequenceParallelMixin, Trai
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
 
-    def training_step(
+
+class AxolotlMambaTrainer(AxolotlTrainer):
+    """
+    Mamba specific trainer to handle loss calculation
+    """
+
+    tag_names = ["axolotl", "mamba"]
+
+    def compute_loss(
         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.
+        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
 
-        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)
+        labels = input_ids.to(lm_logits.device)
+        shift_logits = lm_logits[:, :-1, :].contiguous()
+        labels = labels[:, 1:].contiguous()
 
-        # Proceed with normal training step
-        loss = super().training_step(model, inputs, num_items_in_batch)
+        loss_fct = torch.nn.CrossEntropyLoss()
+        lm_loss = loss_fct(
+            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
+        )
 
-        return loss
+        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/__init__.py

@@ -1,7 +1,6 @@
 """
 DPO Specific Strategy for training
 """
-
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
 
 

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

@@ -1,7 +1,6 @@
 """
 Axolotl specific DPO args
 """
-
 from dataclasses import dataclass
 
 from trl import DPOConfig

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

@@ -1,7 +1,6 @@
 """
 DPO trainer for axolotl
 """
-
 import gc
 from functools import wraps
 from typing import Any, Dict, Union
@@ -13,10 +12,10 @@ from transformers import Trainer
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 
-from axolotl.core.trainers.mixins import SchedulerMixin
-from axolotl.core.trainers.utils import (
-    sanitize_kwargs_for_ds_tagging,
-    sanitize_kwargs_for_tagging,
+from axolotl.core.trainers.base import (
+    SchedulerMixin,
+    _sanitize_kwargs_for_ds_tagging,
+    _sanitize_kwargs_for_tagging,
 )
 
 if is_sagemaker_mp_enabled():
@@ -74,10 +73,10 @@ 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 +9,6 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.schemas.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -32,44 +31,30 @@ class GRPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         grpo_args_kwargs = {}
-
-        if not hasattr(cfg, "trl") or not cfg.trl:
-            return grpo_args_kwargs
-
-        trl: TRLConfig = cfg.trl  # type: ignore
-
-        if trl.use_vllm:
-            grpo_args_kwargs["use_vllm"] = trl.use_vllm
-            grpo_args_kwargs["vllm_device"] = (
-                trl.vllm_device if trl.vllm_device else "auto"
-            )
-
-            if trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
-                    trl.vllm_gpu_memory_utilization
-                )
-
-            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
-
-        if trl.sync_ref_model:
-            grpo_args_kwargs["sync_ref_model"] = trl.sync_ref_model
-
-            if trl.ref_model_mixup_alpha:
-                grpo_args_kwargs["ref_model_mixup_alpha"] = trl.ref_model_mixup_alpha
-
-            if trl.ref_model_sync_steps:
-                grpo_args_kwargs["ref_model_sync_steps"] = trl.ref_model_sync_steps
-
-        grpo_args_kwargs["max_completion_length"] = trl.max_completion_length
-        grpo_args_kwargs["log_completions"] = trl.log_completions
-
-        if trl.reward_weights:
-            grpo_args_kwargs["reward_weights"] = trl.reward_weights
-
+        if cfg.trl and cfg.trl.use_vllm:
+            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
+            if cfg.trl and cfg.trl.vllm_device:
+                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
+            else:
+                grpo_args_kwargs["vllm_device"] = "auto"
+            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
+                grpo_args_kwargs[
+                    "vllm_gpu_memory_utilization"
+                ] = cfg.trl.vllm_gpu_memory_utilization
+            if cfg.trl and cfg.trl.vllm_max_model_len:
+                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
+        if cfg.trl and cfg.trl.num_generations:
+            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
+        if cfg.trl and cfg.trl.sync_ref_model:
+            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
+            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
+                grpo_args_kwargs[
+                    "ref_model_mixup_alpha"
+                ] = cfg.trl.ref_model_mixup_alpha
+            if cfg.trl and cfg.trl.ref_model_sync_steps:
+                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
+        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
+        grpo_args_kwargs["log_completions"] = cfg.trl.log_completions
         return grpo_args_kwargs
 
     @classmethod
@@ -86,9 +71,9 @@ class GRPOStrategy:
     def set_trainer_kwargs(cls, cfg):
         trainer_kwargs = {}
         if cfg.trl and cfg.trl.reward_processing_classes:
-            trainer_kwargs["reward_processing_classes"] = (
-                cfg.trl.reward_processing_classes
-            )
+            trainer_kwargs[
+                "reward_processing_classes"
+            ] = cfg.trl.reward_processing_classes
         return trainer_kwargs
 
     @classmethod

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

@@ -1,7 +1,6 @@
 """
 Axolotl Specific Training Args
 """
-
 from dataclasses import dataclass
 
 from trl import GRPOConfig

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

@@ -1,7 +1,6 @@
 """
 Axolotl GRPO trainer
 """
-
 from accelerate.utils import is_peft_model
 from accelerate.utils.other import is_compiled_module
 from transformers import PreTrainedModel

src/axolotl/core/trainers/mamba.py

@@ -1,32 +0,0 @@
-"""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

@@ -1,8 +0,0 @@
-"""Init for axolotl.core.trainers.mixins"""
-
-# pylint: disable=unused-import
-# flake8: noqa
-
-from .optimizer import OptimizerMixin
-from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelMixin

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

@@ -1,201 +0,0 @@
-"""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/scheduler.py

@@ -1,113 +0,0 @@
-"""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

@@ -1,131 +0,0 @@
-"""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.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)

src/axolotl/core/trainers/relora.py

@@ -1,43 +0,0 @@
-"""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

@@ -1,25 +1,15 @@
-"""Module for TRL PPO trainer"""
-
-from typing import Literal, Union
-
+"""
+module for TRL PPO training
+"""
 import torch
 from tqdm import tqdm
-from trl import (
-    CPOTrainer,
-    KTOTrainer,
-    ORPOTrainer,
-    PPOTrainer,
-    PRMTrainer,
-    RewardTrainer,
-)
-
-from axolotl.core.trainers.mixins.scheduler import SchedulerMixin
+from trl import PPOTrainer
 
 
 class TRLPPOTrainer(PPOTrainer):
-    """Wrapper for TRL PPO trainer to handle customizations"""
-
-    tag_names = ["axolotl", "ppo"]
+    """
+    wrapper for ppo trainer to handle customizations
+    """
 
     def train(
         self,
@@ -40,7 +30,9 @@ class TRLPPOTrainer(PPOTrainer):
             "batch_size": 16,
         }
 
-        for _, batch in tqdm(enumerate(self.dataloader)):
+        for epoch, batch in tqdm(  # pylint: disable=unused-variable
+            enumerate(self.dataloader)
+        ):
             query_tensors = batch["input_ids"]
 
             # generate model response
@@ -72,189 +64,3 @@ class TRLPPOTrainer(PPOTrainer):
                 rewards,
                 columns_to_log=["query", "response", "ref_response", "ref_rewards"],
             )
-
-
-class AxolotlORPOTrainer(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(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"]
-
-    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(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/utils.py

@@ -1,33 +0,0 @@
-"""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

@@ -1,11 +1,9 @@
 """
 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
 
@@ -208,33 +206,14 @@ 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
-    default value so it can't be used as a mixin.
+    This code is duplicated due to HF TrainingArguments not setting output_dir with a defaujlt value
+    so it can't be used as a mixin.
     """
 
 

src/axolotl/evaluate.py

@@ -8,8 +8,6 @@ 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
@@ -27,18 +25,18 @@ LOG = get_logger("axolotl.evaluate")
 
 
 def evaluate_dataset(
-    trainer: Trainer, dataset: Dataset, dataset_type: str, flash_optimum: bool = False
+    trainer, dataset, dataset_type: str, flash_optimum: bool = False
 ) -> Optional[Dict[str, float]]:
-    """Helper function to evaluate a single dataset.
+    """Helper function to evaluate a single dataset safely.
 
     Args:
-        trainer: The trainer instance.
-        dataset: Dataset to evaluate.
-        dataset_type: Type of dataset ('train' or 'eval').
-        flash_optimum: Whether to use flash optimum.
+        trainer: The trainer instance
+        dataset: Dataset to evaluate
+        dataset_type: Type of dataset ('train' or 'eval')
+        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
@@ -65,14 +63,17 @@ 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:
-        Dictionary mapping metric names to their values.
+        Tuple containing:
+        - 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

src/axolotl/integrations/base.py

@@ -23,8 +23,6 @@ import importlib
 import logging
 from typing import OrderedDict
 
-import torch
-
 
 class BasePlugin:
     """
@@ -471,14 +469,3 @@ class PluginManager:
         """
         for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)
-
-
-class BaseOptimizerFactory:
-    """
-    Base class for factories to create custom optimizers
-    """
-
-    def __call__(
-        self, opt_model, training_args, **optimizer_kwargs
-    ) -> "torch.optim.Optimizer":
-        pass

src/axolotl/integrations/config.py

@@ -11,17 +11,19 @@
 # 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.schemas.config import (
+from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
+from axolotl.utils.config.models.input.v0_4_1 import (
+    AxolotlInputConfig as AxolotlInputConfigBase,
+)
 
 
 def merge_input_args():

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -1,25 +1,9 @@
 # Cut Cross Entropy
 
-Cut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
+Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
 
 See https://github.com/apple/ml-cross-entropy
 
-## Requirements
-
-- PyTorch 2.4.0 or higher
-
-## Installation
-
-Run the following command to install `cut_cross_entropy[transformers]` if you don't have it already.
-
-```bash
-# if you are in dev environment
-python scripts/cutcrossentropy_install.py | sh
-
-# if you are not in dev environment
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"
-```
-
 ## Usage
 
 ```yaml
@@ -29,20 +13,6 @@ 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

@@ -33,7 +33,7 @@ LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
 
 _CCE_INSTALL_MESSAGE = (
     "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"`'
+    '`pip install "cut-cross-entropy[transformers]==24.11.4"`'
 )
 
 
@@ -72,9 +72,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         if cfg.cut_cross_entropy:
             self._check_requirements()
 
-            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
-                cce_patch,
-            )
+            from cut_cross_entropy.transformers import cce_patch
 
             with zero_only():
                 LOG.info(

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

@@ -1,201 +0,0 @@
-"""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

@@ -1,175 +0,0 @@
-"""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

@@ -1,465 +0,0 @@
-"""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,
-    apply_lce,
-)
-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
-
-_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
-        if self.config.final_logit_softcapping is not None:
-            logger.warning_once(
-                "final_logit_softcapping is not supported for gemma3_text with CCE. Disabling."
-            )
-        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, :]
-
-        if self.config.final_logit_softcapping is not None:
-            logger.warning_once(
-                "final_logit_softcapping is not supported for gemma3 with CCE. Disabling."
-            )
-    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,
-            **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

@@ -1,392 +0,0 @@
-"""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

@@ -1,379 +0,0 @@
-"""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

@@ -1,85 +0,0 @@
-# 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/grokfast/__init__.py

@@ -1,7 +1,6 @@
 """
 Grokfast plugin for Axolotl
 """
-
 import logging
 
 from transformers.trainer_callback import TrainerCallback

src/axolotl/integrations/grokfast/args.py

@@ -1,7 +1,6 @@
 """
 config args for grokfast plugin
 """
-
 from typing import Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/kd/args.py

@@ -26,12 +26,12 @@ class KDArgs(BaseModel):
     """
 
     kd_trainer: Optional[bool] = None  # whether to use KD trainer
-    kd_ce_alpha: Optional[float] = (
-        None  # loss coefficient for cross-entropy loss during KD
-    )
+    kd_ce_alpha: Optional[
+        float
+    ] = None  # loss coefficient for cross-entropy loss during KD
     kd_alpha: Optional[float] = None  # loss coefficient for KD loss
     kd_temperature: Optional[float] = None  # temperature for sampling during KD
     kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
-    kd_top_k_before_softmax: Optional[bool] = (
-        None  # whether to sample top k before softmax during KD
-    )
+    kd_top_k_before_softmax: Optional[
+        bool
+    ] = None  # whether to sample top k before softmax during KD

src/axolotl/integrations/liger/__init__.py

@@ -55,9 +55,9 @@ class LigerPlugin(BasePlugin):
             if "cross_entropy" in liger_fn_sig.parameters:
                 kwargs["cross_entropy"] = cfg.liger_cross_entropy
             if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
-                kwargs["fused_linear_cross_entropy"] = (
-                    cfg.liger_fused_linear_cross_entropy
-                )
+                kwargs[
+                    "fused_linear_cross_entropy"
+                ] = cfg.liger_fused_linear_cross_entropy
             if "rms_norm" in liger_fn_sig.parameters:
                 kwargs["rms_norm"] = cfg.liger_rms_norm
             if "layer_norm" in liger_fn_sig.parameters:
@@ -114,5 +114,3 @@ class LigerPlugin(BasePlugin):
                 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_text", "deepseek_v3"]:
-            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")