wip fix

.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 .
-        - 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/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: |
@@ -147,7 +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: |

.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

@@ -55,7 +55,6 @@ 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 +96,6 @@ 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

@@ -1,178 +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.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"
@@ -204,11 +32,8 @@ 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/*
@@ -249,27 +74,12 @@ 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

@@ -31,7 +31,6 @@ 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 [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

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

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.
 ---
 
@@ -30,8 +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:
 
 # (Internal use only)
 # Used to identify which the model is based on
@@ -85,12 +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
@@ -213,46 +205,10 @@ 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:
@@ -276,7 +232,7 @@ default_system_message: You are a helpful assistant. Please give a long and deta
 # 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

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_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/faq.qmd

@@ -27,16 +27,6 @@ description: Frequently asked questions
 
 > 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.
-
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**

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]'
 ```
 
@@ -79,7 +78,6 @@ 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 +107,7 @@ 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/reward_modelling.qmd

@@ -41,10 +41,6 @@ Bradley-Terry chat templates expect single-turn conversations in the following f
 
 ### 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

docs/rlhf.qmd

@@ -298,7 +298,7 @@ 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 +344,8 @@ 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 +497,6 @@ 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:
@@ -545,19 +540,6 @@ To see other examples of custom reward functions, please see [TRL GRPO Docs](htt
 
 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

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:

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

@@ -1,7 +1,7 @@
 --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
@@ -12,12 +12,12 @@ liger-kernel==0.5.3
 
 packaging==23.2
 
-peft==0.15.0
+peft==0.14.0
 transformers==4.49.0
-tokenizers>=0.21.1
-accelerate==1.5.2
-datasets==3.4.1
-deepspeed==0.16.4
+tokenizers>=0.21.0
+accelerate==1.3.0
+datasets==3.2.0
+deepspeed==0.16.1
 trl==0.15.1
 
 optimum==1.16.2

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
 

setup.py

@@ -128,7 +128,7 @@ setup(
             "flash-attn==2.7.4.post1",
         ],
         "deepspeed": [
-            "deepspeed==0.16.4",
+            "deepspeed==0.16.1",
             "deepspeed-kernels",
         ],
         "mamba-ssm": [

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

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/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
@@ -23,7 +24,7 @@ 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.
 

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
 

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
@@ -38,7 +40,6 @@ from trl.trainer.utils import RewardDataCollatorWithPadding
 
 from axolotl.core.trainers.base import (
     AxolotlCPOTrainer,
-    AxolotlKTOTrainer,
     AxolotlMambaTrainer,
     AxolotlORPOTrainer,
     AxolotlPRMTrainer,
@@ -49,6 +50,7 @@ from axolotl.core.trainers.base import (
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
 from axolotl.core.trainers.grpo import GRPOStrategy
+from axolotl.core.trainers.kto import AxolotlKTOTrainer
 from axolotl.core.training_args import (
     AxolotlCPOConfig,
     AxolotlKTOConfig,
@@ -83,8 +85,8 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
+from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -330,9 +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
@@ -349,13 +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:
@@ -371,9 +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
@@ -397,28 +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
@@ -450,9 +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
 
@@ -465,13 +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:
@@ -480,32 +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
@@ -552,9 +554,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lr_scheduler in ["one_cycle", "rex", "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"
@@ -563,9 +565,9 @@ 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
         )
@@ -578,40 +580,40 @@ 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
@@ -625,9 +627,9 @@ 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 = {}
 
@@ -729,23 +731,23 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 importlib.import_module("torchdistx")
 
         if self.cfg.optim_target_modules:
-            training_arguments_kwargs["optim_target_modules"] = (
-                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[
+            "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.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
@@ -754,13 +756,13 @@ 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[
+                "kd_top_k_before_softmax"
+            ] = self.cfg.kd_top_k_before_softmax
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -970,32 +972,32 @@ 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
@@ -1069,9 +1071,9 @@ 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:
@@ -1106,9 +1108,9 @@ 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/base.py

@@ -20,9 +20,10 @@ from torch.utils.data import BatchSampler, DataLoader, RandomSampler, Sequential
 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 import CPOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
 
+from axolotl.core.trainers.kto import AxolotlKTOTrainer
 from axolotl.integrations.base import BaseOptimizerFactory
 from axolotl.monkeypatch.relora import ReLoRAScheduler
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
@@ -462,9 +463,9 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 "pin_memory": self.args.dataloader_pin_memory,
             }
             if self.args.dataloader_prefetch_factor:
-                dataloader_params["prefetch_factor"] = (
-                    self.args.dataloader_prefetch_factor
-                )
+                dataloader_params[
+                    "prefetch_factor"
+                ] = self.args.dataloader_prefetch_factor
 
             sampler = self._get_train_sampler()
             if isinstance(sampler, BatchSampler):
@@ -509,9 +510,9 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 "pin_memory": self.args.dataloader_pin_memory,
             }
             if self.args.dataloader_prefetch_factor:
-                dataloader_params["prefetch_factor"] = (
-                    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
@@ -874,14 +875,6 @@ class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     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

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

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

@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.schemas.trl import TRLConfig
+from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -45,9 +45,9 @@ class GRPOStrategy:
             )
 
             if trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
-                    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
@@ -86,9 +86,9 @@ 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/kto/__init__.py

@@ -0,0 +1,7 @@
+"""
+KTO package initialization.
+"""
+
+from axolotl.core.trainers.kto.trainer import AxolotlKTOTrainer
+
+__all__ = ["AxolotlKTOTrainer"]

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

@@ -0,0 +1,512 @@
+"""
+KTO trainer implementation for Axolotl.
+"""
+
+from __future__ import annotations
+
+import inspect
+import logging
+import warnings
+from contextlib import nullcontext
+from typing import Any, Callable, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from datasets import Dataset
+from torch.utils.data import DataLoader, SequentialSampler
+from transformers import (
+    AutoModelForCausalLM,
+    BaseImageProcessor,
+    DataCollator,
+    FeatureExtractionMixin,
+    PreTrainedModel,
+    PreTrainedTokenizerBase,
+    ProcessorMixin,
+    Trainer,
+    TrainerCallback,
+    TrainingArguments,
+)
+from transformers.trainer_utils import EvalLoopOutput
+from trl import KTOTrainer
+from trl.trainer.kto_config import KTOConfig
+from trl.trainer.utils import KTODataCollatorWithPadding, pad_to_length
+
+from axolotl.core.trainers.base import SchedulerMixin
+
+# Check if PEFT is available
+try:
+    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training, peft_module_casting_to_bf16
+    is_peft_available = True
+except ImportError:
+    is_peft_available = False
+
+LOG = logging.getLogger("axolotl.core.trainers.kto")
+
+
+class AxolotlKTOTrainer(SchedulerMixin, Trainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module, str] = None,
+        args: KTOConfig = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        data_collator: Optional[DataCollator] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+        dataset_tags=None,
+        model_adapter_name: Optional[str] = None,
+        ref_adapter_name: Optional[str] = None,
+    ):
+        self.dataset_tags = dataset_tags
+        self._tag_names = ["trl", "kto"]
+        if hasattr(self, "tag_names"):
+            self._tag_names.extend(self.tag_names)
+
+        if type(args) is TrainingArguments:
+            raise ValueError("Please use `KTOConfig` instead TrainingArguments.")
+
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if args.ref_model_init_kwargs is None:
+            ref_model_init_kwargs = {}
+        elif not isinstance(ref_model, str):
+            raise ValueError(
+                "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated."
+            )
+        else:
+            ref_model_init_kwargs = args.ref_model_init_kwargs
+            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                ref_model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        if isinstance(ref_model, str):
+            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = get_peft_model(model, peft_config)
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
+        self.model_adapter_name = model_adapter_name
+        self.ref_adapter_name = ref_adapter_name
+
+        if ref_model:
+            self.ref_model = ref_model
+        elif self.is_peft_model or args.precompute_ref_log_probs:
+            # The `model` with adapters turned off will be used as the reference model
+            self.ref_model = None
+        else:
+            self.ref_model = create_reference_model(model)
+
+        if processing_class is None:
+            raise ValueError(
+                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
+            )
+        if args.max_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init"
+                " it will be set to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        if args.max_length is not None:
+            max_length = args.max_length
+
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        if args.max_prompt_length is not None:
+            max_prompt_length = args.max_prompt_length
+
+        max_completion_length = None
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_completion_length = 128
+        if args.max_completion_length is not None and self.is_encoder_decoder:
+            max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+            if self.ref_model is not None:
+                disable_dropout_in_model(self.ref_model)
+
+        self.loss_type = args.loss_type
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.max_completion_length = max_completion_length
+        self.processing_class = processing_class
+        self.precompute_ref_log_probs = args.precompute_ref_log_probs
+
+        # Not all losses require a KL calculation
+        self.calculate_KL = True
+        if self.loss_type in ["apo_zero_unpaired"]:
+            self.calculate_KL = False
+
+        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
+        # keep track of first called to avoid computation of future calls
+        self._precomputed_train_ref_log_probs = False
+        self._precomputed_eval_ref_log_probs = False
+
+        # metric
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # KTO parameter
+        self.beta = args.beta
+        self.desirable_weight = args.desirable_weight
+        self.undesirable_weight = args.undesirable_weight
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
+        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
+        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
+        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
+        # issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Compute that only on the main process for faster data processing.
+        # see: https://github.com/huggingface/trl/pull/1255
+        with PartialState().local_main_process_first():
+            # Extract the prompt if needed
+            train_dataset = train_dataset.map(
+                maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset"
+            )
+            # Unpair the dataset if needed
+            train_dataset = maybe_unpair_preference_dataset(
+                train_dataset, args.dataset_num_proc, desc="Unpairing train dataset"
+            )
+            # Apply the chat template if needed
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template,
+                fn_kwargs={"tokenizer": processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Applying chat template to train dataset",
+            )
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset"
+                )
+                eval_dataset = maybe_unpair_preference_dataset(
+                    eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset"
+                )
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                    desc="Applying chat template to eval dataset",
+                )
+
+            # Tokenize and prepare the training datasets
+            train_dataset = train_dataset.map(
+                _tokenize,
+                batched=True,
+                fn_kwargs={"tokenizer": self.processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Tokenizing train dataset",
+            )
+
+            fn_kwargs = {
+                "prefix": "",
+                "is_encoder_decoder": self.is_encoder_decoder,
+                "tokenizer": self.processing_class,
+                "max_length": self.max_length,
+                "truncation_mode": self.truncation_mode,
+                "label_pad_token_id": self.label_pad_token_id,
+                "max_prompt_length": self.max_prompt_length,
+                "max_completion_length": self.max_completion_length,
+            }
+
+            train_dataset = train_dataset.map(
+                _process_tokens,
+                fn_kwargs=fn_kwargs,
+                num_proc=args.dataset_num_proc,
+                desc="Processing tokenized train dataset",
+            )
+
+            # Tokenize and prepare the eval datasets
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    _tokenize,
+                    fn_kwargs={"tokenizer": self.processing_class},
+                    batched=True,
+                    num_proc=args.dataset_num_proc,
+                    desc="Tokenizing eval dataset",
+                )
+
+                eval_dataset = eval_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    desc="Processing tokenized eval dataset",
+                )
+
+            # Get KL datasets if needed
+            if self.calculate_KL:
+                if args.per_device_train_batch_size <= 1:
+                    raise ValueError(
+                        "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward."
+                    )
+
+                # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size
+                # i.e., (x_1, y_1), ..., (x_n, y_n) --> (x_1, y_n), ..., (x_n, y_1) = (x'_1, y'_1), ..., (x'_n, y'_n)
+                train_kl_dataset = train_dataset.map(
+                    _get_kl_dataset,
+                    batched=True,
+                    batch_size=args.per_device_train_batch_size,
+                    num_proc=args.dataset_num_proc,
+                    desc="Extracting KL train dataset",
+                )
+
+                fn_kwargs["prefix"] = "KL_"
+                train_kl_dataset = train_kl_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names],
+                    desc="Processing tokenized train KL dataset",
+                )
+
+                # merge the datasets
+                train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1)
+
+                if eval_dataset is not None:
+                    # Get KL dataset
+                    eval_kl_dataset = eval_dataset.map(
+                        _get_kl_dataset,
+                        batched=True,
+                        batch_size=args.per_device_train_batch_size,
+                        num_proc=args.dataset_num_proc,
+                        desc="Extracting eval KL dataset",
+                    )
+
+                    eval_kl_dataset = eval_kl_dataset.map(
+                        _process_tokens,
+                        fn_kwargs=fn_kwargs,
+                        num_proc=args.dataset_num_proc,
+                        remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names],
+                        desc="Processing tokenized eval KL dataset",
+                    )
+
+                    # merge the datasets
+                    eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1)
+
+            # calculate dataset desirability balance
+            num_desirable = max(sum(train_dataset["label"]), 1)
+            num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1)  # "label" is binary
+
+            if num_desirable != num_undesirable:
+                # The lower and upper bounds come from Eq. (8) of https://huggingface.co/papers/2402.01306
+                des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2)
+                des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2)
+                und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2)
+                und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2)
+
+                des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound
+                und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound
+
+                if not (des_weight_in_range or und_weight_in_range):
+                    warnings.warn(
+                        "You have different amounts of desirable/positive and undesirable/negative examples but the "
+                        "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based "
+                        f"on your data, we recommend EITHER "
+                        f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or "
+                        f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). "
+                        "See the documentation on how to optimally set these weights.",
+                        UserWarning,
+                    )
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+        # Deepspeed Zero-3 does not support precompute_ref_log_probs
+        if self.is_deepspeed_enabled:
+            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
+                raise ValueError(
+                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
+                )
+
+        if self.ref_model is None:
+            if not (self.is_peft_model or self.precompute_ref_log_probs):
+                raise ValueError(
+                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
+                )
+        else:
+            if self.is_deepspeed_enabled:
+                self.ref_model = self._prepare_deepspeed(self.ref_model)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

src/axolotl/core/trainers/trl.py

@@ -1,7 +1,6 @@
 """
 module for TRL PPO training
 """
-
 import torch
 from tqdm import tqdm
 from trl import PPOTrainer

src/axolotl/core/training_args.py

@@ -1,7 +1,6 @@
 """
 extra axolotl specific training args
 """
-
 from dataclasses import dataclass, field
 from typing import Optional
 

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/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/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:

src/axolotl/integrations/liger/models/deepseekv2.py

@@ -1,7 +1,6 @@
 """
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
-
 # pylint: disable=duplicate-code
 
 from typing import List, Optional, Tuple, Union

src/axolotl/integrations/liger/models/jamba.py

@@ -1,7 +1,6 @@
 """
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
-
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple, Union

src/axolotl/integrations/lm_eval/__init__.py

@@ -1,7 +1,6 @@
 """
 Module for the Plugin for LM Eval Harness
 """
-
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin

src/axolotl/integrations/lm_eval/args.py

@@ -1,7 +1,6 @@
 """
 Module for handling lm eval harness input arguments.
 """
-
 from typing import List, Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/lm_eval/cli.py

@@ -1,7 +1,6 @@
 """
 axolotl CLI for running lm_eval tasks
 """
-
 import subprocess  # nosec
 from collections import defaultdict
 from datetime import datetime

src/axolotl/kernels/geglu.py

@@ -5,7 +5,6 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
-
 # pylint: disable=invalid-name,unnecessary-lambda-assignment,duplicate-code
 
 import torch

src/axolotl/kernels/lora.py

@@ -6,7 +6,6 @@ See "LoRA: Low-Rank Adaptation of Large Language Models"
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
-
 # pylint: disable=invalid-name
 
 from typing import Callable

src/axolotl/kernels/quantize.py

@@ -1,5 +1,4 @@
 """Dequantization utilities for `bitsandbytes` integration."""
-
 # pylint: disable=invalid-name,global-statement
 
 import ctypes

src/axolotl/kernels/swiglu.py

@@ -5,7 +5,6 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
-
 import torch
 import triton
 import triton.language as tl

src/axolotl/models/mamba/configuration_mamba.py

@@ -1,7 +1,6 @@
 """
 HF Transformers MambaConfig
 """
-
 from transformers import PretrainedConfig
 
 

src/axolotl/monkeypatch/attention/mllama.py

@@ -1,7 +1,6 @@
 """
 Monkeypatch for Vision Llama for FA2 support
 """
-
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple
@@ -221,10 +220,10 @@ def patch_mllama():
         True
     )
     MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
-    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES["flash_attention_2"] = (
-        MllamaTextCrossFlashAttention2
-    )
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MllamaTextCrossFlashAttention2
     # fallback to SDPA
-    MLLAMA_VISION_ATTENTION_CLASSES["flash_attention_2"] = (
-        MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]
-    )
+    MLLAMA_VISION_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -1,5 +1,4 @@
 """monkey patches for the dataset fetcher to handle batches of packed indexes"""
-
 # pylint: disable=protected-access
 
 import torch

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -12,9 +12,7 @@ import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
 from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.llama.modeling_llama import (
-    LlamaAttention,
-)
+from transformers.models.llama.modeling_llama import LlamaAttention
 from transformers.models.llama.modeling_llama import (
     LlamaDecoderLayer as OriginalLlamaDecoderLayer,
 )
@@ -492,11 +490,9 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=(
-                attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None
-            ),
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -535,11 +531,9 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=(
-                    attention_mask[:, -query_states.size(1) :]
-                    if attention_mask is not None
-                    else None
-                ),
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -1,7 +1,6 @@
 """
 expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
 """
-
 from typing import Optional
 
 import torch

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -1,5 +1,4 @@
 """Flash attention monkey patch for mistral model"""
-
 # pylint: disable=duplicate-code
 
 import logging
@@ -22,10 +21,7 @@ from transformers.models.mistral.modeling_mistral import (
 from transformers.models.mistral.modeling_mistral import (
     MistralDecoderLayer as OriginalMistralDecoderLayer,
 )
-from transformers.models.mistral.modeling_mistral import (
-    apply_rotary_pos_emb,
-    repeat_kv,
-)
+from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 
@@ -247,11 +243,9 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=(
-                attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None
-            ),
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -292,11 +286,9 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=(
-                    attention_mask[:, -query_states.size(1) :]
-                    if attention_mask is not None
-                    else None
-                ),
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -1,7 +1,6 @@
 """
 Patches to support multipack for mixtral
 """
-
 import torch
 
 

src/axolotl/monkeypatch/relora.py

@@ -1,5 +1,4 @@
 """Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
-
 import glob
 import json
 import logging
@@ -412,10 +411,7 @@ def merge_and_save(
         if shard_path.endswith(".safetensors"):
             in_tensors = st.load_file(str(Path(model_src) / shard_path))
         else:
-            in_tensors = torch.load(
-                Path(model_src) / shard_path,
-                weights_only=True,  # to prevent arbitrary code execution
-            )
+            in_tensors = torch.load(Path(model_src) / shard_path)
             if "state_dict" in in_tensors:
                 in_tensors = in_tensors["state_dict"]
 

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -17,7 +17,7 @@
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
 # pylint: disable=duplicate-code
-"""PyTorch StableLM Epoch model."""
+""" PyTorch StableLM Epoch model. """
 import importlib
 import math
 from typing import Optional, Tuple, Union

src/axolotl/monkeypatch/trainer_fsdp_optim.py

@@ -1,7 +1,6 @@
 """
 fix for FSDP optimizer save in trainer w 4.47.0
 """
-
 import inspect
 import logging
 

src/axolotl/monkeypatch/utils.py

@@ -1,7 +1,6 @@
 """
 Shared utils for the monkeypatches
 """
-
 import re
 from typing import Optional, Tuple
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -1,7 +1,6 @@
 """
 Fused MLP layer for incrementally improved training efficiency
 """
-
 import torch
 from transformers.models.llama.modeling_llama import LlamaMLP
 from xformers.ops import SwiGLU

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -1,7 +1,6 @@
 """
 Prompt strategies loader for alpaca instruction datasets with system prompts
 """
-
 from typing import Generator, Tuple, Union
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy

src/axolotl/prompt_strategies/chat_template.py

@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.schemas.datasets import DatasetConfig
+from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")

src/axolotl/prompt_strategies/completion.py

@@ -1,7 +1,6 @@
 """
 Basic completion text
 """
-
 from collections import defaultdict
 from typing import Any, Dict, Generator, Optional, Tuple
 

src/axolotl/prompt_strategies/context_qa.py

@@ -1,5 +1,4 @@
 """Module containing the classes for Context QA Prompt Tokenization Strategies"""
-
 from typing import Tuple
 
 from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy

src/axolotl/prompt_strategies/dpo/__init__.py

@@ -1,7 +1,6 @@
 """
 module for DPO style dataset transform strategies
 """
-
 from functools import partial
 
 from ..base import load as load_base

src/axolotl/prompt_strategies/dpo/chat_template.py

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

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -33,9 +33,9 @@ def default(
                 f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample[chosen_key]}<|im_end|>"
         sample["rejected"] = f"{sample[rejected_key]}<|im_end|>"
         return sample
@@ -52,9 +52,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample["prompt"] = (
-            f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
-        )
+        sample[
+            "prompt"
+        ] = f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
         return sample
@@ -78,9 +78,9 @@ def icr(
                 f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -100,9 +100,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -120,9 +120,9 @@ def prompt_pairs(
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -142,9 +142,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
         return sample

src/axolotl/prompt_strategies/dpo/llama3.py

@@ -34,9 +34,9 @@ def default(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample[chosen_key]}<|eot_id|>"
         sample["rejected"] = f"{sample[rejected_key]}<|eot_id|>"
         return sample
@@ -53,9 +53,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample["prompt"] = (
-            f"<|start_header_id|>user<|end_header_id|>\n\n{sample['chosen'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-        )
+        sample[
+            "prompt"
+        ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['chosen'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|eot_id|>"
         return sample
@@ -79,9 +79,9 @@ def icr(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -101,9 +101,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -121,9 +121,9 @@ def prompt_pairs(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -143,9 +143,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|eot_id|>"
         return sample

src/axolotl/prompt_strategies/input_output.py

@@ -1,5 +1,4 @@
 """Module for plain input/output prompt pairs"""
-
 from typing import Generator, Tuple
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy

src/axolotl/prompt_strategies/jinja_template_analyzer.py

@@ -1,5 +1,4 @@
 """Module for inspect jinja templates for the variables they use"""
-
 from typing import Dict, Optional, Set, TypedDict, Union
 
 from jinja2 import Environment, meta, nodes

src/axolotl/prompt_strategies/kto/chatml.py

@@ -1,7 +1,6 @@
 """
 KTO strategies for chatml
 """
-
 # pylint: disable=duplicate-code
 
 
@@ -16,9 +15,9 @@ def argilla(
                 f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -34,9 +33,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample["prompt"] = (
-            f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
-        )
+        sample[
+            "prompt"
+        ] = f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
         sample["completion"] = f"{sample['completion'][1]['content']}<|im_end|>"
         return sample
 
@@ -56,9 +55,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -75,9 +74,9 @@ def prompt_pairs(
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -97,9 +96,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 

src/axolotl/prompt_strategies/kto/llama3.py

@@ -1,7 +1,6 @@
 """
 KTO strategies for llama-3 chat template
 """
-
 # pylint: disable=duplicate-code
 
 
@@ -16,9 +15,9 @@ def argilla(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -34,9 +33,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample["prompt"] = (
-            f"<|start_header_id|>user<|end_header_id|>\n\n{sample['completion'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-        )
+        sample[
+            "prompt"
+        ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['completion'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["completion"] = f"{sample['completion'][1]['content']}<|eot_id|>"
         return sample
 
@@ -56,9 +55,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -75,9 +74,9 @@ def prompt_pairs(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -97,9 +96,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample["prompt"] = (
-                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
-            )
+            sample[
+                "prompt"
+            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 

src/axolotl/prompt_strategies/kto/user_defined.py

@@ -1,7 +1,6 @@
 """
 User-defined KTO strategies
 """
-
 # pylint: disable=duplicate-code
 
 

src/axolotl/prompt_strategies/messages/chat.py

@@ -1,7 +1,6 @@
 """
 Chat dataset wrapping strategy for new internal messages representations
 """
-
 from typing import Any, Callable, Dict, Optional
 
 from axolotl.core.datasets.chat import TokenizedChatDataset

src/axolotl/prompt_strategies/orcamini.py

@@ -9,7 +9,6 @@ this one specifies the system prompt with "### System:".
 
 Not suited/tested for multiple-turn conversations without further adjustments.
 """
-
 from typing import Generator, Union
 
 from axolotl.prompt_strategies.alpaca_w_system import OpenOrcaPromptTokenizingStrategy