fix llama modeling

fix residuals and add llama support
add custom modeling for gemma3 using liger fused add rms
2025-07-30 11:37:58 -04:00 · 2025-07-30 10:22:38 -04:00 · 2025-07-30 08:21:03 -04:00 · 2025-07-30 08:05:25 -04:00 · 2025-07-30 06:44:06 -04:00 · 2025-07-30 06:38:13 -04:00
446 changed files with 20453 additions and 5872 deletions
--- a/.axolotl-complete.bash
+++ b/.axolotl-complete.bash
@@ -0,0 +1,41 @@
+#!/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
--- a/.bandit
+++ b/.bandit
@@ -1,3 +1,3 @@
 [bandit]
 exclude = tests
-skips = B101
+skips = B101,B615
--- a/.coderabbit.yaml
+++ b/.coderabbit.yaml
@@ -0,0 +1,16 @@
+# 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
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -5,17 +5,19 @@ on:
    branches:
      - "main"
    paths:
-      - 'Dockerfile-base'
+      - 'docker/Dockerfile-base'
+      - 'docker/Dockerfile-uv-base'
      - '.github/workflows/base.yml'
  pull_request:
    paths:
-      - 'Dockerfile-base'
+      - 'docker/Dockerfile-base'
+      - 'docker/Dockerfile-uv-base'
      - '.github/workflows/base.yml'
  workflow_dispatch:

 jobs:
  build-base:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
    timeout-minutes: 480
    # this job needs to be run on self-hosted GPU runners...
    runs-on: ubuntu-latest-m
@@ -23,13 +25,6 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.5.1
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
          - cuda: "124"
            cuda_version: 12.4.1
            cudnn_version: ""
@@ -44,6 +39,13 @@ jobs:
            pytorch: 2.6.0
            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
            dockerfile: "Dockerfile-base"
+          - cuda: "126"
+            cuda_version: 12.6.3
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.7.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
          - cuda: "126"
            cuda_version: 12.6.3
            cudnn_version: ""
@@ -106,7 +108,7 @@ jobs:
            PYTORCH_VERSION=${{ matrix.pytorch }}
            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
  build-base-uv:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
    timeout-minutes: 480
    runs-on: ubuntu-latest-m
    strategy:
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -3,6 +3,7 @@ on:
  # check on PRs, and manual triggers
  merge_group:
  pull_request:
+      types: [opened, synchronize, reopened, ready_for_review]
      paths:
       - '**.py'
       - 'requirements.txt'
@@ -16,6 +17,7 @@ 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
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -15,17 +15,16 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
+            axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.0
            axolotl_extras: vllm
-            is_latest: true
          - cuda: 126
            cuda_version: 12.6.3
            python_version: "3.11"
@@ -83,22 +82,22 @@ jobs:
    strategy:
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
            axolotl_extras:
-            is_latest: true
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.0
+            axolotl_extras:
          - cuda: 126
            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.7.1
            axolotl_extras:
+            is_latest: true
          - cuda: 128
            cuda_version: 12.8.1
            python_version: "3.11"
@@ -146,8 +145,8 @@ jobs:
    strategy:
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
            axolotl_extras:
--- a/.github/workflows/multi-gpu-e2e.yml
+++ b/.github/workflows/multi-gpu-e2e.yml
@@ -21,22 +21,22 @@ concurrency:

 jobs:
  test-axolotl-multigpu:
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
+    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) }}
    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
-            axolotl_extras: vllm
+            axolotl_extras:
            num_gpus: 2
            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.7.0
            axolotl_extras:
            num_gpus: 2
            nightly_build: "true"
@@ -44,7 +44,7 @@ jobs:
            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.7.1
-            axolotl_extras:
+            axolotl_extras: vllm
            num_gpus: 2
            nightly_build: "true"
    runs-on: [self-hosted, modal]
--- a/.github/workflows/nightlies.yml
+++ b/.github/workflows/nightlies.yml
@@ -12,16 +12,16 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
            axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
      - name: Checkout
@@ -65,16 +65,16 @@ jobs:
    strategy:
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            axolotl_extras:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
            pytorch: 2.6.0
            axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
    runs-on: axolotl-gpu-runner
    steps:
      - name: Checkout
--- a/.github/workflows/preview-docs.yml
+++ b/.github/workflows/preview-docs.yml
@@ -2,7 +2,7 @@ name: Preview
 on:
  workflow_dispatch:
  pull_request:
-    types: [opened, synchronize, reopened]
+    types: [opened, synchronize, reopened, ready_for_review]

    # Run the workflow only when one of these files changes
    paths:
@@ -25,9 +25,12 @@ permissions:
 jobs:
  preview:
    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft }}
    steps:
      - name: Check out repository
        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event.pull_request.head.sha }}

      - name: Set up Quarto
        uses: quarto-dev/quarto-actions/setup@v2
@@ -50,10 +53,12 @@ jobs:

      - name: Netlify Publish
        uses: nwtgck/actions-netlify@v3.0
+        if: ${{ secrets.NETLIFY_AUTH_TOKEN != '' }}
+        id: netlify
        with:
          publish-dir: './_site'
-          enable-pull-request-comment: true
-          enable-github-deployment: true
+          enable-pull-request-comment: false
+          enable-github-deployment: false
          github-token: ${{ secrets.GITHUB_TOKEN }}
          deploy-message: "Deployed On Netlify"
          github-deployment-environment: 'preview'
@@ -61,3 +66,13 @@ jobs:
        env:
          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
+
+      - name: Update PR with preview link
+        if: ${{ steps.netlify.outcome == 'success' && secrets.NETLIFY_AUTH_TOKEN != '' }}
+        uses: marocchino/sticky-pull-request-comment@v2
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          message: |
+            📖 **Documentation Preview**: ${{ steps.netlify.outputs.deploy-url }}
+
+            Deployed on Netlify from commit ${{ github.event.pull_request.head.sha }}
--- a/.github/workflows/tests-nightly.yml
+++ b/.github/workflows/tests-nightly.yml
@@ -18,116 +18,26 @@ jobs:
        env:
          SKIP: no-commit-to-branch

-  preload-cache:
-    name: Preload HF cache
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.11"]
-        pytorch_version: ["2.6.0"]
-    timeout-minutes: 20
-
-    env:
-      AXOLOTL_IS_CI_CACHE_PRELOAD: "1"
-
-    steps:
-      - name: Check out repository code
-        uses: actions/checkout@v4
-
-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
-
-      - name: upgrade pip
-        run: |
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
-
-      - name: Install PyTorch
-        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
-
-      - name: Install dependencies
-        run: |
-          pip3 show torch
-          pip3 install --no-build-isolation -U -e .
-          python scripts/unsloth_install.py | sh
-          python scripts/cutcrossentropy_install.py | sh
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
-
-      - name: Make sure PyTorch version wasn't clobbered
-        run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
-
-      - name: Ensure axolotl CLI was installed
-        run: |
-          axolotl --help
-
-      - name: Pre-Download dataset fixture
-        run: |
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-
-      - name: Run tests
-        run: |
-          pytest -v tests/conftest.py
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-          files: ./coverage.xml
-          flags: unittests,pytorch-${{ matrix.pytorch_version }}
-          fail_ci_if_error: false
-
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
-      - name: Save HF cache
-        id: hf-cache
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ steps.hf-cache-restore.outputs.cache-primary-key }}
-
  pytest:
    name: PyTest
    runs-on: ubuntu-latest
-    needs: [preload-cache]
    strategy:
      fail-fast: false
      max-parallel: 2
      matrix:
        python_version: ["3.11"]
-        pytorch_version: ["2.5.1", "2.6.0", "2.7.0"]
+        pytorch_version: ["2.6.0", "2.7.0"]
    timeout-minutes: 20

    steps:
      - name: Check out repository code
        uses: actions/checkout@v4

-      - name: Restore HF cache
-        id: hf-cache-restore
-        uses: actions/cache/restore@v4
-        with:
-          path: |
-            /home/runner/.cache/huggingface/hub/datasets--*
-            /home/runner/.cache/huggingface/hub/models--*
-          key: ${{ runner.os }}-hf-hub-cache-v2
+      - name: Restore Cache from S3
+        id: hf-cache-restore-s3
+        run: |
+          mkdir -p /home/runner/.cache/huggingface/hub
+          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C /home/runner/.cache/huggingface/hub/  --use-compress-program unzstd

      - name: Setup Python
        uses: actions/setup-python@v5
@@ -142,7 +52,7 @@ jobs:

      - name: Install PyTorch
        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
+          pip3 install torch==${{ matrix.pytorch_version }} torchvision

      - name: Update requirements.txt
        run: |
@@ -168,15 +78,11 @@ jobs:
        run: |
          axolotl --help

-      - name: Pre-Download dataset fixture
-        run: |
-          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
-
      - name: Run tests
        run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest -v tests/patched/
-          pytest -v tests/cli/
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v --durations=10 tests/patched/
+          pytest -v --durations=10 tests/cli/

      - name: cleanup pip cache
        run: |
@@ -186,24 +92,24 @@ jobs:
    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: 60
+    timeout-minutes: 120
    needs: [pre-commit, pytest]

    strategy:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
-            pytorch: 2.5.1
+            pytorch: 2.6.0
            num_gpus: 1
            axolotl_extras:
            nightly_build: "true"
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.7.1
            num_gpus: 1
            axolotl_extras:
            nightly_build: "true"
@@ -217,7 +123,7 @@ jobs:
      - name: Install Modal
        run: |
          python -m pip install --upgrade pip
-          pip install modal==0.71.8 jinja2
+          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 }}" >> $GITHUB_ENV
@@ -231,3 +137,45 @@ jobs:
      - name: Run tests job on Modal
        run: |
          modal run cicd.e2e_tests
+  docker-e2e-multigpu-tests:
+    if: github.repository_owner == 'axolotl-ai-cloud'
+    # this job needs to be run on self-hosted GPU runners...
+    runs-on: [self-hosted, modal]
+    timeout-minutes: 120
+    needs: [pre-commit, pytest, docker-e2e-tests]
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            num_gpus: 2
+            axolotl_extras:
+            nightly_build: "true"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install Modal
+        run: |
+          python -m pip install --upgrade pip
+          pip install modal==1.0.2 jinja2
+      - name: Update env vars
+        run: |
+          echo "BASE_TAG=main-base-py${{ 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
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -13,6 +13,7 @@ on:
      - 'cicd/cicd.sh'
      - 'cicd/Dockerfile.jinja'
  pull_request:
+      types: [opened, synchronize, reopened, ready_for_review]
      paths:
       - '**.py'
       - 'requirements.txt'
@@ -34,6 +35,7 @@ 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
@@ -47,12 +49,13 @@ jobs:
  pytest:
    name: PyTest
    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft }}
 #    needs: [preload-cache]
    strategy:
      fail-fast: false
      matrix:
        python_version: ["3.11"]
-        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
+        pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
    timeout-minutes: 20

    steps:
@@ -78,7 +81,7 @@ jobs:

      - name: Install PyTorch
        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
+          pip3 install torch==${{ matrix.pytorch_version }} torchvision

      - name: Install dependencies
        run: |
@@ -102,9 +105,9 @@ jobs:

      - name: Run tests
        run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
-          pytest -v tests/patched/ --cov=axolotl --cov-append --cov-report=xml
-          pytest -v tests/cli/ --cov=axolotl --cov-append --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
+          pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml

      - name: Upload coverage to Codecov
        uses: codecov/codecov-action@v5
@@ -121,11 +124,12 @@ jobs:
  pytest-sdist:
    name: PyTest from Source Dist
    runs-on: ubuntu-latest
+    if: ${{ !github.event.pull_request.draft }}
    strategy:
      fail-fast: false
      matrix:
        python_version: ["3.11"]
-        pytorch_version: ["2.5.1", "2.6.0", "2.7.1"]
+        pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
    timeout-minutes: 20

    steps:
@@ -151,7 +155,7 @@ jobs:

      - name: Install PyTorch
        run: |
-          pip3 install torch==${{ matrix.pytorch_version }}
+          pip3 install torch==${{ matrix.pytorch_version }} torchvision

      - name: Install dependencies
        run: |
@@ -175,9 +179,9 @@ jobs:

      - name: Run tests
        run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest -v tests/patched/
-          pytest -v tests/cli/
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
+          pytest -v --durations=10 tests/patched/
+          pytest -v --durations=10 tests/cli/

      - name: cleanup pip cache
        run: |
@@ -185,7 +189,7 @@ 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' }}
+    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
    # this job needs to be run on self-hosted GPU runners...
    runs-on: [self-hosted, modal]
    timeout-minutes: 120
@@ -195,12 +199,12 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
            python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.7.1
            num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
          - cuda: 126
            cuda_version: 12.6.3
            python_version: "3.11"
@@ -235,7 +239,7 @@ jobs:
          modal run cicd.e2e_tests

  docker-e2e-tests:
-    if: github.repository_owner == 'axolotl-ai-cloud'
+    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
    # this job needs to be run on self-hosted GPU runners...
    runs-on: [self-hosted, modal]
    timeout-minutes: 120
@@ -247,22 +251,10 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras: llmcompressor
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.5.1
-            num_gpus: 1
-            axolotl_extras:
          - cuda: 126
            cuda_version: 12.6.3
            python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.6.0
            num_gpus: 1
            axolotl_extras:
          - cuda: 128
@@ -301,6 +293,7 @@ jobs:
    runs-on: [self-hosted, modal]
    timeout-minutes: 90
    needs: [docker-e2e-tests]
+    if: ${{ !github.event.pull_request.draft }}

    strategy:
      fail-fast: false
@@ -311,7 +304,7 @@ jobs:
            python_version: "3.11"
            pytorch: 2.6.0
            num_gpus: 1
-            axolotl_extras: vllm
+            axolotl_extras:
    steps:
      - name: Checkout
        uses: actions/checkout@v4
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -19,7 +19,7 @@ repos:
    hooks:
      - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 7.2.0
+    rev: 7.3.0
    hooks:
    - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
@@ -27,7 +27,7 @@ repos:
    hooks:
    - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.16.0
+    rev: v1.17.0
    hooks:
    - id: mypy
      additional_dependencies:
@@ -36,7 +36,7 @@ repos:
            'pydantic>=2.5.3',
        ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.8.3
+    rev: 1.8.6
    hooks:
    -   id: bandit
        args: [
--- a/.runpod/README.md
+++ b/.runpod/README.md
@@ -119,14 +119,15 @@ 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           |
-| `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             |
+| `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |
+| `dataset_exact_deduplication`     | `true`                     | Deduplicate datasets                |

 ## LoRA Configuration

--- a/.runpod/src/config/config.yaml
+++ b/.runpod/src/config/config.yaml
@@ -97,7 +97,7 @@
 #       # 'no_input_format' cannot include {input}
 #       no_input_format: "{instruction} "

-#       # For `completion` datsets only, uses the provided field instead of `text` column
+#       # For `completion` datasets only, uses the provided field instead of `text` column
 #       field:

 # # Axolotl attempts to save the dataset as an arrow after packing the data together so
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -2,4 +2,5 @@ include requirements.txt
 include README.md
 include LICENSE
 include src/setuptools_axolotl_dynamic_dependencies.py
+include src/axolotl/utils/chat_templates/templates/*.jinja
 recursive-include axolotl *.py
--- a/README.md
+++ b/README.md
@@ -25,6 +25,8 @@

 ## 🎉 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!
@@ -43,7 +45,7 @@ Features:
 - **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
 - **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
 - **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.
- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
+- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
 - **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
 - **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.

@@ -55,10 +57,12 @@ Features:

 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
 - Python 3.11
- PyTorch ≥2.5.1
+- PyTorch ≥2.6.0

 ### Installation

+#### Using pip
+
 ```bash
 pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
@@ -68,8 +72,29 @@ axolotl fetch examples
 axolotl fetch deepspeed_configs  # OPTIONAL
 ```

+#### Using Docker
+
+Installing with Docker can be less error prone than installing in your own environment.
+```bash
+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
@@ -111,12 +136,6 @@ 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
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -268,6 +268,8 @@ 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:
@@ -276,6 +278,7 @@ website:
            - docs/torchao.qmd
            - docs/custom_integrations.qmd
            - docs/sequence_parallelism.qmd
+            - docs/gradient_checkpointing.qmd

        - section: "Troubleshooting"
          contents:
--- a/cicd/Dockerfile-uv.jinja
+++ b/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
+    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

 WORKDIR /workspace

--- a/cicd/Dockerfile.jinja
+++ b/cicd/Dockerfile.jinja
@@ -9,9 +9,10 @@ ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
 ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 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
+    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

 WORKDIR /workspace

--- a/cicd/multigpu.py
+++ b/cicd/multigpu.py
@@ -24,9 +24,9 @@ df_template = template_env.get_template("Dockerfile.jinja")
 df_args = {
    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
-    "CUDA": os.environ.get("CUDA", "124"),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
+    "CUDA": os.environ.get("CUDA", "126"),
    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
--- a/cicd/multigpu.sh
+++ b/cicd/multigpu.sh
@@ -19,5 +19,7 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
  --cov-append \
  --cov-report=xml:multigpu-coverage.xml

-# Upload coverage to Codecov
-codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
+# 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
--- a/cicd/single_gpu.py
+++ b/cicd/single_gpu.py
@@ -24,14 +24,16 @@ df_template = template_env.get_template(dockerfile)
 df_args = {
    "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
    "AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
-    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
-    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
-    "CUDA": os.environ.get("CUDA", "124"),
+    "PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.6.0"),
+    "BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu126-2.6.0"),
+    "CUDA": os.environ.get("CUDA", "126"),
    "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
    "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
    "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
    "HF_HOME": "/workspace/data/huggingface-cache/hub",
+    "PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
+    "DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
 }

 dockerfile_contents = df_template.render(**df_args)
--- a/codecov.yml
+++ b/codecov.yml
@@ -22,6 +22,7 @@ coverage:
        only_pulls: true
        flags: null
        paths: null
+        informational: true
    patch:
      default:
        # basic
--- a/deepspeed_configs/zero3.json
+++ b/deepspeed_configs/zero3.json
@@ -7,9 +7,9 @@
    "reduce_bucket_size": "auto",
    "stage3_prefetch_bucket_size": "auto",
    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
+    "max_live_parameters": 0,
+    "max_reuse_distance": 0,
+    "gather_16bit_weights_on_model_save": true
  },
  "bf16": {
    "enabled": "auto"
--- a/deepspeed_configs/zero3_bf16.json
+++ b/deepspeed_configs/zero3_bf16.json
@@ -7,9 +7,9 @@
    "reduce_bucket_size": "auto",
    "stage3_prefetch_bucket_size": "auto",
    "stage3_param_persistence_threshold": "auto",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
+    "max_live_parameters": 0,
+    "max_reuse_distance": 0,
+    "gather_16bit_weights_on_model_save": true
  },
  "bf16": {
    "enabled": true
--- a/deepspeed_configs/zero3_bf16_cpuoffload_all.json
+++ b/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",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
+    "max_live_parameters": 0,
+    "max_reuse_distance": 0,
+    "gather_16bit_weights_on_model_save": true
  },
  "bf16": {
    "enabled": true
--- a/deepspeed_configs/zero3_bf16_cpuoffload_params.json
+++ b/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",
-    "stage3_max_live_parameters": 0,
-    "stage3_max_reuse_distance": 0,
-    "stage3_gather_16bit_weights_on_model_save": true
+    "max_live_parameters": 0,
+    "max_reuse_distance": 0,
+    "gather_16bit_weights_on_model_save": true
  },
  "bf16": {
    "enabled": true
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -10,7 +10,9 @@ 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
+    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/*

 WORKDIR /workspace

@@ -23,17 +25,17 @@ 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
+    fi && \
+    python scripts/unsloth_install.py | sh && \
+    python scripts/cutcrossentropy_install.py | sh && \
+    pip install pytest && \
+    pip cache purge

-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
+# fix so that git fetch/pull from remote works with shallow clone
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
+    git config --get remote.origin.fetch && \
+    git config --global credential.helper store

-# helper for huggingface-login cli
-RUN git config --global credential.helper store
+COPY .axolotl-complete.bash /root/.axolotl-complete.bash
+RUN chmod +x /root/.axolotl-complete.bash && \
+    echo 'source /root/.axolotl-complete.bash' >> ~/.bashrc
--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -16,12 +16,16 @@ 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/* \
+    && 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/* \
    && 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}"
@@ -31,13 +35,15 @@ 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 install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
+    python3 -m pip cache purge

 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 install -U --no-cache-dir pydantic==1.10.10 && \
+    pip3 cache purge

-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
-        pip3 install flash-attn==2.7.4.post1; \
+RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
+        FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \
    fi
--- a/docker/Dockerfile-base-nightly
+++ b/docker/Dockerfile-base-nightly
@@ -22,18 +22,22 @@ RUN apt-get update \
    && mkdir /root/.conda \
    && bash Miniconda3-latest-Linux-x86_64.sh -b \
    && rm -f Miniconda3-latest-Linux-x86_64.sh \
+    && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main \
+    && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r \
    && conda create -n "py${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 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 --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 install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
+    python3 -m pip cache purge

 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 install -U --no-cache-dir pydantic==1.10.10 && \
+    pip3 cache purge
--- a/docker/Dockerfile-cloud
+++ b/docker/Dockerfile-cloud
@@ -14,7 +14,10 @@ COPY scripts/motd /etc/motd

 RUN pip install jupyterlab notebook ipywidgets && \
    jupyter lab clean
-RUN apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
+RUN apt 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/* && \
    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 && \
--- a/docker/Dockerfile-cloud-no-tmux
+++ b/docker/Dockerfile-cloud-no-tmux
@@ -9,13 +9,15 @@ ENV HF_HUB_ENABLE_HF_TRANSFER="1"
 EXPOSE 8888
 EXPOSE 22

-COPY scripts/cloud-entrypoint-term.sh /root/cloud-entrypoint.sh
+COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh
 COPY scripts/motd /etc/motd

 RUN pip install jupyterlab notebook ipywidgets && \
    jupyter lab clean
-RUN apt install --yes --no-install-recommends openssh-server tmux sudo && \
-    pip3 install -U --no-cache-dir grpcio ray[default]==2.9.3 && \
+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/* && \
    mkdir -p ~/.ssh && \
    chmod 700 ~/.ssh && \
    printf "[ ! -z \"\$TERM\" -a -r /etc/motd ] && cat /etc/motd\n" >> ~/.bashrc && \
--- a/docker/Dockerfile-uv-base
+++ b/docker/Dockerfile-uv-base
@@ -34,7 +34,3 @@ RUN uv pip install packaging setuptools wheel psutil \
    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
    && uv pip install awscli pydantic
-
-RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
-        uv pip install --no-build-isolation flash-attn==2.7.4.post1; \
-    fi
--- a/docs/custom_integrations.qmd
+++ b/docs/custom_integrations.qmd
@@ -7,6 +7,7 @@ toc-depth: 3
 ```{python}
 #| echo: false

+import os
 import re

 def process_readme(integration_name):
@@ -53,6 +54,24 @@ sections = [
    ("LLMCompressor", "llm_compressor")
 ]

+for folder_name in os.listdir("../src/axolotl/integrations/"):
+    if folder_name in [path for name, path in sections]:
+        # skip if already in sections
+        continue
+    if os.path.exists(f"../src/axolotl/integrations/{folder_name}/README.md"):
+        # grab the first heading in README.md as the section name
+        with open(f"../src/axolotl/integrations/{folder_name}/README.md", "r") as f:
+            txt = f.read()
+            matches = re.search(r'^# (.*)\n?', txt, flags=re.MULTILINE)
+            if matches:
+                name = matches.group(1)
+            else:
+                continue
+            sections.append((name, folder_name))
+
+# sort sections by name
+sections = sorted(sections, key=lambda x: x[0])
+
 for section_name, folder_name in sections:
    print(print_section(section_name, folder_name))
 ```
--- a/docs/dataset-formats/conversation.qmd
+++ b/docs/dataset-formats/conversation.qmd
@@ -9,7 +9,7 @@ order: 3
 Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.

 ```{.json filename="data.jsonl"}
-{"conversations": [{"role": "...", "content": "..."}]}
+{"messages": [{"role": "...", "content": "..."}, {"role": "...", "content": "..."}, ...]}
 ```

 See [configs](../config-reference.qmd) for full configs and supported templates.
@@ -187,6 +187,7 @@ Instead of passing `tools` via the system prompt, an alternative method would be
            "role": "assistant", // call the function via assistant
            "tool_calls": [
                {
+                    "id": "...",  // required only for mistral
                    "type": "function",
                    "function": {
                        "name": "...",
@@ -199,6 +200,7 @@ Instead of passing `tools` via the system prompt, an alternative method would be
        },
        {
            "role": "tool",
+            "tool_call_id": "...",  // required only for mistral
            "name": "...",
            "content": "..."
        },
--- a/docs/docker.qmd
+++ b/docs/docker.qmd
@@ -9,7 +9,7 @@ format:
 This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).

 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with Pytorch 2.7.1 and CUDA 12.8.
+For Blackwell GPUs, please use the tags with PyTorch 2.7.1 and CUDA 12.8.
 :::

 ## Base
@@ -34,8 +34,9 @@ Tags examples:

 - `main-base-py3.11-cu128-2.7.1`
 - `main-base-py3.11-cu126-2.7.1`
+- `main-base-py3.11-cu126-2.7.0`
+- `main-base-py3.11-cu126-2.6.0`
 - `main-base-py3.11-cu124-2.6.0`
- `main-base-py3.11-cu124-2.5.1`

 ## Main

@@ -73,13 +74,15 @@ There may be some extra tags appended to the image, like `-vllm` which installs

 Tags examples:

+- `main-py3.11-cu128-2.7.1`
+- `main-py3.11-cu126-2.7.1`
 - `main-py3.11-cu126-2.7.0`
+- `main-py3.11-cu126-2.6.0`
 - `main-py3.11-cu124-2.6.0`
- `main-py3.11-cu124-2.5.1`
 - `main-latest`
 - `main-20250303-py3.11-cu124-2.6.0`
- `main-20250303-py3.11-cu124-2.5.1`
- `0.9.2`
+- `main-20250303-py3.11-cu126-2.6.0`
+- `0.10.1`

 ## Cloud

--- a/docs/faq.qmd
+++ b/docs/faq.qmd
@@ -51,6 +51,18 @@ description: Frequently asked questions
 >   pad_token: "..."
 > ```

+**Q: `IterableDataset error` or `KeyError: 'input_ids'` when using `preprocess` CLI**
+
+> A: This is because you may be using `preprocess` CLI with `pretraining_dataset:` or `skip_prepare_dataset: true` respectively. Please use `axolotl train` CLI directly instead as these datasets are prepared on demand.
+
+**Q: vLLM is not working with Axolotl**
+
+> A: We currently recommend torch 2.6.0 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.11-cu124-2.6.0` tag.
+
+**Q: FA2 2.8.0 `undefined symbol` runtime error on CUDA 12.4**
+
+> A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717.
+
 ### Chat templates

 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
@@ -124,3 +136,7 @@ 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.
--- a/docs/fsdp_qlora.qmd
+++ b/docs/fsdp_qlora.qmd
@@ -20,7 +20,7 @@ To enable `QLoRA` with `FSDP`, you need to perform the following steps:
 > See the [example config](#example-config) file in addition to reading these instructions.

 1. Set `adapter: qlora` in your axolotl config file.
-2. Enable FSDP in your axolotl config, as [described here](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#fsdp).
+2. Enable FSDP in your axolotl config, as [described here](multi-gpu.qmd#sec-fsdp).
 3. Use one of the supported model types: `llama`, `mistral` or `mixtral`.

 ## Example Config
--- a/docs/gradient_checkpointing.qmd
+++ b/docs/gradient_checkpointing.qmd
@@ -0,0 +1,29 @@
+---
+title: Gradient Checkpointing and Activation Offloading
+---
+
+Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning
+models by reducing the memory footprint and improving computational efficiency.
+
+### Enabling Gradient Checkpointing
+
+```yaml
+gradient_checkpointing: true
+```
+
+### Enabling Activation Offloading
+
+```yaml
+gradient_checkpointing: true  # required for activation offloading
+activation_offloading: true
+```
+
+Activation offloading variants:
+
+The default `activation_offloading: true` offloads activations to CPU and uses CUDA streams
+to overlap the communications and computations when offloading.
+
+The `activation_offloading: legacy` naively offloads activations to CPU and without additional optimizations.
+
+For resource constrained environments with limited CPU memory, `activation_offloading: disk` offloads
+activations to disk instead of CPU RAM so that much larger context lengths can be trained with minimal memory.
--- a/docs/installation.qmd
+++ b/docs/installation.qmd
@@ -15,7 +15,7 @@ This guide covers all the ways you can install and set up Axolotl for your envir

 - NVIDIA GPU (Ampere architecture or newer for `bf16` and Flash Attention) or AMD GPU
 - Python ≥3.11
- PyTorch ≥2.5.1
+- PyTorch ≥2.6.0

 ## Installation Methods {#sec-installation-methods}

@@ -124,10 +124,13 @@ For providers supporting Docker:

 - Use `axolotlai/axolotl-cloud:main-latest`
 - Available on:
-  - [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)
+    - [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)

 ### Google Colab {#sec-colab}

--- a/docs/mixed_precision.qmd
+++ b/docs/mixed_precision.qmd
@@ -0,0 +1,149 @@
+---
+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).
--- a/docs/multi-gpu.qmd
+++ b/docs/multi-gpu.qmd
@@ -23,8 +23,6 @@ Axolotl supports several methods for multi-GPU training:

 ## DeepSpeed {#sec-deepspeed}

-DeepSpeed is the recommended approach for multi-GPU training due to its stability and performance. It provides various optimization levels through ZeRO stages.
-
 ### Configuration {#sec-deepspeed-config}

 Add to your YAML config:
@@ -32,7 +30,6 @@ Add to your YAML config:
 ```{.yaml}
 deepspeed: deepspeed_configs/zero1.json
 ```
-
 ### Usage {#sec-deepspeed-usage}

 ```{.bash}
@@ -66,9 +63,75 @@ Start from Stage 1 -> Stage 2 -> Stage 3.

 :::

-## FSDP {#sec-fsdp}
+::: {.callout-tip}

-### Basic FSDP Configuration {#sec-fsdp-config}
+Using ZeRO Stage 3 with Single-GPU training
+
+ZeRO Stage 3 can be used for training on a single GPU by manually setting the environment variables:
+`WORLD_SIZE=1 LOCAL_RANK=0 MASTER_ADDR=0.0.0.0 MASTER_PORT=29500`
+
+:::
+
+## Fully Sharded Data Parallel (FSDP) {#sec-fsdp}
+
+::: {.callout-note}
+
+FSDP2 is recommended for new users. FSDP1 is deprecated and will be removed in an upcoming release of Axolotl.
+
+:::
+
+### Migrating from FSDP1 to FSDP2 {#sec-migrate-fsdp1-fsdp2}
+
+To migrate your config from FSDP1 to FSDP2, you must use the `fsdp_version` top-level config field to specify the FSDP version, and
+also follow the config field mapping below to update field names.
+
+#### Config mapping
+
+FSDP1 | FSDP2
+-------- | --------
+fsdp_sharding_strategy | reshard_after_forward
+fsdp_backward_prefetch_policy | **REMOVED**
+fsdp_backward_prefetch | **REMOVED**
+fsdp_forward_prefetch | **REMOVED**
+fsdp_sync_module_states | **REMOVED**
+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:
+
+```{.yaml}
+fsdp_version: 1
+fsdp_config:
+  fsdp_offload_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+```
+
+You can migrate to the following FSDP2 config:
+
+```{.yaml}
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: true
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Qwen3DecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  reshard_after_forward: true
+```
+
+### FSDP1 (deprecated) {#sec-fsdp-config}
+
+::: {.callout-note}
+
+Using `fsdp` to configure FSDP is deprecated and will be removed in an upcoming release of Axolotl. Please use `fsdp_config` as above instead.
+
+:::

 ```{.yaml}
 fsdp:
@@ -80,6 +143,7 @@ fsdp_config:
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```

+
 ## Sequence parallelism {#sec-sequence-parallelism}

 We support sequence parallelism (SP) via the
--- a/docs/multi-node.qmd
+++ b/docs/multi-node.qmd
@@ -40,13 +40,13 @@ use_cpu: false

 Configure your model to use FSDP in the Axolotl yaml. For example:
 ```yaml
-fsdp:
-  - full_shard
-  - auto_wrap
+fsdp_version: 2
 fsdp_config:
-  fsdp_offload_params: true
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  offload_params: true
+  state_dict_type: FULL_STATE_DICT
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  reshard_after_forward: true
 ```

 All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.
--- a/docs/multimodal.qmd
+++ b/docs/multimodal.qmd
@@ -14,6 +14,7 @@ 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)

@@ -110,6 +111,22 @@ 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
@@ -132,7 +149,9 @@ 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` or `image` or `path` or `url` or `base64`).
+- `content` is a list of `type` and (`text`, `image`, `path`, `url`, `base64`, or `audio`).
+
+### Image

 ::: {.callout-note}
 For backwards compatibility:
@@ -141,15 +160,29 @@ 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
 [
@@ -178,3 +211,9 @@ 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.
--- a/docs/rlhf.qmd
+++ b/docs/rlhf.qmd
@@ -17,7 +17,6 @@ feedback. Various methods include, but not limited to:
 - [Kahneman-Tversky Optimization (KTO)](#kto)
 - [Odds Ratio Preference Optimization (ORPO)](#orpo)
 - [Group Relative Policy Optimization (GRPO)](#grpo)
- Proximal Policy Optimization (PPO) (not yet supported in axolotl, if you're interested in contributing, please reach out!)


 ## RLHF using Axolotl
@@ -275,15 +274,14 @@ rl: dpo
 datasets:
  - path: ...
    split: train
-    type: user_defined.default
-
-    field_prompt: "prompt"
-    field_system: "system"
-    field_chosen: "chosen"
-    field_rejected: "rejected"
-    prompt_format: "{prompt}"
-    chosen_format: "{chosen}"
-    rejected_format: "{rejected}"
+    type:
+      field_prompt: "prompt"
+      field_system: "system"
+      field_chosen: "chosen"
+      field_rejected: "rejected"
+      prompt_format: "{prompt}"
+      chosen_format: "{chosen}"
+      rejected_format: "{rejected}"
 ```

 The input format is a simple JSON input with customizable fields based on the above config.
@@ -476,14 +474,13 @@ rl: kto
 datasets:
  - path: ...
    split: train
-    type: user_defined.default
-
-    field_prompt: "prompt"
-    field_system: "system"
-    field_completion: "completion"
-    field_label: "label"
-    prompt_format: "{prompt}"
-    completion_format: "{completion}"
+    type:
+      field_prompt: "prompt"
+      field_system: "system"
+      field_completion: "completion"
+      field_label: "label"
+      prompt_format: "{prompt}"
+      completion_format: "{completion}"
 ```

 The input format is a simple JSON input with customizable fields based on the above config.
--- a/examples/alst/README.md
+++ b/examples/alst/README.md
@@ -0,0 +1,9 @@
+# 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).
--- a/examples/alst/llama3-8b-deepspeed-alst.yaml
+++ b/examples/alst/llama3-8b-deepspeed-alst.yaml
@@ -0,0 +1,53 @@
+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
--- a/examples/alst/llama3-8b-fsdp2-alst.yaml
+++ b/examples/alst/llama3-8b-fsdp2-alst.yaml
@@ -0,0 +1,59 @@
+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
--- a/examples/archived/README.md
+++ b/examples/archived/README.md
@@ -0,0 +1,5 @@
+# Archived Examples
+
+This directory contains examples that are no longer maintained and may no longer be functional.
+
+We keep them around for archival purposes in case they are useful to others.
--- a/examples/archived/cerebras/btlm-ft.yml
+++ b/examples/archived/cerebras/btlm-ft.yml
@@ -66,7 +66,7 @@ flash_optimum:
 gptq_groupsize:
 gptq_model_v1:

-warmup_steps: 32
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 save_total_limit:
--- a/examples/archived/cerebras/qlora.yml
+++ b/examples/archived/cerebras/qlora.yml
@@ -43,7 +43,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/code-llama/13b/lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/13b/qlora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/34b/lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/34b/qlora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/7b/lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/7b/qlora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/code-llama/README.md
+++ b/examples/archived/code-llama/README.md
--- a/examples/archived/dbrx/16bit-lora.yaml
+++ b/examples/archived/dbrx/16bit-lora.yaml
@@ -54,7 +54,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch:
 saves_per_epoch: 1

--- a/examples/archived/dbrx/8bit-lora.yaml
+++ b/examples/archived/dbrx/8bit-lora.yaml
@@ -57,7 +57,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch:
 saves_per_epoch: 1

--- a/examples/archived/dbrx/README.md
+++ b/examples/archived/dbrx/README.md
--- a/examples/archived/dbrx/fft-ds-zero3.yaml
+++ b/examples/archived/dbrx/fft-ds-zero3.yaml
@@ -41,7 +41,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch:
 saves_per_epoch: 1

--- a/examples/archived/deepcoder/deepcoder-14B-preview-lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/falcon/config-7b-lora.yml
+++ b/examples/archived/falcon/config-7b-lora.yml
@@ -47,7 +47,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 40
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/falcon/config-7b-qlora.yml
+++ b/examples/archived/falcon/config-7b-qlora.yml
@@ -77,7 +77,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.000001
--- a/examples/archived/falcon/config-7b.yml
+++ b/examples/archived/falcon/config-7b.yml
@@ -44,7 +44,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 40
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/gemma/qlora.yml
+++ b/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:
--- a/examples/archived/gptj/qlora.yml
+++ b/examples/archived/gptj/qlora.yml
@@ -40,7 +40,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/jeopardy-bot/config.yml
+++ b/examples/archived/jeopardy-bot/config.yml
@@ -41,7 +41,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/mpt-7b/README.md
+++ b/examples/archived/mpt-7b/README.md
--- a/examples/archived/mpt-7b/config.yml
+++ b/examples/archived/mpt-7b/config.yml
@@ -42,7 +42,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001
--- a/examples/archived/openllama-3b/README.md
+++ b/examples/archived/openllama-3b/README.md
--- a/examples/archived/openllama-3b/config.yml
+++ b/examples/archived/openllama-3b/config.yml
@@ -42,7 +42,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/openllama-3b/lora.yml
+++ b/examples/archived/openllama-3b/lora.yml
@@ -50,7 +50,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/openllama-3b/qlora.yml
+++ b/examples/archived/openllama-3b/qlora.yml
@@ -43,7 +43,7 @@ logging_steps: 1
 flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.1
--- a/examples/archived/pythia-12b/README.md
+++ b/examples/archived/pythia-12b/README.md
--- a/examples/archived/pythia-12b/config.yml
+++ b/examples/archived/pythia-12b/config.yml
--- a/examples/archived/pythia/lora.yml
+++ b/examples/archived/pythia/lora.yml
--- a/examples/archived/qwen/README.md
+++ b/examples/archived/qwen/README.md
--- a/examples/archived/qwen/lora.yml
+++ b/examples/archived/qwen/lora.yml
@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/qwen/qlora.yml
+++ b/examples/archived/qwen/qlora.yml
@@ -49,7 +49,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention:

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/qwen/qwen2-moe-lora.yaml
+++ b/examples/archived/qwen/qwen2-moe-lora.yaml
@@ -45,7 +45,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/qwen/qwen2-moe-qlora.yaml
+++ b/examples/archived/qwen/qwen2-moe-qlora.yaml
@@ -48,7 +48,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/redpajama/README.md
+++ b/examples/archived/redpajama/README.md
--- a/examples/archived/redpajama/config-3b.yml
+++ b/examples/archived/redpajama/config-3b.yml
@@ -43,7 +43,7 @@ logging_steps: 5
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0001
--- a/examples/archived/replit-3b/config-lora.yml
+++ b/examples/archived/replit-3b/config-lora.yml
@@ -41,7 +41,7 @@ logging_steps: 1
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0
--- a/examples/archived/stablelm-2/1.6b/fft.yml
+++ b/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_steps: 100
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1

--- a/examples/archived/stablelm-2/1.6b/lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/stablelm-2/README.md
+++ b/examples/archived/stablelm-2/README.md
--- a/examples/archived/starcoder2/qlora.yml
+++ b/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_steps: 20
+warmup_ratio: 0.1
 evals_per_epoch: 4
 eval_steps:
 saves_per_epoch: 4
--- a/examples/archived/tiny-llama/README.md
+++ b/examples/archived/tiny-llama/README.md
--- a/examples/archived/tiny-llama/lora-mps.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 0
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/tiny-llama/lora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/tiny-llama/pretrain.yml
+++ b/examples/archived/tiny-llama/pretrain.yml
@@ -38,7 +38,7 @@ resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true

-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/tiny-llama/qlora.yml
+++ b/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_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/xgen-7b/xgen-7b-8k-qlora.yml
+++ b/examples/archived/xgen-7b/xgen-7b-8k-qlora.yml
@@ -75,7 +75,7 @@ xformers_attention: true
 flash_attention:
 gptq_groupsize:
 gptq_model_v1:
-warmup_steps: 10
+warmup_ratio: 0.1
 evals_per_epoch: 4
 saves_per_epoch: 1
 weight_decay: 0.0
--- a/examples/archived/yi-34B-chat/README.md
+++ b/examples/archived/yi-34B-chat/README.md
--- a/examples/archived/yi-34B-chat/qlora.yml
+++ b/examples/archived/yi-34B-chat/qlora.yml
@@ -20,7 +20,7 @@ special_tokens:
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-warmup_steps: 10
+warmup_ratio: 0.1

 # Iterations
 num_epochs: 1
--- a/examples/cohere/command-r-7b-qlora.yml
+++ b/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:
@@ -35,7 +35,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:

-
 gradient_accumulation_steps: 4
 micro_batch_size: 1
 num_epochs: 4
@@ -56,3 +55,5 @@ evals_per_epoch:
 saves_per_epoch: 1
 weight_decay: 0.0
 special_tokens:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config
--- a/examples/colab-notebooks/colab-axolotl-example.ipynb
+++ b/examples/colab-notebooks/colab-axolotl-example.ipynb
--- a/Show More
+++ b/Show More