Merge branch 'main' into kwargs-refactor

.axolotl-complete.bash

@@ -1,41 +0,0 @@
-#!/bin/bash
-
-_axolotl_completions() {
-    local cur prev
-    COMPREPLY=()
-    cur="${COMP_WORDS[COMP_CWORD]}"
-    prev="${COMP_WORDS[COMP_CWORD-1]}"
-
-    # If we're completing the first argument (the command)
-    if [[ $COMP_CWORD -eq 1 ]]; then
-        mapfile -t COMPREPLY < <(compgen -W "delinearize-llama4 fetch lm-eval merge-sharded-fsdp-weights quantize vllm-serve evaluate inference merge-lora preprocess train" -- "$cur")
-        return 0
-    fi
-
-    # Commands that should complete with directories and YAML files
-    local -a yaml_commands=("merge-sharded-fsdp-weights" "quantize" "vllm-serve" "evaluate" "inference" "merge-lora" "preprocess" "train")
-
-    # Check if previous word is in our list
-    if [[ " ${yaml_commands[*]} " =~ (^|[[:space:]])$prev($|[[:space:]]) ]]; then
-        # Use filename completion which handles directories properly
-        compopt -o filenames
-        mapfile -t COMPREPLY < <(compgen -f -- "$cur")
-
-        # Filter to only include directories and YAML files
-        local -a filtered=()
-        for item in "${COMPREPLY[@]}"; do
-            if [[ -d "$item" ]] || [[ "$item" == *.yaml ]] || [[ "$item" == *.yml ]]; then
-                filtered+=("$item")
-            fi
-        done
-        COMPREPLY=("${filtered[@]}")
-
-        return 0
-    fi
-
-    # Default: no completion
-    return 0
-}
-
-# Remove the -o nospace option - let filenames handle it
-complete -F _axolotl_completions axolotl

.coderabbit.yaml

@@ -1,16 +0,0 @@
-# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
-language: "en-US"
-early_access: false
-reviews:
-  profile: "chill"
-  request_changes_workflow: false
-  high_level_summary: true
-  review_status: true
-  collapse_walkthrough: true
-  poem: false
-  sequence_diagrams: false
-  auto_review:
-    enabled: true
-    drafts: false
-chat:
-  auto_reply: true

.github/workflows/base.yml

@@ -17,7 +17,7 @@ on:
 
 jobs:
   build-base:
-    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
+    if: github.repository_owner == 'axolotl-ai-cloud'
     timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
     runs-on: ubuntu-latest-m
@@ -108,7 +108,7 @@ jobs:
             PYTORCH_VERSION=${{ matrix.pytorch }}
             TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
   build-base-uv:
-    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
+    if: github.repository_owner == 'axolotl-ai-cloud'
     timeout-minutes: 480
     runs-on: ubuntu-latest-m
     strategy:

.github/workflows/lint.yml

@@ -3,7 +3,6 @@ on:
   # check on PRs, and manual triggers
   merge_group:
   pull_request:
-      types: [opened, synchronize, reopened, ready_for_review]
       paths:
        - '**.py'
        - 'requirements.txt'
@@ -17,7 +16,6 @@ jobs:
   pre-commit:
     name: pre-commit
     runs-on: ubuntu-latest
-    if: ${{ !github.event.pull_request.draft }}
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5

.github/workflows/main.yml

@@ -87,6 +87,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+            is_latest: true
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -97,7 +98,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
-            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"

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

@@ -21,7 +21,7 @@ concurrency:
 
 jobs:
   test-axolotl-multigpu:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     strategy:
       fail-fast: false
       matrix:
@@ -37,14 +37,14 @@ jobs:
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
-            axolotl_extras:
+            axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
-            axolotl_extras: vllm
+            axolotl_extras:
             num_gpus: 2
             nightly_build: "true"
     runs-on: [self-hosted, modal]

.github/workflows/preview-docs.yml

@@ -2,7 +2,7 @@ name: Preview
 on:
   workflow_dispatch:
   pull_request:
-    types: [opened, synchronize, reopened, ready_for_review]
+    types: [opened, synchronize, reopened]
 
     # Run the workflow only when one of these files changes
     paths:
@@ -25,7 +25,6 @@ permissions:
 jobs:
   preview:
     runs-on: ubuntu-latest
-    if: ${{ !github.event.pull_request.draft }}
     steps:
       - name: Check out repository
         uses: actions/checkout@v4
@@ -53,7 +52,6 @@ jobs:
 
       - name: Netlify Publish
         uses: nwtgck/actions-netlify@v3.0
-        if: ${{ secrets.NETLIFY_AUTH_TOKEN != '' }}
         id: netlify
         with:
           publish-dir: './_site'
@@ -68,7 +66,7 @@ jobs:
           NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
 
       - name: Update PR with preview link
-        if: ${{ steps.netlify.outcome == 'success' && secrets.NETLIFY_AUTH_TOKEN != '' }}
+        if: ${{ steps.netlify.outcome == 'success' }}
         uses: marocchino/sticky-pull-request-comment@v2
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/tests-nightly.yml

@@ -52,7 +52,7 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Update requirements.txt
         run: |
@@ -106,13 +106,6 @@ jobs:
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.7.1
-            num_gpus: 1
-            axolotl_extras:
-            nightly_build: "true"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -137,45 +130,3 @@ jobs:
       - name: Run tests job on Modal
         run: |
           modal run cicd.e2e_tests
-  docker-e2e-multigpu-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
-    # this job needs to be run on self-hosted GPU runners...
-    runs-on: [self-hosted, modal]
-    timeout-minutes: 120
-    needs: [pre-commit, pytest, docker-e2e-tests]
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.7.1
-            num_gpus: 2
-            axolotl_extras:
-            nightly_build: "true"
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install Modal
-        run: |
-          python -m pip install --upgrade pip
-          pip install modal==1.0.2 jinja2
-      - name: Update env vars
-        run: |
-          echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
-          echo "PYTORCH_VERSION=${{ matrix.pytorch}}" >> $GITHUB_ENV
-          echo "AXOLOTL_ARGS=${{ matrix.axolotl_args}}" >> $GITHUB_ENV
-          echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
-          echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
-          echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
-          echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
-      - name: Run tests job on Modal
-        run: |
-          modal run cicd.multigpu

.github/workflows/tests.yml

@@ -13,7 +13,6 @@ on:
       - 'cicd/cicd.sh'
       - 'cicd/Dockerfile.jinja'
   pull_request:
-      types: [opened, synchronize, reopened, ready_for_review]
       paths:
        - '**.py'
        - 'requirements.txt'
@@ -35,7 +34,6 @@ jobs:
   pre-commit:
     name: pre-commit
     runs-on: ubuntu-latest
-    if: ${{ !github.event.pull_request.draft }}
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
@@ -49,7 +47,6 @@ jobs:
   pytest:
     name: PyTest
     runs-on: ubuntu-latest
-    if: ${{ !github.event.pull_request.draft }}
 #    needs: [preload-cache]
     strategy:
       fail-fast: false
@@ -81,7 +78,7 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Install dependencies
         run: |
@@ -124,7 +121,6 @@ jobs:
   pytest-sdist:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
-    if: ${{ !github.event.pull_request.draft }}
     strategy:
       fail-fast: false
       matrix:
@@ -155,7 +151,7 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install torch==${{ matrix.pytorch_version }}
 
       - name: Install dependencies
         run: |
@@ -189,7 +185,7 @@ jobs:
 
   docker-e2e-tests-1st:
     # Run this job first as a gate for running the remainder of the test matrix
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
@@ -239,7 +235,7 @@ jobs:
           modal run cicd.e2e_tests
 
   docker-e2e-tests:
-    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: github.repository_owner == 'axolotl-ai-cloud'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
@@ -293,7 +289,6 @@ jobs:
     runs-on: [self-hosted, modal]
     timeout-minutes: 90
     needs: [docker-e2e-tests]
-    if: ${{ !github.event.pull_request.draft }}
 
     strategy:
       fail-fast: false

.pre-commit-config.yaml

@@ -27,7 +27,7 @@ repos:
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.0
+    rev: v1.16.1
     hooks:
     - id: mypy
       additional_dependencies:

.runpod/README.md

@@ -119,15 +119,14 @@ datasets:
 
 ## Dataset Processing
 
-| Option                            | Default                    | Description                         |
-| --------------------------------- | -------------------------- | ----------------------------------- |
-| `dataset_prepared_path`           | `"data/last_run_prepared"` | Path for prepared dataset           |
-| `push_dataset_to_hub`             | `""`                       | Push dataset to HF hub              |
-| `dataset_processes`               | `4`                        | Number of preprocessing processes   |
-| `dataset_keep_in_memory`          | `false`                    | Keep dataset in memory              |
-| `shuffle_merged_datasets`         | `true`                     | Shuffle merged datasets             |
-| `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |
-| `dataset_exact_deduplication`     | `true`                     | Deduplicate datasets                |
+| Option                        | Default                    | Description                       |
+| ----------------------------- | -------------------------- | --------------------------------- |
+| `dataset_prepared_path`       | `"data/last_run_prepared"` | Path for prepared dataset         |
+| `push_dataset_to_hub`         | `""`                       | Push dataset to HF hub            |
+| `dataset_processes`           | `4`                        | Number of preprocessing processes |
+| `dataset_keep_in_memory`      | `false`                    | Keep dataset in memory            |
+| `shuffle_merged_datasets`     | `true`                     | Shuffle merged datasets           |
+| `dataset_exact_deduplication` | `true`                     | Deduplicate datasets              |
 
 ## LoRA Configuration
 

README.md

@@ -25,8 +25,6 @@
 
 ## 🎉 Latest Updates
 
-- 2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the [docs](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral)!
-- 2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!
 - 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!
 - 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
 - 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version!
@@ -81,20 +79,6 @@ docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
 
 Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
 
-#### Cloud Providers
-
-<details>
-
-- [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-- [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=github&utm_medium=developer_community&utm_campaign=template_launch_axolotl&utm_content=readme)
-- [PRIME Intellect](https://app.primeintellect.ai/dashboard/create-cluster?image=axolotl&location=Cheapest&security=Cheapest&show_spot=true)
-- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl)
-- [Novita](https://novita.ai/gpus-console?templateId=311)
-- [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
-- [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
-
-</details>
-
 ### Your First Fine-tune
 
 ```bash
@@ -136,6 +120,12 @@ Contributions are welcome! Please see our [Contributing Guide](https://github.co
 
 ## ❤️ Sponsors
 
+Thank you to our sponsors who help make Axolotl possible:
+
+- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl) - Modal lets you run
+jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale,
+fine-tune large language models, run protein folding simulations, and much more.
+
 Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)
 
 ## 📜 License

_quarto.yml

@@ -268,8 +268,6 @@ website:
             - docs/batch_vs_grad.qmd
             - docs/dataset_preprocessing.qmd
             - docs/multipack.qmd
-            - docs/mixed_precision.qmd
-            - docs/gradient_accumulation.qmd
 
         - section: "Advanced Features"
           contents:

cicd/Dockerfile-uv.jinja

@@ -11,7 +11,7 @@ ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 ENV HF_HOME="{{ HF_HOME }}"
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
 
 WORKDIR /workspace
 

cicd/Dockerfile.jinja

@@ -12,7 +12,7 @@ ENV HF_HOME="{{ HF_HOME }}"
 ENV AXOLOTL_DATASET_PROCESSES="8"
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev
 
 WORKDIR /workspace
 

cicd/multigpu.sh

@@ -19,7 +19,5 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
   --cov-append \
   --cov-report=xml:multigpu-coverage.xml
 
-# Upload coverage to Codecov if CODECOV_TOKEN is available
-if [ -n "$CODECOV_TOKEN" ]; then
-  codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
-fi
+# Upload coverage to Codecov
+codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true

codecov.yml

@@ -22,7 +22,6 @@ coverage:
         only_pulls: true
         flags: null
         paths: null
-        informational: true
     patch:
       default:
         # basic

deepspeed_configs/zero3.json

@@ -7,9 +7,9 @@
     "reduce_bucket_size": "auto",
     "stage3_prefetch_bucket_size": "auto",
     "stage3_param_persistence_threshold": "auto",
-    "max_live_parameters": 0,
-    "max_reuse_distance": 0,
-    "gather_16bit_weights_on_model_save": true
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
   },
   "bf16": {
     "enabled": "auto"

deepspeed_configs/zero3_bf16.json

@@ -7,9 +7,9 @@
     "reduce_bucket_size": "auto",
     "stage3_prefetch_bucket_size": "auto",
     "stage3_param_persistence_threshold": "auto",
-    "max_live_parameters": 0,
-    "max_reuse_distance": 0,
-    "gather_16bit_weights_on_model_save": true
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
   },
   "bf16": {
     "enabled": true

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -17,9 +17,9 @@
     "reduce_bucket_size": "auto",
     "stage3_prefetch_bucket_size": "auto",
     "stage3_param_persistence_threshold": "auto",
-    "max_live_parameters": 0,
-    "max_reuse_distance": 0,
-    "gather_16bit_weights_on_model_save": true
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
   },
   "bf16": {
     "enabled": true

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -13,9 +13,9 @@
     "reduce_bucket_size": "auto",
     "stage3_prefetch_bucket_size": "auto",
     "stage3_param_persistence_threshold": "auto",
-    "max_live_parameters": 0,
-    "max_reuse_distance": 0,
-    "gather_16bit_weights_on_model_save": true
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
   },
   "bf16": {
     "enabled": true

docker/Dockerfile

@@ -10,9 +10,7 @@ ARG PYTORCH_VERSION="2.1.2"
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/*
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev rsync s3fs
 
 WORKDIR /workspace
 
@@ -25,17 +23,17 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
-    fi && \
-    python scripts/unsloth_install.py | sh && \
-    python scripts/cutcrossentropy_install.py | sh && \
-    pip install pytest && \
-    pip cache purge
+    fi
 
-# fix so that git fetch/pull from remote works with shallow clone
+RUN python scripts/unsloth_install.py | sh
+RUN python scripts/cutcrossentropy_install.py | sh
+
+# So we can test the Docker image
+RUN pip install pytest
+
+# fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch && \
-    git config --global credential.helper store
+    git config --get remote.origin.fetch
 
-COPY .axolotl-complete.bash /root/.axolotl-complete.bash
-RUN chmod +x /root/.axolotl-complete.bash && \
-    echo 'source /root/.axolotl-complete.bash' >> ~/.bashrc
+# helper for huggingface-login cli
+RUN git config --global credential.helper store

docker/Dockerfile-base

@@ -16,16 +16,12 @@ 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/cache/apt/archives \
-    && rm -rf /var/lib/apt/lists/* \
+    && 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 tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main \
-    && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r \
     && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
 
 ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
@@ -35,14 +31,12 @@ WORKDIR /workspace
 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} torchvision --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" && \
-    python3 -m pip cache purge
+    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 && \
-    pip3 cache purge
+    pip3 install -U --no-cache-dir pydantic==1.10.10
 
 RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
         FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \

docker/Dockerfile-base-nightly

@@ -22,22 +22,18 @@ RUN apt-get update \
     && mkdir /root/.conda \
     && bash Miniconda3-latest-Linux-x86_64.sh -b \
     && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main \
-    && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r \
     && 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 -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 --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" && \
-    python3 -m pip cache purge
+    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 && \
-    pip3 cache purge
+    pip3 install -U --no-cache-dir pydantic==1.10.10

docker/Dockerfile-cloud

@@ -14,10 +14,7 @@ COPY scripts/motd /etc/motd
 
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
-RUN apt update && \
-    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/* && \
+RUN apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
     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 && \

docker/Dockerfile-cloud-no-tmux

@@ -9,15 +9,13 @@ ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 EXPOSE 8888
 EXPOSE 22
 
-COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
+COPY scripts/cloud-entrypoint-term.sh /root/cloud-entrypoint.sh
 COPY scripts/motd /etc/motd
 
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
-RUN apt update && \
-    apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm && \
-    rm -rf /var/cache/apt/archives && \
-    rm -rf /var/lib/apt/lists/* && \
+RUN apt install --yes --no-install-recommends openssh-server tmux sudo && \
+    pip3 install -U --no-cache-dir grpcio ray[default]==2.9.3 && \
     mkdir -p ~/.ssh && \
     chmod 700 ~/.ssh && \
     printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \

docs/faq.qmd

@@ -136,7 +136,3 @@ description: Frequently asked questions
 >   dynamic: false
 >   mode: max-autotune-no-cudagraphs
 > ```
-
-**Q: `ValueError("Backward pass should have cleared tracker of all tensors")`
-
-> A: This may happen due to edge cases in using the modern OffloadActivations context manager for CUDA streams. If you encounter this error, you may have success using the naive implementation with `offload_activations: legacy` in your YAML.

docs/installation.qmd

@@ -124,13 +124,10 @@ For providers supporting Docker:
 
 - Use `axolotlai/axolotl-cloud:main-latest`
 - Available on:
-    - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
-    - [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=axolotl&utm_medium=partner&utm_campaign=template_launch_july2025&utm_content=docs_link)
-    - [PRIME Intellect](https://app.primeintellect.ai/dashboard/create-cluster?image=axolotl&location=Cheapest&security=Cheapest&show_spot=true)
-    - [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl)
-    - [Novita](https://novita.ai/gpus-console?templateId=311)
-    - [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)
-    - [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)
+  - [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}
 

docs/mixed_precision.qmd

@@ -1,149 +0,0 @@
----
-title: "Mixed Precision Training"
-format:
-  html:
-    toc: true
-    toc-depth: 3
-    number-sections: true
-    code-tools: true
-execute:
-  enabled: false
----
-
-Mixed precision training uses lower precision data types to reduce memory usage and increase training speed while maintaining model quality. Axolotl supports several mixed precision formats:
-
-- **FP16** - Half precision 16-bit (Pascal generation+)
-- **BF16** - Brain Float 16-bit (Ampere generation+)
-- **FP8** - 8-bit floating point (Hopper generation+)
-
-## FP16 Mixed Precision {#sec-fp16}
-
-### Overview {#sec-fp16-overview}
-
-FP16 is the traditional half-precision format, supported on older GPUs but can be less numerically stable than BF16.
-
-### Configuration {#sec-fp16-config}
-
-```{.yaml}
-fp16: true
-```
-
-### FP16 Considerations {#sec-fp16-considerations}
-
-- May require gradient scaling to prevent underflow
-- Less numerically stable than BF16
-- Can cause training instability with some model architectures
-- Consider using BF16 if your hardware supports it
-
-## BF16 Mixed Precision {#sec-bf16}
-
-### Overview {#sec-bf16-overview}
-
-BF16 (Brain Float 16) offers better numerical stability than FP16 and is the recommended mixed precision format for modern GPUs. It provides the same dynamic range as FP32 while using half the memory.
-
-### Configuration {#sec-bf16-config}
-
-```{.yaml}
-# Automatic BF16 detection (recommended)
-bf16: auto
-
-# Or explicitly enable
-bf16: true
-
-# For evaluation with BF16
-bf16: full  # Equivalent to bf16_full_eval in the HF trainer
-```
-
-## FP8 Mixed Precision {#sec-fp8}
-
-::: {.callout-note}
-FP8 support is experimental and requires compatible hardware (H100, H200) and recent PyTorch versions with TorchAO.
-:::
-
-### What is FP8? {#sec-fp8-overview}
-
-FP8 (8-bit floating point) can provide significant time savings compared to FP16/BF16 while maintaining training stability. Axolotl's implementation uses PyTorch's TorchAO library with "tensorwise" scaling strategy.
-
-### Requirements {#sec-fp8-software}
-
-- Hopper+ GPUs (H100/H200)
-- PyTorch 2.7+ (+ compatible TorchAO version)
-- CUDA 12.4+
-
-### Configuration {#sec-fp8-config}
-
-Add to your YAML config:
-
-```{.yaml}
-# Enable FP8 mixed precision
-fp8: true
-
-# Optional: Enable FP8 for FSDP all-gather operations
-fp8_enable_fsdp_float8_all_gather: true
-
-# Enable torch.compile (almost always necessary for FP8 speedups)
-torch_compile: true
-```
-
-::: {.callout-important}
-**torch.compile is critical for FP8 performance**
-
-FP8 training requires `torch_compile: true` to see meaningful speedups. Without compilation, FP8 may actually be slower and use more memory than FP16/BF16.
-:::
-
-### Advanced FP8 Configs {#sec-fp8-advanced}
-
-For [FSDP](multi-gpu.qmd#sec-fsdp) (Fully Sharded Data Parallel) training:
-
-```{.yaml}
-fp8: true
-fp8_enable_fsdp_float8_all_gather: true
-
-torch_compile: true
-
-# FSDP configuration
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  cpu_ram_efficient_loading: true
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  reshard_after_forward: true
-```
-
-## Best Practices {#sec-best-practices}
-
-### Choosing Precision Format {#sec-choosing-format}
-
-- **Start with automatic detection**: `bf16: auto`
-- **For Hopper+ (H100/H200)**: Try FP8 + torch.compile for maximum speed
-- **For Ampere (A100/RTX 30/40)**: Use BF16
-- **For older Pascal/Turing GPUs**: Use FP16 with caution
-- **For very old or unsupported GPUs**: Use FP32
-
-### Validation and Testing {#sec-validation}
-
-Always validate your mixed precision setup:
-
-- **Start with a small dataset** to verify stability
-- **Monitor loss curves** for irregularities
-- **Compare with FP32 baseline** when possible
-- **Test evaluation metrics** match expectations
-
-### FP8 Particulars {#sec-fp8-details}
-
-- Use cases
-  - Single GPU training
-  - Multi GPU training with FSDP2 or Deepspeed
-- Speedups
-  - Please refer to the [TorchAO FP8 training benchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#rowwise-scaling) for expected matmul speedups for different (M, K, N) settings
-  - Concrete number for LLaMA 3 8B training can be found [here](https://github.com/pytorch/ao/tree/main/torchao/float8#training-benchmarks)
-- Known issues:
-  - FP8 + DDP + `torch.compile` (causes [error](https://gist.github.com/djsaunde/0c1664c32e44a64d31b5e01b4aafe5c4))
-  - FP8 + FSDP2 + `torch.compile` + FSDP2 activation checkpointing tends to be _slower_ than the BF16 equivalent training
-  - Flash Attention 2 does not play nicely with `torch.compile`
-
-See `examples/llama-3/3b-fp8-fsdp2.yaml` for an optimized example config. Enabling FP8 mixed precision + FP8 all-gather training results in ~10% faster iterations per second vs. BF16 for a relatively small (3B param) model
-
-For more information on multi-GPU training, see our [Multi-GPU guide](multi-gpu.qmd).

docs/multi-gpu.qmd

@@ -98,8 +98,8 @@ fsdp_cpu_ram_efficient_loading | cpu_ram_efficient_loading
 fsdp_state_dict_type | state_dict_type
 fsdp_use_orig_params | **REMOVED**
 
-For more details, please see the migration guide in the [torchtitan repo](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md). In Axolotl,
-if you were using the following FSDP1 config:
+
+For example, if you were using the following FSDP1 config:
 
 ```{.yaml}
 fsdp_version: 1

docs/multimodal.qmd

@@ -14,7 +14,6 @@ format:
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
 - [Gemma-3](#sec-gemma-3)
-- [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
 
@@ -111,22 +110,6 @@ base_model: google/gemma-3-4b-it
 chat_template: gemma3
 ```
 
-### Gemma-3n {#sec-gemma-3n}
-
-::: {.callout-warning}
-The model's initial loss and grad norm will be very high. We suspect this to be due to the Conv in the vision layers.
-:::
-
-::: {.callout-tip}
-Please make sure to install `timm` via `pip3 install timm==1.0.17`
-:::
-
-```yaml
-base_model: google/gemma-3n-E2B-it
-
-chat_template: gemma3n
-```
-
 ### Qwen2-VL {#sec-qwen2-vl}
 
 ```yaml
@@ -149,9 +132,7 @@ For multi-modal datasets, we adopt an extended `chat_template` format similar to
 
 - A message is a list of `role` and `content`.
 - `role` can be `system`, `user`, `assistant`, etc.
-- `content` is a list of `type` and (`text`, `image`, `path`, `url`, `base64`, or `audio`).
-
-### Image
+- `content` is a list of `type` and (`text` or `image` or `path` or `url` or `base64`).
 
 ::: {.callout-note}
 For backwards compatibility:
@@ -160,29 +141,15 @@ For backwards compatibility:
 - If `content` is a string, it will be converted to a list with `type` as `text`.
 :::
 
+::: {.callout-tip}
 For image loading, you can use the following keys within `content` alongside `"type": "image"`:
 
 - `"path": "/path/to/image.jpg"`
 - `"url": "https://example.com/image.jpg"`
 - `"base64": "..."`
 - `"image": PIL.Image`
-
-### Audio
-
-For audio loading, you can use the following keys within `content` alongside `"type": "audio"`:
-
-- `"path": "/path/to/audio.mp3"`
-- `"url": "https://example.com/audio.mp3"`
-- `"audio": np.ndarray`
-
-::: {.callout-tip}
-
-You may need to install `librosa` via `pip3 install librosa==0.11.0`.
-
 :::
 
-### Example
-
 Here is an example of a multi-modal dataset:
 ```json
 [
@@ -211,9 +178,3 @@ Here is an example of a multi-modal dataset:
   }
 ]
 ```
-
-## FAQ
-
-1. `PIL.UnidentifiedImageError: cannot identify image file ...`
-
-`PIL` could not retrieve the file at `url` using `requests`. Please check for typo. One alternative reason is that the request is blocked by the server.

examples/alst/README.md

@@ -1,9 +0,0 @@
-# Arctic Long Sequence Training (ALST)
-
-Artic Long Sequence Training (ALST) is a technique for training long context models using a variety of optimization
-techniques. It is a combination of:
-- TiledMLP: Leverage tiling over the sequence dimension on MLP layers to reduce memory usage
-- Tiled Loss: Using optimized loss functions like Liger-Kernel or Cut Cross Entropy to reduce memory usage
-- Activation Offloading: Offload activations to CPU RAM to reduce memory usage
-
-For more information, you can check out the ALST paper [here](https://www.arxiv.org/abs/2506.13996).

examples/alst/llama3-8b-deepspeed-alst.yaml

@@ -1,53 +0,0 @@
-base_model: meta-llama/Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: togethercomputer/Long-Data-Collections
-    type: completion
-    field: text
-    data_files:
-      - pretrain/rp_sub.jsonl.zst
-  - path: princeton-nlp/TextbookChapters
-    type: completion
-    field: chapter
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 500_000
-min_sample_len: 200_000
-sample_packing: true
-
-tiled_mlp: true
-sequence_parallel_degree: 8
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-activation_offloading: legacy
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 100
-saves_per_epoch: 1
-evals_per_epoch: 2
-weight_decay: 0.0
-special_tokens:
-  pad_token: <|end_of_text|>
-
-deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/alst/llama3-8b-fsdp2-alst.yaml

@@ -1,59 +0,0 @@
-base_model: meta-llama/Llama-3.1-8B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: togethercomputer/Long-Data-Collections
-    type: completion
-    field: text
-    data_files:
-      - pretrain/rp_sub.jsonl.zst
-  - path: princeton-nlp/TextbookChapters
-    type: completion
-    field: chapter
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 500_000
-min_sample_len: 200_000
-sample_packing: true
-
-tiled_mlp: true
-context_parallel_size: 8
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_8bit
-lr_scheduler: cosine
-learning_rate: 2e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-activation_offloading: legacy
-
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 100
-saves_per_epoch: 1
-evals_per_epoch: 2
-weight_decay: 0.0
-special_tokens:
-  pad_token: <|end_of_text|>
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false  # offloading is currently not compatible with SP + torchao optimizer
-  state_dict_type: SHARDED_STATE_DICT
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  reshard_after_forward: true
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/archived/cerebras/btlm-ft.yml

@@ -66,7 +66,7 @@ flash_optimum:
 gptq_groupsize:
 gptq_model_v1:
 
-warmup_ratio: 0.1
+warmup_steps: 32
 evals_per_epoch: 4
 saves_per_epoch: 1
 save_total_limit:

examples/archived/cerebras/qlora.yml

@@ -43,7 +43,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/code-llama/13b/lora.yml

@@ -17,7 +17,7 @@ output_dir: ./outputs/lora-out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/13b/qlora.yml

@@ -20,7 +20,7 @@ lora_model_dir:
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16
@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/34b/lora.yml

@@ -17,7 +17,7 @@ output_dir: ./outputs/lora-out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/34b/qlora.yml

@@ -20,7 +20,7 @@ lora_model_dir:
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16
@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/7b/lora.yml

@@ -17,7 +17,7 @@ output_dir: ./outputs/lora-out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/code-llama/7b/qlora.yml

@@ -20,7 +20,7 @@ lora_model_dir:
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16
@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/dbrx/16bit-lora.yaml

@@ -54,7 +54,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/dbrx/8bit-lora.yaml

@@ -57,7 +57,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/dbrx/fft-ds-zero3.yaml

@@ -41,7 +41,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/archived/deepcoder/deepcoder-14B-preview-lora.yml

@@ -21,7 +21,7 @@ output_dir: ./outputs/lora-out
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/falcon/config-7b-lora.yml

@@ -47,7 +47,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/falcon/config-7b-qlora.yml

@@ -77,7 +77,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.000001

examples/archived/falcon/config-7b.yml

@@ -44,7 +44,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 40
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/gemma/qlora.yml

@@ -25,7 +25,7 @@ lora_target_linear: true
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/archived/gptj/qlora.yml

@@ -40,7 +40,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/jeopardy-bot/config.yml

@@ -41,7 +41,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/mpt-7b/config.yml

@@ -42,7 +42,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001

examples/archived/openllama-3b/config.yml

@@ -42,7 +42,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/openllama-3b/lora.yml

@@ -50,7 +50,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/openllama-3b/qlora.yml

@@ -43,7 +43,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1

examples/archived/qwen/lora.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qlora.yml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qwen2-moe-lora.yaml

@@ -45,7 +45,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/qwen/qwen2-moe-qlora.yaml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/redpajama/config-3b.yml

@@ -43,7 +43,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001

examples/archived/replit-3b/config-lora.yml

@@ -41,7 +41,7 @@ logging_steps: 1
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0

examples/archived/stablelm-2/1.6b/fft.yml

@@ -16,7 +16,7 @@ output_dir: ./outputs/out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter:
 lora_model_dir:
@@ -50,7 +50,7 @@ flash_attn_rms_norm: true
 flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 

examples/archived/stablelm-2/1.6b/lora.yml

@@ -19,7 +19,7 @@ output_dir: ./outputs/lora-out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -51,7 +51,7 @@ flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/starcoder2/qlora.yml

@@ -19,7 +19,7 @@ lora_model_dir:
 
 sequence_len: 8192
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16
@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 20
 evals_per_epoch: 4
 eval_steps:
 saves_per_epoch: 4

examples/archived/tiny-llama/lora-mps.yml

@@ -17,7 +17,7 @@ output_dir: ./outputs/lora-out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 eval_sample_packing: false
 
 adapter: lora
@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: false
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 0
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/lora.yml

@@ -17,7 +17,7 @@ output_dir: ./outputs/lora-out
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -47,7 +47,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/pretrain.yml

@@ -38,7 +38,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/tiny-llama/qlora.yml

@@ -21,7 +21,7 @@ lora_model_dir:
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16
@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/xgen-7b/xgen-7b-8k-qlora.yml

@@ -75,7 +75,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0

examples/archived/yi-34B-chat/qlora.yml

@@ -20,7 +20,7 @@ special_tokens:
 datasets:
   - path: mhenrichsen/alpaca_2k_test
     type: alpaca
-warmup_ratio: 0.1
+warmup_steps: 10
 
 # Iterations
 num_epochs: 1

examples/cloud/modal.yaml

@@ -26,3 +26,5 @@ timeout: 86400
 # Preprocess specific configurations
 memory_preprocess: 32
 timeout_preprocess: 14400
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

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

@@ -27,7 +27,7 @@ lora_target_linear: true
 sequence_len: 2048
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
         "%%capture\n",
         "# This step can take ~5-10 minutes to install dependencies\n",
         "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@010c3ac3f1e725098961832830303eeb4142dd88\""
+        "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@50cef19\""
       ]
     },
     {

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -21,7 +21,7 @@ output_dir: ./outputs/lora-out
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -21,7 +21,7 @@ output_dir: ./outputs/lora-out
 sequence_len: 4096
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -51,7 +51,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -12,7 +12,7 @@ output_dir: ./outputs/out
 
 sequence_len: 2048
 sample_packing: true
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:
@@ -37,7 +37,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -30,7 +30,7 @@ output_dir: ./outputs/out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:
@@ -61,7 +61,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0

examples/devstral/devstral-small-qlora.yml

@@ -25,7 +25,7 @@ lora_model_dir:
 
 sequence_len: 2048
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/falcon-h1/falcon-h1-1b-deep-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/falcon-h1/falcon-h1-1b-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/falcon-h1/falcon-h1-34b-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/falcon-h1/falcon-h1-3b-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/falcon-h1/falcon-h1-500m-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/falcon-h1/falcon-h1-7b-qlora.yaml

@@ -38,7 +38,7 @@ lora_target_modules:
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/gemma2/qlora.yml

@@ -31,7 +31,7 @@ lora_target_linear: true
 sequence_len: 2048
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/gemma2/reward-model.yaml

@@ -18,7 +18,7 @@ remove_unused_columns: false
 sequence_len: 2048
 sample_packing: false
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

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

@@ -35,7 +35,7 @@ lora_target_linear: true
 sequence_len: 2048
 sample_packing: true
 eval_sample_packing: false
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

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

@@ -25,7 +25,7 @@ lora_model_dir:
 
 sequence_len: 2048
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 32
 lora_alpha: 16

examples/gemma3n/README.md

@@ -1,65 +0,0 @@
-# Finetune Gemma-3n with Axolotl
-
-Gemma-3n is a family of multimodal models from Google found on [HuggingFace](https://huggingface.co/collections/google/gemma-3n-685065323f5984ef315c93f4). This guide shows how to fine-tune it with Axolotl.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Gemma3n is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
-
-    Here is an example of how to install from main for pip:
-
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min recommended)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn]'
-```
-
-2. In addition to Axolotl's requirements, Gemma-3n requires:
-
-```bash
-pip3 install timm==1.0.17
-
-# for loading audio data
-pip3 install librosa==0.11.0
-```
-
-3. Run the finetuning example:
-
-```bash
-# text only
-axolotl train examples/gemma3n/gemma-3n-e2b-qlora.yml
-
-# text + vision
-axolotl train examples/gemma3n/gemma-3n-e2b-vision-qlora.yml
-
-# text + vision + audio
-axolotl train examples/gemma3n/gemma-3n-e2b-vision-audio-qlora.yml
-```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-WARNING: The loss and grad norm will be much higher than normal. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
-
-### TIPS
-
-- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-- The multimodal dataset format follows the OpenAI multi-content Messages format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-## Optimization Guides
-
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-
-## Related Resources
-
-- [Gemma 3n Blog](https://ai.google.dev/gemma/docs/gemma-3n)
-- [Axolotl Docs](https://docs.axolotl.ai)
-- [Axolotl Website](https://axolotl.ai)
-- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
-- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/gemma3n/gemma-3n-e2b-qlora.yml

@@ -1,74 +0,0 @@
-base_model: google/gemma-3n-E2B-it
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-cut_cross_entropy: true
-
-load_in_8bit: false
-load_in_4bit: true
-
-# for use with fft to only train on language model layers
-# unfrozen_parameters:
-  # - model.language_model.*
-  # - lm_head
-  # - embed_tokens
-
-
-chat_template: gemma3n
-eot_tokens:
-  - <end_of_turn>
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    split: train[:1%]
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-# lora_target_linear: # Does not work with gemma3n currently
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-resume_from_checkpoint:
-logging_steps: 1
-# flash_attention: true  # Any attention impl does not work with gemma3n now
-
-warmup_ratio: 0.1
-evals_per_epoch:
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:

examples/gemma3n/gemma-3n-e2b-vision-audio-qlora.yml

@@ -1,78 +0,0 @@
-base_model: google/gemma-3n-E2B-it
-processor_type: AutoProcessor
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-cut_cross_entropy: true
-
-# for use with fft to only train on language model layers
-# unfrozen_parameters:
-  # - model.language_model.*
-  # - lm_head
-  # - embed_tokens
-
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3n
-eot_tokens:
-  - <end_of_turn>
-
-# sample dataset below requires downloading audio/image in advance
-# wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/African_elephant.jpg
-# wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/En-us-African_elephant.oga
-datasets:
-  - path: Nanobit/text-vision-audio-2k-test
-    type: chat_template
-dataset_prepared_path:
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-logging_steps: 1
-# flash_attention: true  # Any attention impl does not work with gemma3n now
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0

examples/gemma3n/gemma-3n-e2b-vision-qlora.yml

@@ -1,75 +0,0 @@
-base_model: google/gemma-3n-E2B-it
-processor_type: AutoProcessor
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-cut_cross_entropy: true
-
-# for use with fft to only train on language model layers
-# unfrozen_parameters:
-  # - model.language_model.*
-  # - lm_head
-  # - embed_tokens
-
-load_in_4bit: true
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3n
-eot_tokens:
-  - <end_of_turn>
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-dataset_prepared_path:
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-logging_steps: 1
-# flash_attention: true  # Any attention impl does not work with gemma3n now
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0

examples/glm4/qlora-32b.yaml

@@ -17,7 +17,7 @@ lora_model_dir:
 sequence_len: 2048
 sample_packing: true
 eval_sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 16
 lora_alpha: 32
@@ -55,7 +55,7 @@ flash_attention: true
 loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/jamba/qlora.yaml

@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0

examples/jamba/qlora_deepspeed.yaml

@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch:
 saves_per_epoch: 1
 

examples/jamba/qlora_fsdp_large.yaml

@@ -23,7 +23,7 @@ save_safetensors: true
 adapter: qlora
 sequence_len: 2048
 sample_packing: true
-
+pad_to_sequence_len: true
 
 lora_r: 16
 lora_alpha: 16
@@ -47,7 +47,7 @@ gradient_checkpointing_kwargs:
 logging_steps: 1
 flash_attention: true
 
-warmup_ratio: 0.1
+warmup_steps: 10
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0

examples/lfm2/lfm2-350m-fft.yaml

@@ -18,7 +18,7 @@ output_dir: ./outputs/out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 wandb_project:
 wandb_entity:

examples/llama-2/fft_optimized.yml

@@ -14,7 +14,7 @@ output_dir: ./outputs/out
 
 sequence_len: 4096
 sample_packing: true
-
+pad_to_sequence_len: true
 
 adapter:
 lora_model_dir:
@@ -48,7 +48,7 @@ flash_attn_rms_norm: true
 flash_attn_fuse_qkv: false
 flash_attn_fuse_mlp: true
 
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 

examples/llama-2/gptq-lora.yml

@@ -56,7 +56,7 @@ logging_steps: 1
 flash_attention:
 sdp_attention:
 flash_optimum:
-warmup_ratio: 0.1
+warmup_steps: 100
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1