installing axolotl prior to quartodoc build

pre-commit
update pylint
2025-03-21 16:51:02 +00:00 · 2025-03-21 11:23:39 -04:00 · 2025-03-21 11:18:59 -04:00 · 2025-03-21 11:18:59 -04:00 · 2025-03-21 11:18:59 -04:00 · 2025-03-21 11:18:59 -04:00
175 changed files with 1887 additions and 1261 deletions
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -40,6 +40,12 @@ 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
@@ -61,7 +67,7 @@ jobs:
        uses: docker/build-push-action@v4
        with:
          context: .
-          file: ./docker/Dockerfile-base
+          file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || './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 }}
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -20,9 +20,12 @@ jobs:
          uses: actions/setup-python@v5
          with:
            python-version: '3.11'
-        - name: install dependencies
+        - name: Install dependencies
          run: |
-            python3 -m pip install jupyter
+            python3 -m pip install jupyter quartodoc
+            python3 -m pip install -e .
+        - name: Build autodoc
+          run: quartodoc build
        - name: Publish to GitHub Pages (and render)
          uses: quarto-dev/quarto-actions/publish@v2
          with:
--- a/.github/workflows/precommit-autoupdate.yml
+++ b/.github/workflows/precommit-autoupdate.yml
@@ -0,0 +1,49 @@
+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>
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -40,7 +40,7 @@ jobs:

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

--- a/.github/workflows/tests-nightly.yml
+++ b/.github/workflows/tests-nightly.yml
@@ -42,7 +42,7 @@ jobs:
      - name: upgrade pip
        run: |
          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel

      - name: Install PyTorch
        run: |
@@ -59,7 +59,7 @@ jobs:
      - name: Install dependencies
        run: |
          pip3 install --upgrade pip
-          pip3 install --upgrade packaging
+          pip3 install --upgrade packaging==23.2
          pip3 install --no-build-isolation -U -e .
          python scripts/unsloth_install.py | sh
          python scripts/cutcrossentropy_install.py | sh
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -74,7 +74,7 @@ jobs:
      - name: upgrade pip
        run: |
          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel

      - name: Install PyTorch
        run: |
@@ -147,7 +147,7 @@ jobs:
      - name: upgrade pip
        run: |
          pip3 install --upgrade pip
-          pip3 install --upgrade packaging setuptools setuptools_scm build wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel

      - name: Install PyTorch
        run: |
--- a/.gitignore
+++ b/.gitignore
@@ -181,6 +181,10 @@ prepared-datasets/
 submit.sh
 *.out*

+# Quartodoc generated files
+objects.json
+site_libs/
+
 typings/
 out/

--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,7 +3,7 @@ default_language_version:

 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
    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: 23.3.0
+    rev: 25.1.0
    hooks:
    -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 6.0.1
    hooks:
      - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.1.2
    hooks:
    - id: flake8
-   repo: https://github.com/PyCQA/pylint
-    rev: v3.3.0
+-   repo: https://github.com/pylint-dev/pylint
+    rev: v3.3.6
    hooks:
    - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.3.0
+    rev: v1.15.0
    hooks:
    - id: mypy
      additional_dependencies:
@@ -36,7 +36,7 @@ repos:
            'pydantic>=2.5.3',
        ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.7.5
+    rev: 1.8.3
    hooks:
    -   id: bandit
        args: [
--- a/README.md
+++ b/README.md
@@ -55,7 +55,7 @@ Features:
 ### Installation

 ```bash
-pip3 install -U packaging setuptools wheel ninja
+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
@@ -97,6 +97,7 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 - [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
 - [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
 - [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
+- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
 - [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions

 ## 🤝 Getting Help
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -1,6 +1,178 @@
 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"
@@ -35,6 +207,8 @@ website:
            - docs/inference.qmd
            - docs/cli.qmd
            - docs/config.qmd
+            - text: "API Reference"
+              href: docs/api

        - section: "Dataset Formats"
          contents: docs/dataset-formats/*
@@ -80,3 +254,22 @@ format:
    theme: darkly
    css: styles.css
    toc: true
+    # Enable better handling of line breaks in markdown
+    preserve-tabs: true
+    html-math-method: mathjax
+    # Improved markdown processing options
+    md-extensions:
+      - markdown_it
+      - def_list
+      - attr_list
+      - fenced_divs
+      - tables
+      - html_admonition
+      - lineblocks
+      - fancy_lists
+    # Control whitespace handling
+    whitespace: preserve
+    # Process newlines in paragraphs
+    wrap: preserve
+    # Better line break handling
+    preserve-linebreaks: true
--- a/cicd/Dockerfile.jinja
+++ b/cicd/Dockerfile.jinja
@@ -31,6 +31,7 @@ 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 \
--- a/cicd/multigpu.py
+++ b/cicd/multigpu.py
@@ -1,6 +1,7 @@
 """
- modal application to run axolotl gpu tests in Modal
- """
+modal application to run axolotl gpu tests in Modal
+"""
+
 # pylint: disable=duplicate-code

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

 import os
--- a/docker/Dockerfile-base
+++ b/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 packaging && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
--- a/docker/Dockerfile-base-nightly
+++ b/docker/Dockerfile-base-nightly
@@ -0,0 +1,39 @@
+ARG CUDA_VERSION="12.8.1"
+ARG CUDNN_VERSION="8"
+ARG UBUNTU_VERSION="22.04"
+ARG MAX_JOBS=4
+
+FROM nvidia/cuda:$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
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,2 +1,4 @@
 /.quarto/
 _site/
+/api/*.qmd
+/api/*.html
--- a/docs/cli.qmd
+++ b/docs/cli.qmd
@@ -1,5 +1,5 @@
 ---
-title: "CLI Reference"
+title: "Command Line Interface (CLI)"
 format:
  html:
    toc: true
--- a/docs/config.qmd
+++ b/docs/config.qmd
@@ -85,6 +85,12 @@ 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
--- a/docs/custom_integrations.qmd
+++ b/docs/custom_integrations.qmd
@@ -55,3 +55,47 @@ 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)
+
+:::
--- a/docs/dataset_preprocessing.qmd
+++ b/docs/dataset_preprocessing.qmd
@@ -6,7 +6,7 @@ description: How datasets are processed
 ## Overview

 Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
-the [dataset format](docs/dataset-formats) and prompt strategies to:
+the [dataset format](dataset-formats) and prompt strategies to:

 - parse the dataset based on the *dataset format*
 - transform the dataset to how you would interact with the model based on the *prompt strategy*
--- a/docs/installation.qmd
+++ b/docs/installation.qmd
@@ -79,6 +79,7 @@ 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}

--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
 build-backend = "setuptools.build_meta"

 [project]
@@ -8,6 +8,7 @@ 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"
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -2,3 +2,5 @@ pre-commit
 black
 mypy
 types-requests
+quartodoc
+jupyter
--- a/requirements.txt
+++ b/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==0.45.2
+bitsandbytes==0.45.3
 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==0.14.0
+peft==0.15.0
 transformers==4.49.0
-tokenizers>=0.21.0
-accelerate==1.3.0
-datasets==3.2.0
-deepspeed==0.16.1
+tokenizers>=0.21.1
+accelerate==1.5.2
+datasets==3.4.1
+deepspeed==0.16.4
 trl==0.15.1

 optimum==1.16.2
--- a/scripts/chat_datasets.py
+++ b/scripts/chat_datasets.py
@@ -1,6 +1,7 @@
 """
 helper script to parse chat datasets into a usable yaml
 """
+
 import click
 import yaml
 from datasets import load_dataset
--- a/scripts/cutcrossentropy_install.py
+++ b/scripts/cutcrossentropy_install.py
@@ -1,4 +1,5 @@
 """Script to output the correct installation command for cut-cross-entropy."""
+
 import importlib.util
 import sys

--- a/setup.py
+++ b/setup.py
@@ -128,7 +128,7 @@ setup(
            "flash-attn==2.7.4.post1",
        ],
        "deepspeed": [
-            "deepspeed==0.16.1",
+            "deepspeed==0.16.4",
            "deepspeed-kernels",
        ],
        "mamba-ssm": [
--- a/src/axolotl/cli/cloud/init.py
+++ b/src/axolotl/cli/cloud/init.py
@@ -1,6 +1,7 @@
 """
 launch axolotl in supported cloud platforms
 """
+
 from pathlib import Path
 from typing import Union

--- a/src/axolotl/cli/cloud/base.py
+++ b/src/axolotl/cli/cloud/base.py
@@ -1,6 +1,7 @@
 """
 base class for cloud platforms from cli
 """
+
 from abc import ABC, abstractmethod


--- a/src/axolotl/cli/cloud/modal_.py
+++ b/src/axolotl/cli/cloud/modal_.py
@@ -1,6 +1,7 @@
 """
 Modal Cloud support from CLI
 """
+
 import copy
 import json
 import os
--- a/src/axolotl/cli/main.py
+++ b/src/axolotl/cli/main.py
@@ -1,4 +1,5 @@
 """Click CLI definitions for various axolotl commands."""
+
 # pylint: disable=redefined-outer-name

 import logging
@@ -24,7 +25,7 @@ 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.config.models.input.v0_4_1 import AxolotlInputConfig
+from axolotl.utils.schemas.config import AxolotlInputConfig


@click.group()
--- a/src/axolotl/cli/utils.py
+++ b/src/axolotl/cli/utils.py
@@ -5,7 +5,6 @@ import dataclasses
 import hashlib
 import json
 import logging
-import typing
 from functools import wraps
 from pathlib import Path
 from types import NoneType
@@ -24,7 +23,7 @@ configure_logging()
 LOG = logging.getLogger(__name__)


-def strip_optional_type(field_type: type | typing._SpecialForm | None):
+def strip_optional_type(field_type: type | str | None):
    """
    Extracts the non-`None` type from an `Optional` / `Union` type.

--- a/src/axolotl/convert.py
+++ b/src/axolotl/convert.py
@@ -1,6 +1,5 @@
 """Module containing File Reader, File Writer, Json Parser, and Jsonl Serializer classes"""

-
 import json
 import sys

--- a/src/axolotl/core/chat/format/chatml.py
+++ b/src/axolotl/core/chat/format/chatml.py
@@ -1,6 +1,7 @@
 """
 ChatML transformation functions for MessageContents
 """
+
 from typing import Optional

 from ..messages import MessageContents, Messages
--- a/src/axolotl/core/chat/format/llama3x.py
+++ b/src/axolotl/core/chat/format/llama3x.py
@@ -1,6 +1,7 @@
 """
 Llama 3.x chat formatting functions for MessageContents
 """
+
 from typing import Optional

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


--- a/src/axolotl/core/chat/messages.py
+++ b/src/axolotl/core/chat/messages.py
@@ -1,6 +1,7 @@
 """
 internal message representations of chat messages
 """
+
 import json
 from enum import Enum
 from typing import Any, Callable, List, Optional, Union
--- a/src/axolotl/core/datasets/chat.py
+++ b/src/axolotl/core/datasets/chat.py
@@ -1,6 +1,7 @@
 """
 chat dataset module
 """
+
 import os
 from typing import Callable, Optional, Union

--- a/src/axolotl/core/datasets/transforms/chat_builder.py
+++ b/src/axolotl/core/datasets/transforms/chat_builder.py
@@ -1,6 +1,7 @@
 """
 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


--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -13,9 +13,7 @@
 # limitations under the License.

 # pylint: disable=too-many-lines
-"""
-Builder for the training args and trainer
-"""
+"""Builder for the training args and trainer"""

 import abc
 import importlib
@@ -85,8 +83,8 @@ from axolotl.utils.collators import (
    V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
-from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers

 try:
    import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -332,9 +330,9 @@ 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 +349,13 @@ 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 +371,9 @@ 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 +397,28 @@ 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 +450,9 @@ 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 +465,13 @@ 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 +480,32 @@ 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 +552,9 @@ 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 +563,9 @@ 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 +578,40 @@ 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 +625,9 @@ 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 +729,23 @@ 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 +754,13 @@ 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 +970,32 @@ 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 +1069,9 @@ 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 +1106,9 @@ 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]
--- a/src/axolotl/core/trainers/base.py
+++ b/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
--- a/src/axolotl/core/trainers/dpo/init.py
+++ b/src/axolotl/core/trainers/dpo/init.py
@@ -1,6 +1,7 @@
 """
 DPO Specific Strategy for training
 """
+
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer


--- a/src/axolotl/core/trainers/dpo/args.py
+++ b/src/axolotl/core/trainers/dpo/args.py
@@ -1,6 +1,7 @@
 """
 Axolotl specific DPO args
 """
+
 from dataclasses import dataclass

 from trl import DPOConfig
--- a/src/axolotl/core/trainers/dpo/trainer.py
+++ b/src/axolotl/core/trainers/dpo/trainer.py
@@ -1,6 +1,7 @@
 """
 DPO trainer for axolotl
 """
+
 import gc
 from functools import wraps
 from typing import Any, Dict, Union
--- a/src/axolotl/core/trainers/grpo/init.py
+++ b/src/axolotl/core/trainers/grpo/init.py
@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc

 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
+from axolotl.utils.schemas.trl import TRLConfig

 LOG = logging.getLogger("axolotl")

@@ -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
--- a/src/axolotl/core/trainers/grpo/args.py
+++ b/src/axolotl/core/trainers/grpo/args.py
@@ -1,6 +1,7 @@
 """
 Axolotl Specific Training Args
 """
+
 from dataclasses import dataclass

 from trl import GRPOConfig
--- a/src/axolotl/core/trainers/grpo/trainer.py
+++ b/src/axolotl/core/trainers/grpo/trainer.py
@@ -1,6 +1,7 @@
 """
 Axolotl GRPO trainer
 """
+
 from accelerate.utils import is_peft_model
 from accelerate.utils.other import is_compiled_module
 from transformers import PreTrainedModel
--- a/src/axolotl/core/trainers/trl.py
+++ b/src/axolotl/core/trainers/trl.py
@@ -1,6 +1,7 @@
 """
 module for TRL PPO training
 """
+
 import torch
 from tqdm import tqdm
 from trl import PPOTrainer
--- a/src/axolotl/core/training_args.py
+++ b/src/axolotl/core/training_args.py
@@ -1,6 +1,7 @@
 """
 extra axolotl specific training args
 """
+
 from dataclasses import dataclass, field
 from typing import Optional

--- a/src/axolotl/evaluate.py
+++ b/src/axolotl/evaluate.py
@@ -8,6 +8,8 @@ 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
@@ -25,18 +27,18 @@ LOG = get_logger("axolotl.evaluate")


 def evaluate_dataset(
-    trainer, dataset, dataset_type: str, flash_optimum: bool = False
+    trainer: Trainer, dataset: Dataset, dataset_type: str, flash_optimum: bool = False
 ) -> Optional[Dict[str, float]]:
-    """Helper function to evaluate a single dataset safely.
+    """Helper function to evaluate a single dataset.

    Args:
-        trainer: The trainer instance
-        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
@@ -63,17 +65,14 @@ def evaluate_dataset(

 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
    """
-    Evaluate a model on training and validation datasets
+    Evaluate a model on training and validation datasets.

    Args:
        cfg: Dictionary mapping `axolotl` config keys to values.
        dataset_meta: Dataset metadata containing training and evaluation datasets.

    Returns:
-        Tuple containing:
-        - The model (either PeftModel or PreTrainedModel)
-        - The tokenizer
-        - Dictionary of evaluation metrics
+        Dictionary mapping metric names to their values.
    """
    # pylint: disable=duplicate-code
    # Enable expandable segments for cuda allocation to improve VRAM usage
--- a/src/axolotl/integrations/config.py
+++ b/src/axolotl/integrations/config.py
@@ -11,19 +11,17 @@
 # the License.

 """
-module to handle merging the plugins' input arguments with the base configurations.
+Module to handle merging the plugins' input arguments with the base configurations.

-this was moved here to prevent circular imports
+This was moved here to prevent circular imports.
 """

 from typing import Any, Dict, List

-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import (
    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import (
-    AxolotlInputConfig as AxolotlInputConfigBase,
-)
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase


 def merge_input_args():
--- a/src/axolotl/integrations/grokfast/init.py
+++ b/src/axolotl/integrations/grokfast/init.py
@@ -1,6 +1,7 @@
 """
 Grokfast plugin for Axolotl
 """
+
 import logging

 from transformers.trainer_callback import TrainerCallback
--- a/src/axolotl/integrations/grokfast/args.py
+++ b/src/axolotl/integrations/grokfast/args.py
@@ -1,6 +1,7 @@
 """
 config args for grokfast plugin
 """
+
 from typing import Optional

 from pydantic import BaseModel
--- a/src/axolotl/integrations/kd/args.py
+++ b/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
+    )
--- a/src/axolotl/integrations/liger/init.py
+++ b/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:
--- a/src/axolotl/integrations/liger/models/deepseekv2.py
+++ b/src/axolotl/integrations/liger/models/deepseekv2.py
@@ -1,6 +1,7 @@
 """
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code

 from typing import List, Optional, Tuple, Union
--- a/src/axolotl/integrations/liger/models/jamba.py
+++ b/src/axolotl/integrations/liger/models/jamba.py
@@ -1,6 +1,7 @@
 """
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code

 from typing import Optional, Tuple, Union
--- a/src/axolotl/integrations/lm_eval/init.py
+++ b/src/axolotl/integrations/lm_eval/init.py
@@ -1,6 +1,7 @@
 """
 Module for the Plugin for LM Eval Harness
 """
+
 import subprocess  # nosec

 from axolotl.integrations.base import BasePlugin
--- a/src/axolotl/integrations/lm_eval/args.py
+++ b/src/axolotl/integrations/lm_eval/args.py
@@ -1,6 +1,7 @@
 """
 Module for handling lm eval harness input arguments.
 """
+
 from typing import List, Optional

 from pydantic import BaseModel
--- a/src/axolotl/integrations/lm_eval/cli.py
+++ b/src/axolotl/integrations/lm_eval/cli.py
@@ -1,6 +1,7 @@
 """
 axolotl CLI for running lm_eval tasks
 """
+
 import subprocess  # nosec
 from collections import defaultdict
 from datetime import datetime
--- a/src/axolotl/kernels/geglu.py
+++ b/src/axolotl/kernels/geglu.py
@@ -5,6 +5,7 @@ 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
--- a/src/axolotl/kernels/lora.py
+++ b/src/axolotl/kernels/lora.py
@@ -6,6 +6,7 @@ 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
--- a/src/axolotl/kernels/quantize.py
+++ b/src/axolotl/kernels/quantize.py
@@ -1,4 +1,5 @@
 """Dequantization utilities for `bitsandbytes` integration."""
+
 # pylint: disable=invalid-name,global-statement

 import ctypes
--- a/src/axolotl/kernels/swiglu.py
+++ b/src/axolotl/kernels/swiglu.py
@@ -5,6 +5,7 @@ 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
--- a/src/axolotl/models/mamba/configuration_mamba.py
+++ b/src/axolotl/models/mamba/configuration_mamba.py
@@ -1,6 +1,7 @@
 """
 HF Transformers MambaConfig
 """
+
 from transformers import PretrainedConfig


--- a/src/axolotl/monkeypatch/attention/mllama.py
+++ b/src/axolotl/monkeypatch/attention/mllama.py
@@ -1,6 +1,7 @@
 """
 Monkeypatch for Vision Llama for FA2 support
 """
+
 # pylint: disable=duplicate-code

 from typing import Optional, Tuple
@@ -220,10 +221,10 @@ 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"]
+    )
--- a/src/axolotl/monkeypatch/data/batch_dataset_fetcher.py
+++ b/src/axolotl/monkeypatch/data/batch_dataset_fetcher.py
@@ -1,4 +1,5 @@
 """monkey patches for the dataset fetcher to handle batches of packed indexes"""
+
 # pylint: disable=protected-access

 import torch
--- a/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
@@ -12,7 +12,9 @@ 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,
 )
@@ -490,9 +492,11 @@ 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,
@@ -531,9 +535,11 @@ 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)
--- a/src/axolotl/monkeypatch/llama_expand_mask.py
+++ b/src/axolotl/monkeypatch/llama_expand_mask.py
@@ -1,6 +1,7 @@
 """
 expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
 """
+
 from typing import Optional

 import torch
--- a/src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
@@ -1,4 +1,5 @@
 """Flash attention monkey patch for mistral model"""
+
 # pylint: disable=duplicate-code

 import logging
@@ -21,7 +22,10 @@ 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

@@ -243,9 +247,11 @@ 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,
@@ -286,9 +292,11 @@ 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)
--- a/src/axolotl/monkeypatch/mixtral/init.py
+++ b/src/axolotl/monkeypatch/mixtral/init.py
@@ -1,6 +1,7 @@
 """
 Patches to support multipack for mixtral
 """
+
 import torch


--- a/src/axolotl/monkeypatch/relora.py
+++ b/src/axolotl/monkeypatch/relora.py
@@ -1,4 +1,5 @@
 """Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
+
 import glob
 import json
 import logging
@@ -411,7 +412,10 @@ 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)
+            in_tensors = torch.load(
+                Path(model_src) / shard_path,
+                weights_only=True,  # to prevent arbitrary code execution
+            )
            if "state_dict" in in_tensors:
                in_tensors = in_tensors["state_dict"]

--- a/src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py
+++ b/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
--- a/src/axolotl/monkeypatch/trainer_fsdp_optim.py
+++ b/src/axolotl/monkeypatch/trainer_fsdp_optim.py
@@ -1,6 +1,7 @@
 """
 fix for FSDP optimizer save in trainer w 4.47.0
 """
+
 import inspect
 import logging

--- a/src/axolotl/monkeypatch/utils.py
+++ b/src/axolotl/monkeypatch/utils.py
@@ -1,6 +1,7 @@
 """
 Shared utils for the monkeypatches
 """
+
 import re
 from typing import Optional, Tuple

--- a/src/axolotl/monkeypatch/xformers_/init.py
+++ b/src/axolotl/monkeypatch/xformers_/init.py
@@ -1,6 +1,7 @@
 """
 Fused MLP layer for incrementally improved training efficiency
 """
+
 import torch
 from transformers.models.llama.modeling_llama import LlamaMLP
 from xformers.ops import SwiGLU
--- a/src/axolotl/prompt_strategies/alpaca_w_system.py
+++ b/src/axolotl/prompt_strategies/alpaca_w_system.py
@@ -1,6 +1,7 @@
 """
 Prompt strategies loader for alpaca instruction datasets with system prompts
 """
+
 from typing import Generator, Tuple, Union

 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
--- a/src/axolotl/prompt_strategies/chat_template.py
+++ b/src/axolotl/prompt_strategies/chat_template.py
@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
+from axolotl.utils.schemas.datasets import DatasetConfig

 # Configure the logger
 LOG = logging.getLogger("axolotl")
--- a/src/axolotl/prompt_strategies/completion.py
+++ b/src/axolotl/prompt_strategies/completion.py
@@ -1,6 +1,7 @@
 """
 Basic completion text
 """
+
 from collections import defaultdict
 from typing import Any, Dict, Generator, Optional, Tuple

--- a/src/axolotl/prompt_strategies/context_qa.py
+++ b/src/axolotl/prompt_strategies/context_qa.py
@@ -1,4 +1,5 @@
 """Module containing the classes for Context QA Prompt Tokenization Strategies"""
+
 from typing import Tuple

 from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
--- a/src/axolotl/prompt_strategies/dpo/init.py
+++ b/src/axolotl/prompt_strategies/dpo/init.py
@@ -1,6 +1,7 @@
 """
 module for DPO style dataset transform strategies
 """
+
 from functools import partial

 from ..base import load as load_base
--- a/src/axolotl/prompt_strategies/dpo/chat_template.py
+++ b/src/axolotl/prompt_strategies/dpo/chat_template.py
@@ -3,7 +3,7 @@ DPO prompt strategies for using tokenizer chat templates.
 """

 from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
-from axolotl.utils.config.models.input.v0_4_1 import handle_legacy_message_fields_logic
+from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic


 def default(
--- a/src/axolotl/prompt_strategies/dpo/chatml.py
+++ b/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
--- a/src/axolotl/prompt_strategies/dpo/llama3.py
+++ b/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
--- a/src/axolotl/prompt_strategies/input_output.py
+++ b/src/axolotl/prompt_strategies/input_output.py
@@ -1,4 +1,5 @@
 """Module for plain input/output prompt pairs"""
+
 from typing import Generator, Tuple

 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
--- a/src/axolotl/prompt_strategies/jinja_template_analyzer.py
+++ b/src/axolotl/prompt_strategies/jinja_template_analyzer.py
@@ -1,4 +1,5 @@
 """Module for inspect jinja templates for the variables they use"""
+
 from typing import Dict, Optional, Set, TypedDict, Union

 from jinja2 import Environment, meta, nodes
--- a/src/axolotl/prompt_strategies/kto/chatml.py
+++ b/src/axolotl/prompt_strategies/kto/chatml.py
@@ -1,6 +1,7 @@
 """
 KTO strategies for chatml
 """
+
 # pylint: disable=duplicate-code


@@ -15,9 +16,9 @@ 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

@@ -33,9 +34,9 @@ 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

@@ -55,9 +56,9 @@ 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

@@ -74,9 +75,9 @@ 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

@@ -96,9 +97,9 @@ 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

--- a/src/axolotl/prompt_strategies/kto/llama3.py
+++ b/src/axolotl/prompt_strategies/kto/llama3.py
@@ -1,6 +1,7 @@
 """
 KTO strategies for llama-3 chat template
 """
+
 # pylint: disable=duplicate-code


@@ -15,9 +16,9 @@ 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

@@ -33,9 +34,9 @@ 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

@@ -55,9 +56,9 @@ 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

@@ -74,9 +75,9 @@ 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

@@ -96,9 +97,9 @@ 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

--- a/src/axolotl/prompt_strategies/kto/user_defined.py
+++ b/src/axolotl/prompt_strategies/kto/user_defined.py
@@ -1,6 +1,7 @@
 """
 User-defined KTO strategies
 """
+
 # pylint: disable=duplicate-code


--- a/src/axolotl/prompt_strategies/messages/chat.py
+++ b/src/axolotl/prompt_strategies/messages/chat.py
@@ -1,6 +1,7 @@
 """
 Chat dataset wrapping strategy for new internal messages representations
 """
+
 from typing import Any, Callable, Dict, Optional

 from axolotl.core.datasets.chat import TokenizedChatDataset
--- a/src/axolotl/prompt_strategies/orcamini.py
+++ b/src/axolotl/prompt_strategies/orcamini.py
@@ -9,6 +9,7 @@ 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
--- a/src/axolotl/prompt_strategies/orpo/chat_template.py
+++ b/src/axolotl/prompt_strategies/orpo/chat_template.py
@@ -1,4 +1,5 @@
 """chatml prompt tokenization strategy for ORPO"""
+
 from typing import Any, Dict, Generator, List, Optional, Tuple

 from pydantic import BaseModel
--- a/src/axolotl/prompt_strategies/pretrain.py
+++ b/src/axolotl/prompt_strategies/pretrain.py
@@ -1,4 +1,5 @@
 """pretraining prompt strategies"""
+
 from typing import Generator

 from transformers import BatchEncoding
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -406,9 +406,7 @@ 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,
--- a/src/axolotl/utils/init.py
+++ b/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"
+            )
--- a/src/axolotl/utils/bench.py
+++ b/src/axolotl/utils/bench.py
@@ -1,4 +1,5 @@
 """Benchmarking and measurement utilities"""
+
 import functools

 import torch
--- a/src/axolotl/utils/callbacks/init.py
+++ b/src/axolotl/utils/callbacks/init.py
@@ -33,7 +33,6 @@ from trl.models import unwrap_model_for_generation
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.callbacks.perplexity import Perplexity
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 from axolotl.utils.distributed import (
    barrier,
    broadcast_dict,
@@ -43,6 +42,7 @@ from axolotl.utils.distributed import (
    is_main_process,
    zero_first,
 )
+from axolotl.utils.schemas.config import AxolotlInputConfig

 if TYPE_CHECKING:
    from axolotl.core.trainer_builder import AxolotlTrainingArguments
@@ -343,9 +343,9 @@ def bench_eval_callback_factory(trainer, tokenizer):
                    bench_refs.extend(combined_bench_names[bench_name]["refs"])
                    bench_preds.extend(combined_bench_names[bench_name]["preds"])
                    if not pd.isna(bench_score):
-                        results[
-                            f"{bench_split}_bench_accuracy_{bench_name}"
-                        ] = bench_score
+                        results[f"{bench_split}_bench_accuracy_{bench_name}"] = (
+                            bench_score
+                        )
                        bench_scores.append(bench_score)
                    else:
                        results[f"{bench_split}_bench_accuracy_{bench_name}"] = 0.0
--- a/src/axolotl/utils/callbacks/mlflow_.py
+++ b/src/axolotl/utils/callbacks/mlflow_.py
@@ -1,4 +1,5 @@
 """MLFlow module for trainer callbacks"""
+
 import logging
 from shutil import copyfile
 from tempfile import NamedTemporaryFile
--- a/src/axolotl/utils/callbacks/perplexity.py
+++ b/src/axolotl/utils/callbacks/perplexity.py
@@ -1,4 +1,5 @@
 """callback to calculate perplexity as an evaluation metric."""
+
 from typing import Dict, List, Optional

 import torch
--- a/src/axolotl/utils/callbacks/profiler.py
+++ b/src/axolotl/utils/callbacks/profiler.py
@@ -1,6 +1,7 @@
 """
 HF Trainer callback for creating pytorch profiling snapshots
 """
+
 from pathlib import Path
 from pickle import dump  # nosec B403

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Dan Saunders	0bffef25d0	installing axolotl prior to quartodoc build	2025-03-21 16:51:02 +00:00
Dan Saunders	94c00c1d04	pre-commit	2025-03-21 11:23:39 -04:00
Dan Saunders	ddd84d7c65	update pylint	2025-03-21 11:18:59 -04:00
Dan Saunders	42bdf0bd74	update pre-commit version	2025-03-21 11:18:59 -04:00
Dan Saunders	b03d96a228	include quartodoc build step	2025-03-21 11:18:59 -04:00
Dan Saunders	2653f170fc	fix accidental change	2025-03-21 11:18:59 -04:00
Dan Saunders	3bfcce9f0a	shrinking header sizes	2025-03-21 11:18:59 -04:00
Dan Saunders	8feb746953	fix	2025-03-21 11:18:59 -04:00
Dan Saunders	a563815fe7	pydantic models refactor + add to autodoc + fixes	2025-03-21 11:18:58 -04:00
Dan Saunders	81f2203151	update to reflect recent changes	2025-03-21 11:12:09 -04:00
Dan Saunders	5b7e688fc5	fix broken link	2025-03-21 11:12:09 -04:00
Dan Saunders	5134aa66cd	moving reference up near the top of the sidebar	2025-03-21 11:12:09 -04:00
Dan Saunders	ba9a867adb	more autodoc progress	2025-03-21 11:12:09 -04:00
Dan Saunders	c618f42c39	Fix	2025-03-21 11:12:09 -04:00
Dan Saunders	fc1f985296	Update docs/.gitignore to exclude auto-generated API documentation files	2025-03-21 11:12:09 -04:00
Dan Saunders	a5e37f183c	deletions	2025-03-21 11:12:09 -04:00
Dan Saunders	e6a7bbe9ff	quartodoc progress	2025-03-21 11:12:09 -04:00
Dan Saunders	e4fd7aad0b	quartodoc integration	2025-03-21 11:12:09 -04:00
Dan Saunders	c907ac173e	adding pre-commit auto-update GH action and bumping plugin versions (#2428 ) * adding pre-commit auto-update GH action and bumping plugin versions * running updated pre-commit plugins * sorry to revert, but pylint complained * Update .pre-commit-config.yaml Co-authored-by: Wing Lian <wing.lian@gmail.com> --------- Co-authored-by: Dan Saunders <dan@axolotl.ai> Co-authored-by: Wing Lian <wing.lian@gmail.com>	2025-03-21 11:02:43 -04:00
salman	187227d837	Fixing KTO+QLoRA+multi-GPU (#2420 ) * WIP * removing artifacts * adding error * adding adapter check * linting * simplifying check * linting v2 * config fix -___-	2025-03-21 10:18:28 -04:00
NanoCode012	f8de8bb4f2	chore(doc): add instructions on adding custom integrations (#2422 ) [skip ci] * chore(doc): add instructions on adding custom integrations * chore: add warning help * feat: add note about integration path * fix: adjust text per suggestion	2025-03-21 10:18:01 -04:00
hugo	8e604848a4	add run on novita ai (#2421 ) [skip ci] * add run on novita ai * Revert "add run on novita ai" This reverts commit `4d5df1ac6b`. * add run axolotl on novita ai	2025-03-21 10:17:47 -04:00
Wing Lian	aae4337f40	add 12.8.1 cuda to the base matrix (#2426 ) * add 12.8.1 cuda to the base matrix * use nightly * bump deepspeed and set no binary * deepspeed binary fixes hopefully * install deepspeed by itself * multiline fix * make sure ninja is installed * try with reversion of packaging/setuptools/wheel install * use license instead of license-file * try rolling back packaging and setuptools versions * comment out license for validation for now * make sure packaging version is consistent * more parity across tests and docker images for packaging/setuptools	2025-03-21 10:17:25 -04:00
Wing Lian	38df5a36ea	bump HF versions except for trl (#2427 )	2025-03-20 10:22:05 -04:00
Wing Lian	4d92a68a96	use default torch fused adamw optimizer as default as adamw_hf is deprecated (#2425 ) * use default torch fused adamw optimizer as default as adamw_hf is deprecated * make sure to have latest packaging installed * bump packagingin requirements.txt too	2025-03-19 23:58:33 -04:00