fix import

optimizer_cls_and_kwargs is on trainer_kwargs
only add adamw_kwargs if they're non-null
fix mocks
better handling of override and check the optimizer
unwrap optimizer

.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/main.yml

@@ -88,11 +88,6 @@ jobs:
             pytorch: 2.5.1
             axolotl_extras:
             is_latest: true
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/nightlies.yml

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

.github/workflows/precommit-autoupdate.yml

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

.github/workflows/pypi.yml

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

.github/workflows/tests-nightly.yml

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

.github/workflows/tests.yml

@@ -74,7 +74,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging setuptools wheel
 
       - name: Install PyTorch
         run: |
@@ -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: |

.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

_quarto.yml

@@ -32,9 +32,8 @@ website:
           contents:
             - docs/getting-started.qmd
             - docs/installation.qmd
-            - docs/inference.qmd
             - docs/cli.qmd
-            - docs/config.qmd
+            - docs/inference.qmd
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*
@@ -75,6 +74,10 @@ website:
             - docs/debugging.qmd
             - docs/nccl.qmd
 
+        - section: "Reference"
+          contents:
+            - docs/config.qmd
+
 format:
   html:
     theme: darkly

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

docker/Dockerfile-cloud

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

docs/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
@@ -162,6 +154,8 @@ datasets:
       content: value
       # ...
 
+    message_property_mappings:
+
     # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
       user: ["human", "user"]
@@ -213,46 +207,10 @@ 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 +234,7 @@ 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
@@ -598,13 +556,6 @@ special_tokens:
 # Add extra tokens.
 tokens:
 
-# Mapping token_id to new_token_string to override reserved added_tokens in the tokenizer.
-# Only works for tokens that are not part of the base vocab (aka are added_tokens).
-# Can be checked if they exist in tokenizer.json added_tokens.
-added_tokens_overrides:  # Dict[int, str]
-#  128041: "<|im_start|>"
-#  128042: "<|im_end|>"
-
 # FSDP
 fsdp:
 fsdp_config:

docs/custom_integrations.qmd

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

docs/dataset-formats/conversation.qmd

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

docs/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' / ____`**
@@ -62,7 +52,3 @@ description: Frequently asked questions
 **Q: The EOS/EOT token is incorrectly being masked or not being masked.**
 
 > A: This is because of the mismatch between `tokenizer.eos_token` and EOS/EOT token in template. Please make sure to set `eos_token` under `special_tokens` to the same EOS/EOT token as in template.
-
-**Q: "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null. Please add a `chat_template` in tokenizer config"**
-
-> A: This is because the tokenizer does not have a chat template. Please add a chat template in the tokenizer config. See [chat_template](dataset-formats/conversation.qmd#chat-template) for more details.

docs/getting-started.qmd

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

docs/inference.qmd

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

docs/installation.qmd

@@ -22,7 +22,6 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 ### PyPI Installation (Recommended) {#sec-pypi}
 
 ```{.bash}
-pip3 install -U packaging setuptools wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
@@ -38,7 +37,7 @@ For the latest features between releases:
 ```{.bash}
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip3 install -U packaging setuptools wheel ninja
+pip3 install packaging ninja
 pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
@@ -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

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

docs/rlhf.qmd

@@ -3,7 +3,6 @@ title: "RLHF (Beta)"
 description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
 back-to-top-navigation: true
 toc: true
-toc-expand: 2
 toc-depth: 4
 ---
 
@@ -298,7 +297,7 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### IPO
 
-As IPO is just DPO with a different loss function, all supported dataset formats for [DPO](#dpo) are also supported for IPO.
+As IPO is just DPO with a different loss function, all supported options for DPO works here.
 
 ```yaml
 rl: ipo
@@ -344,9 +343,8 @@ ORPO supports the following types with the following dataset format:
 
 ```yaml
 rl: kto
-rl_beta: 0.1  # default
-kto_desirable_weight: 1.0  # default
-kto_undesirable_weight: 1.0  # default
+rl_beta: 0.5
+kto_desirable_weight: 0.2
 
 remove_unused_columns: false
 
@@ -498,10 +496,6 @@ The input format is a simple JSON input with customizable fields based on the ab
 
 ### GRPO
 
-::: {.callout-tip}
-Check out our [GRPO cookbook](https://github.com/axolotl-ai-cloud/axolotl-cookbook/tree/main/grpo#training-an-r1-style-large-language-model-using-grpo).
-:::
-
 GRPO uses custom reward functions and transformations. Please have them ready locally.
 
 For ex, to load OpenAI's GSM8K and use a random reward for completions:
@@ -534,7 +528,6 @@ trl:
     vllm_gpu_memory_utilization: 0.15
     num_generations: 4
     reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
-    reward_weights: [1.0]
 datasets:
   - path: openai/gsm8k
     name: main
@@ -543,21 +536,6 @@ datasets:
 
 To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).
 
-To see description of the configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/models/input/v0_4_1/trl.py).
-
-### SimPO
-
-SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.
-
-```yaml
-rl: simpo
-rl_beta: 0.1  # default in CPOTrainer
-cpo_alpha: 1.0  # default in CPOTrainer
-simpo_gamma: 0.5  # default in CPOTrainer
-```
-
-This method uses the same dataset format as [DPO](#dpo).
-
 ### Using local dataset files
 
 ```yaml

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.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
@@ -62,5 +62,5 @@ antlr4-python3-runtime==4.13.2
 torchao==0.7.0
 schedulefree==1.3.0
 
-axolotl-contribs-lgpl==0.0.6
+axolotl-contribs-lgpl==0.0.3
 axolotl-contribs-mit==0.0.3

scripts/chat_datasets.py

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

scripts/cutcrossentropy_install.py

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

setup.py

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

src/axolotl/cli/main.py

@@ -1,5 +1,4 @@
 """Click CLI definitions for various axolotl commands."""
-
 # pylint: disable=redefined-outer-name
 
 import logging

src/axolotl/cli/train.py

@@ -1,7 +1,6 @@
 """CLI to run training on a model."""
 
 import logging
-import os
 from pathlib import Path
 from typing import Union
 
@@ -35,8 +34,7 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     """
     print_axolotl_text_art()
     check_accelerate_default_config()
-    if int(os.getenv("LOCAL_RANK", "0")) == 0:
-        check_user_token()
+    check_user_token()
 
     if cfg.rl:
         dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)

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
 
@@ -44,7 +43,7 @@ class TokenizedChatDataset(Dataset):
         process_or_cpu_count: int = (
             process_count or os.cpu_count()  # type: ignore[assignment]
         )
-        num_proc = min(32, process_or_cpu_count)
+        num_proc = min(64, process_or_cpu_count)
         features = data.features.keys()
         tokenized_data = data.map(
             map_fn,

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

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

src/axolotl/core/trainer_builder.py

@@ -332,9 +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
@@ -351,13 +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:
@@ -373,9 +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
@@ -399,28 +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
@@ -452,9 +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
 
@@ -467,13 +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:
@@ -482,32 +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
@@ -554,9 +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"
@@ -565,9 +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
         )
@@ -580,40 +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
@@ -627,9 +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 = {}
 
@@ -731,23 +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
@@ -756,13 +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
@@ -972,32 +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
@@ -1071,9 +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:
@@ -1108,9 +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

@@ -462,9 +462,9 @@ 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 +509,9 @@ 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

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

@@ -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/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/integrations/cut_cross_entropy/README.md

@@ -17,7 +17,7 @@ Run the following command to install `cut_cross_entropy[transformers]` if you do
 python scripts/cutcrossentropy_install.py | sh
 
 # if you are not in dev environment
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
 ```
 
 ## Usage

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -33,7 +33,7 @@ LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
 
 _CCE_INSTALL_MESSAGE = (
     "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"`'
+    '`pip install "cut-cross-entropy[transformers]==24.11.4"`'
 )
 
 

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/integrations/spectrum/args.py

@@ -17,7 +17,7 @@ Module for handling Spectrum input arguments.
 """
 from typing import Optional
 
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel
 
 
 class SpectrumArgs(BaseModel):
@@ -27,20 +27,3 @@ class SpectrumArgs(BaseModel):
 
     spectrum_top_fraction: Optional[float] = 0.5
     spectrum_model_name: Optional[str] = None
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_use_orig_params(cls, data):
-        if (
-            data.get("fsdp")
-            and data.get("fsdp_config")
-            and not data["fsdp_config"].get("use_orig_params")
-            and data.get("plugins")
-            and any("SpectrumPlugin" in plugin for plugin in data["plugins"])
-        ):
-            # would otherwise raise
-            # ValueError: Must flatten tensors with uniform `requires_grad` when `use_orig_params=False`
-            raise ValueError(
-                "FSDP + SpectrumPlugin cannot be used together when `use_orig_params=False` is set"
-            )
-        return data

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/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/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

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -1,5 +1,4 @@
 """chatml prompt tokenization strategy for ORPO"""
-
 from typing import Any, Dict, Generator, List, Optional, Tuple
 
 from pydantic import BaseModel

src/axolotl/prompt_strategies/pretrain.py

@@ -1,5 +1,4 @@
 """pretraining prompt strategies"""
-
 from typing import Generator
 
 from transformers import BatchEncoding

src/axolotl/train.py

@@ -7,7 +7,7 @@ import signal
 import sys
 import weakref
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any
 
 import torch
 import transformers.modelcard
@@ -20,7 +20,7 @@ from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
 
 from axolotl.common.datasets import TrainDatasetMeta
-from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
+from axolotl.contribs.lgpl.unsloth import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
@@ -382,23 +382,21 @@ def handle_untrained_tokens_fix(
     if not cfg.fix_untrained_tokens:
         return
 
-    is_ds_zero3: bool = False
-    if os.environ.get("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3":
-        is_ds_zero3 = True
-
     # Check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
     sig = inspect.signature(fix_untrained_tokens)
 
-    fix_kwargs: Dict[str, Any] = {}
     # If the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
     if "token_ids_to_fix" in sig.parameters and isinstance(
         cfg.fix_untrained_tokens, list
     ):
-        fix_kwargs["token_ids_to_fix"] = cfg.fix_untrained_tokens
-    if "is_ds_zero3" in sig.parameters:
-        fix_kwargs["is_ds_zero3"] = is_ds_zero3
-
-    fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
+        fix_untrained_tokens(
+            model,
+            tokenizer,
+            train_dataset,
+            token_ids_to_fix=cfg.fix_untrained_tokens,
+        )
+    else:
+        fix_untrained_tokens(model, tokenizer, train_dataset)
 
     if cfg.local_rank == 0:
         model.save_pretrained(
@@ -406,7 +404,9 @@ def handle_untrained_tokens_fix(
         )
 
 
-def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[
+def setup_model_and_trainer(
+    cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> tuple[
     HFRLTrainerBuilder | HFCausalTrainerBuilder,
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,

src/axolotl/utils/__init__.py

@@ -40,6 +40,6 @@ def set_pytorch_cuda_alloc_conf():
     torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
     if torch_major == 2 and torch_minor >= 2:
         if os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None:
-            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
-                "expandable_segments:True,roundup_power2_divisions:16"
-            )
+            os.environ[
+                "PYTORCH_CUDA_ALLOC_CONF"
+            ] = "expandable_segments:True,roundup_power2_divisions:16"