📝 Add docstrings to 202512-raise_on_drop

Docstrings generation was requested by @kallewoof.

* https://github.com/axolotl-ai-cloud/axolotl/pull/3321#issuecomment-3668489902

The following files were modified:

* `src/axolotl/utils/data/utils.py`
* `src/axolotl/utils/trainer.py`

.github/workflows/base.yml

@@ -25,18 +25,32 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "128"
-            cuda_version: 12.8.1
+          - cuda: "126"
+            cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.8.0
+            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: ""
+            python_version: "3.11"
+            pytorch: 2.7.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
             python_version: "3.11"
-            pytorch: 2.9.0
+            pytorch: 2.7.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.8.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
           - cuda: "128"
@@ -107,6 +121,20 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: "126"
+            cuda_version: 12.6.3
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.7.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.7.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -121,13 +149,6 @@ jobs:
             pytorch: 2.9.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""

.github/workflows/docs.yml

@@ -12,9 +12,6 @@ jobs:
     build-deploy:
         runs-on: ubuntu-latest
         steps:
-        - name: cleanup node
-          run: |
-            sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
         - name: Check out repository
           uses: actions/checkout@v4
         - name: Set up Quarto

.github/workflows/main.yml

@@ -15,6 +15,21 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - 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: vllm
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -31,11 +46,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-#          - cuda: 130
-#            cuda_version: 13.0.0
-#            python_version: "3.11"
-#            pytorch: 2.9.1
-#            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -82,6 +92,27 @@ jobs:
     strategy:
       matrix:
         include:
+          - 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:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -98,11 +129,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-#          - cuda: 130
-#            cuda_version: 13.0.0
-#            python_version: "3.11"
-#            pytorch: 2.9.1
-#            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -144,18 +170,24 @@ jobs:
     strategy:
       matrix:
         include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
+            is_latest:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
             is_latest:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            is_latest:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -19,9 +19,6 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
-env:
-  MODAL_IMAGE_BUILDER_VERSION: "2025.06"
-
 jobs:
   test-axolotl-multigpu:
     if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
@@ -29,6 +26,13 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
+            num_gpus: 2
+            nightly_build: "true"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -39,7 +43,7 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.9.0
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
             nightly_build: "true"
@@ -55,7 +59,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
@@ -68,4 +72,4 @@ jobs:
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
-          modal run -m cicd.multigpu
+          modal run cicd.multigpu

.github/workflows/nightlies.yml

@@ -12,15 +12,15 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
@@ -64,15 +64,15 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/preview-docs.yml

@@ -11,7 +11,6 @@ on:
       - '_quarto.yml'
       - docs/scripts/generate_config_docs.py
       - src/axolotl/utils/schemas/**.py
-      - .github/workflows/preview-docs.yml
 
 permissions:
   checks: write
@@ -28,10 +27,6 @@ jobs:
     runs-on: ubuntu-latest
     if: ${{ !github.event.pull_request.draft }}
     steps:
-      - name: cleanup node
-        run: |
-          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
-
       - name: Check out repository
         uses: actions/checkout@v4
         with:

.github/workflows/tests-nightly.yml

@@ -26,7 +26,7 @@ jobs:
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
+        pytorch_version: ["2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -99,17 +99,17 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.8.0
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
@@ -123,7 +123,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 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 }}" >> $GITHUB_ENV
@@ -148,10 +148,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.7.1
             num_gpus: 2
             axolotl_extras:
             nightly_build: "true"
@@ -165,7 +165,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

.github/workflows/tests.yml

@@ -55,7 +55,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
+        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
     timeout-minutes: 20
 
     steps:
@@ -66,13 +66,12 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore Cache from S3
-        id: hf-cache-restore-s3
-        run: |
-          mkdir -p ~/.cache/huggingface/hub
-          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
-          ls -ltr ~/.cache/huggingface/hub/
-
+#      - name: Restore Cache from S3
+#        id: hf-cache-restore-s3
+#        run: |
+#          mkdir -p ~/.cache/huggingface/hub
+#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
+#
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -112,13 +111,10 @@ jobs:
         run: |
           huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
 
-      - name: Show HF cache
-        run: hf cache scan
-
       - name: Run tests
         run: |
           df -h
-          pytest -v --durations=10 -n4 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
           df -h
           pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           df -h
@@ -126,9 +122,6 @@ jobs:
           df -h
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
-      - name: Show HF cache
-        run: hf cache scan
-
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
         with:
@@ -145,7 +138,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
+        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
     timeout-minutes: 20
 
     steps:
@@ -156,13 +149,12 @@ jobs:
       - name: Check out repository code
         uses: actions/checkout@v4
 
-      - name: Restore Cache from S3
-        id: hf-cache-restore-s3
-        run: |
-          mkdir -p ~/.cache/huggingface/hub
-          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xpf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd --strip-components=1
-          ls -ltr ~/.cache/huggingface/hub/
-
+#      - name: Restore Cache from S3
+#        id: hf-cache-restore-s3
+#        run: |
+#          mkdir -p ~/.cache/huggingface/hub
+#          curl -L https://d1dttdx32dkk5p.cloudfront.net/hf-cache.tar.zst | tar -xf - -C ~/.cache/huggingface/hub/  --use-compress-program unzstd
+#
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
@@ -204,13 +196,10 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n4 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
           pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/
 
-      - name: Show HF cache
-        run: hf cache scan
-
   gate-skip-e2e:
     needs: [pre-commit, pytest, pytest-sdist]
     runs-on: ubuntu-latest
@@ -271,7 +260,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 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 }}" >> $GITHUB_ENV
@@ -303,6 +292,18 @@ jobs:
       fail-fast: false
       matrix:
         include:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            num_gpus: 1
+            axolotl_extras:
+#          - cuda: 128
+#            cuda_version: 12.8.1
+#            python_version: "3.11"
+#            pytorch: 2.7.1
+#            num_gpus: 1
+#            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -313,7 +314,7 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.9.0
             num_gpus: 1
             axolotl_extras:
     steps:
@@ -326,7 +327,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV
@@ -353,10 +354,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
     steps:
@@ -369,7 +370,7 @@ jobs:
       - name: Install Modal
         run: |
           python -m pip install --upgrade pip
-          pip install modal==1.3.0.post1 jinja2
+          pip install modal==1.0.2 jinja2
       - name: Update env vars
         run: |
           echo "BASE_TAG=main-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}" >> $GITHUB_ENV

.pre-commit-config.yaml

@@ -11,13 +11,13 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.10
+    rev: v0.14.7
     hooks:
     -   id: ruff
         args: [--fix]
     -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.19.1
+    rev: v1.19.0
     hooks:
     - id: mypy
       additional_dependencies:

README.md

@@ -29,15 +29,15 @@
 
 ## 🎉 Latest Updates
 
-- 2025/12: Axolotl now includes support for [Kimi-Linear](https://docs.axolotl.ai/docs/models/kimi-linear.html), [Plano-Orchestrator](https://docs.axolotl.ai/docs/models/plano.html), [MiMo](https://docs.axolotl.ai/docs/models/mimo.html), [InternVL 3.5](https://docs.axolotl.ai/docs/models/internvl3_5.html), [Olmo3](https://docs.axolotl.ai/docs/models/olmo3.html), [Trinity](https://docs.axolotl.ai/docs/models/trinity.html), and [Ministral3](https://docs.axolotl.ai/docs/models/ministral3.html).
-- 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://docs.axolotl.ai/docs/models/qwen3-next.html), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://docs.axolotl.ai/docs/models/qwen3.html), [Granite 4](https://docs.axolotl.ai/docs/models/granite4.html), [HunYuan](https://docs.axolotl.ai/docs/models/hunyuan.html), [Magistral 2509](https://docs.axolotl.ai/docs/models/magistral/vision.html), [Apertus](https://docs.axolotl.ai/docs/models/apertus.html), and [Seed-OSS](https://docs.axolotl.ai/docs/models/seed-oss.html).
+- 2025/12: Axolotl now includes support for [Olmo3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/olmo3), [Trinity](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/trinity), and [Ministral3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/ministral3).
+- 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/qwen3-next), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3), [Granite 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/granite4), [HunYuan](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/hunyuan), [Magistral 2509](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral#vision), [Apertus](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/apertus), and [Seed-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/seed-oss).
 - 2025/09: Axolotl now has text diffusion training. Read more [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion).
 - 2025/08: QAT has been updated to include NVFP4 support. See [PR](https://github.com/axolotl-ai-cloud/axolotl/pull/3107).
 - 2025/07:
   - ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the [blog post](https://huggingface.co/blog/accelerate-nd-parallel) for more info.
-  - Axolotl adds more models: [GPT-OSS](https://docs.axolotl.ai/docs/models/gpt-oss.html), [Gemma 3n](https://docs.axolotl.ai/docs/models/gemma3n.html), [Liquid Foundation Model 2 (LFM2)](https://docs.axolotl.ai/docs/models/LiquidAI.html), and [Arcee Foundation Models (AFM)](https://docs.axolotl.ai/docs/models/arcee.html).
+  - Axolotl adds more models: [GPT-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss), [Gemma 3n](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n), [Liquid Foundation Model 2 (LFM2)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2), and [Arcee Foundation Models (AFM)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm).
   - FP8 finetuning with fp8 gather op is now possible in Axolotl via `torchao`. Get started [here](https://docs.axolotl.ai/docs/mixed_precision.html#sec-fp8)!
-  - [Voxtral](https://docs.axolotl.ai/docs/models/voxtral.html), [Magistral 1.1](https://docs.axolotl.ai/docs/models/magistral.html), and [Devstral](https://docs.axolotl.ai/docs/models/devstral.html) with mistral-common tokenizer support has been integrated in Axolotl!
+  - [Voxtral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral), [Magistral 1.1](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral), and [Devstral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral) with mistral-common tokenizer support has been integrated in Axolotl!
   - 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!
 - 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
 
@@ -46,8 +46,8 @@
 <summary>Expand older updates</summary>
 
 - 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
-- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [docs](https://docs.axolotl.ai/docs/models/magistral.html) to start training your own Magistral models with Axolotl!
-- 2025/04: Llama 4 support has been added in Axolotl. See [docs](https://docs.axolotl.ai/docs/models/llama-4.html) to start training your own Llama 4 models with Axolotl's linearized version!
+- 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!
+- 2025/04: 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!
 - 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!
 - 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try.
 - 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun!
@@ -77,7 +77,7 @@ Features:
 
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
 - Python 3.11
-- PyTorch ≥2.8.0
+- PyTorch ≥2.7.1
 
 ### Google Colab
 

_quarto.yml

@@ -1,8 +1,6 @@
 project:
   type: website
-  pre-render:
-   - docs/scripts/generate_config_docs.py
-   - docs/scripts/generate_examples_docs.py
+  pre-render: docs/scripts/generate_config_docs.py
 
 quartodoc:
   dir: docs/api
@@ -242,46 +240,6 @@ website:
             - docs/getting-started.qmd
             - docs/installation.qmd
             - docs/inference.qmd
-            - section: "Model Guides"
-              contents:
-                - docs/models/kimi-linear.qmd
-                - docs/models/plano.qmd
-                - docs/models/mimo.qmd
-                - docs/models/internvl3_5.qmd
-                - docs/models/olmo3.qmd
-                - docs/models/trinity.qmd
-                - docs/models/arcee.qmd
-                - docs/models/mistral.qmd
-                - section: "Ministral3"
-                  contents:
-                    - docs/models/ministral3.qmd
-                    - docs/models/ministral3/think.qmd
-                    - docs/models/ministral3/vision.qmd
-                - section: "Magistral"
-                  contents:
-                    - docs/models/magistral.qmd
-                    - docs/models/magistral/think.qmd
-                    - docs/models/magistral/vision.qmd
-                - docs/models/ministral.qmd
-                - docs/models/mistral-small.qmd
-                - docs/models/voxtral.qmd
-                - docs/models/devstral.qmd
-                - docs/models/llama-4.qmd
-                - docs/models/llama-2.qmd
-                - docs/models/qwen3-next.qmd
-                - docs/models/qwen3.qmd
-                - docs/models/gemma3n.qmd
-                - docs/models/apertus.qmd
-                - docs/models/gpt-oss.qmd
-                - docs/models/seed-oss.qmd
-                - docs/models/phi.qmd
-                - docs/models/smolvlm2.qmd
-                - docs/models/granite4.qmd
-                - docs/models/LiquidAI.qmd
-                - docs/models/hunyuan.qmd
-                - docs/models/jamba.qmd
-                - docs/models/orpheus.qmd
-
             - docs/cli.qmd
             - docs/telemetry.qmd
             - docs/config-reference.qmd

cicd/multigpu.sh

@@ -2,7 +2,7 @@
 set -e
 
 # Only run two tests at a time to avoid OOM on GPU (with coverage collection)
-pytest -v --durations=10 -n2 --maxfail=4 \
+pytest -v --durations=10 -n2 \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \

docker/Dockerfile-base

@@ -51,7 +51,7 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN if [ "$PYTORCH_VERSION" =~ ^2\.9\.[0-9]+$ ] && [ "$CUDA" = "128" ] ; then \
+RUN if [ "$PYTORCH_VERSION" = "2.9.1" ] && [ "$CUDA" = "128" ] ; then \
         wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
         pip3 install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
         rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \

docs/.gitignore

@@ -3,5 +3,3 @@ _site/
 /api/*.qmd
 /api/*.html
 config-reference.qmd
-models/**/*.qmd
-models/**/*.html

docs/checkpoint_saving.qmd

@@ -1,86 +0,0 @@
----
-title: "Checkpoint Saving"
-format:
-  html:
-    toc: true
-    toc-depth: 2
-    number-sections: true
-execute:
-  enabled: false
----
-
-## Overview
-
-Axolotl supports on-demand checkpoint saving during training. You can trigger checkpoints via file-based triggers (for programmatic control) or Control+C (for interactive use).
-
-## File-Based Checkpoint Trigger
-
-### Configuration
-
-Enable in your config:
-
-```yaml
-dynamic_checkpoint:
-  enabled: true
-  check_interval: 100  # Optional: check every N steps (default: 100)
-  trigger_file_path: "axolotl_checkpoint.save"  # Optional: custom filename
-```
-
-**Options:**
-- `enabled`: `true` to enable (required)
-- `check_interval`: Steps between file checks. Default: 100. Lower = faster response, higher I/O overhead.
-- `trigger_file_path`: Custom trigger filename. Default: `axolotl_checkpoint.save`
-
-### How It Works
-
-1. Rank 0 checks for trigger file every `check_interval` steps in `output_dir`
-2. When detected, file is deleted and checkpoint is saved
-3. In distributed training, rank 0 broadcasts to synchronize all ranks
-
-### Usage
-
-**Command line:**
-```bash
-touch /path/to/output_dir/axolotl_checkpoint.save
-```
-
-**Programmatic:**
-```python
-from pathlib import Path
-Path("/path/to/output_dir/axolotl_checkpoint.save").touch()
-```
-
-Checkpoint saves within the next `check_interval` steps. The trigger file is auto-deleted after detection, so you can create it multiple times.
-
-**Custom filename:**
-```yaml
-dynamic_checkpoint:
-  enabled: true
-  trigger_file_path: "my_trigger.save"
-```
-```bash
-touch /path/to/output_dir/my_trigger.save
-```
-
-## Control+C (SIGINT) Checkpoint
-
-Pressing `Ctrl+C` during training saves the model state and exits gracefully. **Note:** This saves only the model weights, not optimizer state. For resumable checkpoints, use the file-based trigger.
-
-## Best Practices
-
-- **Check interval**: Lower values (10-50) for fast training, default 100 for slower training
-- **Distributed training**: Create trigger file once; rank 0 handles synchronization
-- **Resume**: Dynamic checkpoints can be resumed like regular checkpoints via `resume_from_checkpoint`
-
-## Example
-
-```yaml
-output_dir: ./outputs/lora-out
-save_steps: 500  # Scheduled checkpoints
-
-dynamic_checkpoint:
-  enabled: true
-  check_interval: 50
-```
-
-This enables scheduled checkpoints every 500 steps plus on-demand saves via file trigger (checked every 50 steps).

docs/docker.qmd

@@ -32,8 +32,11 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
 
 Tags examples:
 
-- `main-base-py3.11-cu128-2.8.0`
-- `main-base-py3.11-cu128-2.9.1`
+- `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
 
@@ -71,12 +74,15 @@ There may be some extra tags appended to the image, like `-vllm` which installs
 
 Tags examples:
 
-- `main-py3.11-cu128-2.8.0`
-- `main-py3.11-cu128-2.9.1`
+- `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-latest`
 - `main-20250303-py3.11-cu124-2.6.0`
 - `main-20250303-py3.11-cu126-2.6.0`
-- `0.12.0`
+- `0.10.1`
 
 ## Cloud
 

docs/installation.qmd

@@ -26,7 +26,7 @@ Follow the instructions at: [https://pytorch.org/get-started/locally/](https://p
 :::
 
 ::: {.callout-important}
-For Blackwell GPUs, please use Pytorch 2.9.1 and CUDA 12.8.
+For Blackwell GPUs, please use Pytorch 2.7.0 and CUDA 12.8.
 :::
 
 ### PyPI Installation (Recommended) {#sec-pypi}
@@ -111,7 +111,7 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
 :::
 
 ::: {.callout-important}
-For Blackwell GPUs, please use `axolotlai/axolotl:main-py3.11-cu128-2.9.1` or the cloud variant `axolotlai/axolotl-cloud:main-py3.11-cu128-2.9.1`.
+For Blackwell GPUs, please use `axolotlai/axolotl:main-py3.11-cu128-2.7.0` or the cloud variant `axolotlai/axolotl-cloud:main-py3.11-cu128-2.7.0`.
 :::
 
 Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.

docs/multimodal.qmd

@@ -21,7 +21,6 @@ format:
 - [Qwen2.5-VL](#sec-qwen25-vl)
 - [SmolVLM2](#sec-smolvlm2)
 - [LFM2-VL](#sec-lfm2-vl)
-- [Intern-VL](#sec-intern-vl)
 
 ## Usage
 
@@ -203,16 +202,6 @@ Please uninstall `causal-conv1d` via `pip3 uninstall -y causal-conv1d`
 base_model: LiquidAI/LFM2-VL-450M
 ```
 
-### Intern-VL {#sec-intern-vl}
-
-::: {.callout-tip}
-Please make sure to install `timm` via `pip3 install timm==1.0.19`
-:::
-
-```yaml
-base_model: OpenGVLab/InternVL3_5-8B
-```
-
 ## Dataset Format
 
 For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.

docs/scripts/examples-allowlist.yml

@@ -1,90 +0,0 @@
-examples:
-  # December 2025
-  - name: kimi-linear
-    title: Kimi Linear
-  - name: plano
-    title: Plano Orchestrator
-  - name: mimo
-    title: MiMo
-  - name: internvl3_5
-    title: InternVL 3.5
-
-  # AllenAI
-  - name: olmo3
-    title: OLMo 3
-
-  # ArceeAI
-  - name: trinity
-    title: Trinity
-  - name: arcee
-    title: Arcee AFM
-
-  # MistralAI
-  - name: ministral3/think
-    title: Ministral 3 Thinking
-  - name: ministral3/vision
-    title: Ministral 3 Vision
-  - name: magistral/think
-    title: Magistral Thinking
-  - name: magistral/vision
-    title: Magistral Vision
-  - name: ministral
-    title: Ministral
-  - name: mistral-small
-    title: Mistral Small 3.1/3.2
-  - name: voxtral
-    title: Voxtral
-  - name: devstral
-    title: Devstral
-  - name: mistral
-    title: Mistral 7B
-
-  # Meta
-  - name: llama-4
-    title: Llama 4
-  - name: llama-2
-    title: Llama 2
-
-  # Alibaba
-  - name: qwen3-next
-    title: Qwen 3 Next
-  - name: qwen3
-    title: Qwen 3
-
-  # Google
-  - name: gemma3n
-    title: Gemma 3n
-
-  # Swiss AI
-  - name: apertus
-    title: Apertus
-
-  # GPT-OSS
-  - name: gpt-oss
-    title: GPT-OSS
-  - name: seed-oss
-    title: Seed-OSS
-
-  # Microsoft
-  - name: phi
-    title: Phi
-
-  # SmolVLM
-  - name: smolvlm2
-    title: SmolVLM 2
-
-  # IBM
-  - name: granite4
-    title: Granite 4
-
-  # LiquidAI
-  - name: LiquidAI
-    title: Liquid Foundation Models 2
-
-  # Other
-  - name: hunyuan
-    title: Hunyuan
-  - name: jamba
-    title: Jamba
-  - name: orpheus
-    title: Orpheus

docs/scripts/generate_examples_docs.py

@@ -1,424 +0,0 @@
-"""
-auto generate example docs from allowlist
-"""
-
-import re
-import shutil
-import sys
-from pathlib import Path
-
-import yaml
-
-# Paths
-THIS = Path(__file__).resolve()
-ROOT = THIS.parents[2]  # repo root (docs/scripts -> docs -> ROOT)
-EXAMPLES_DIR = ROOT / "examples"
-OUTPUT_DIR = ROOT / "docs" / "models"
-ALLOWLIST_YML = THIS.parent / "examples-allowlist.yml"
-
-
-def slugify(name: str) -> str:
-    """Convert a name to a slug (lowercase, hyphens for spaces)."""
-    s = re.sub(r"[^a-zA-Z0-9\s\-]+", "", name.strip())
-    s = re.sub(r"\s+", "-", s).strip("-").lower()
-    return s or "example"
-
-
-def read_allowlist():
-    with open(ALLOWLIST_YML, "r", encoding="utf-8") as f:
-        data = yaml.safe_load(f) or {}
-    items = data.get("examples", [])
-    if not isinstance(items, list):
-        raise ValueError("`examples` must be a list in examples-allowlist.yml")
-    return items
-
-
-def find_readme(folder: Path) -> Path | None:
-    for name in ("README.md", "Readme.md", "readme.md"):
-        p = folder / name
-        if p.exists():
-            return p
-    return None
-
-
-def remove_first_h1(md: str) -> tuple[str, str | None]:
-    """
-    Remove the first H1 from markdown and return (modified_md, h1_title).
-    The H1 is removed since we use the frontmatter title instead.
-    """
-    lines = md.splitlines()
-    result = []
-    h1_title = None
-    skipped_first = False
-
-    for line in lines:
-        if not skipped_first and line.startswith("# "):
-            h1_title = line[2:].strip()
-            skipped_first = True
-            continue
-        result.append(line)
-
-    return "\n".join(result), h1_title
-
-
-IMG_RE = re.compile(r"!\[[^\]]*\]\(([^)]+)\)")
-LINK_RE = re.compile(r"\[([^\]]+)\]\(([^)]+)\)")
-
-
-def rewrite_and_copy_assets(md: str, src_dir: Path, dest_assets_root: Path) -> str:
-    """
-    Copy local image assets referenced in markdown to
-    docs/examples/assets/... and rewrite the links.
-    """
-    dest_assets = dest_assets_root / "assets"
-
-    def repl(m):
-        url = m.group(1).strip()
-        if re.match(r"^(https?:)?//", url):
-            return m.group(0)  # leave remote URLs
-        src_path = (src_dir / url).resolve()
-        if not src_path.exists():
-            return m.group(0)  # leave as-is if not found
-        rel = src_path.relative_to(src_dir)
-        # Create a unique asset path based on source directory name
-        asset_name = src_dir.name.replace("/", "-")
-        dest_path = dest_assets / asset_name / rel
-        dest_path.parent.mkdir(parents=True, exist_ok=True)
-        shutil.copy2(src_path, dest_path)
-        new_rel = f"assets/{asset_name}/{rel.as_posix()}"
-        return m.group(0).replace(url, new_rel)
-
-    return IMG_RE.sub(repl, md)
-
-
-def rewrite_readme_links(
-    md: str,
-    src_dir: Path,
-    examples_dir: Path,
-    parent_index_only: set,
-    current_src_path: str,
-    allowlist_entries: set,
-    current_output_path: str,
-) -> str:
-    """
-    Rewrite links between README.md files to point to the correct .qmd files.
-    """
-
-    def repl(m):
-        text = m.group(1)
-        url = m.group(2).strip()
-
-        # Skip remote URLs and anchor links
-        if re.match(r"^(https?:)?//", url) or url.startswith("#"):
-            return m.group(0)
-
-        # Skip non-markdown files
-        if not url.lower().endswith(".md"):
-            return m.group(0)
-
-        # Resolve the target path
-        try:
-            target_path = (src_dir / url).resolve()
-
-            # Check if target is outside examples_dir
-            try:
-                rel_path = target_path.relative_to(examples_dir)
-            except ValueError:
-                # Target is outside examples_dir, leave as-is
-                return m.group(0)
-
-            parts = list(rel_path.parts)
-
-            # Determine the output path for the target
-            if len(parts) > 0 and parts[-1].lower() in ("readme.md", "readme"):
-                # This is a README link
-                if len(parts) == 1:
-                    # Link to root README -> index.qmd
-                    target_output = "index.qmd"
-                elif len(parts) == 2:
-                    if parts[0] == ".":
-                        # Current directory README
-                        target_output = "index.qmd"
-                    else:
-                        # subdir/README.md
-                        parent_dir = parts[0]
-                        if parent_dir in parent_index_only:
-                            target_output = f"{parent_dir}/index.qmd"
-                        else:
-                            target_output = f"{parent_dir}.qmd"
-                else:
-                    # Deeper nesting: parent/subdir/README.md
-                    # Build the full path like "parent/subdir"
-                    full_path = "/".join(parts[:-1])  # Remove README.md
-                    # Check if this exact path is in allowlist
-                    if full_path in allowlist_entries:
-                        # This is a sub-entry with its own entry -> use .qmd
-                        target_output = f"{full_path}.qmd"
-                    elif parts[0] == ".":
-                        # ./subdir/README.md -> check if subdir has own entry
-                        subdir = parts[1]
-                        if subdir in parent_index_only:
-                            target_output = f"{subdir}/index.qmd"
-                        else:
-                            target_output = f"{subdir}.qmd"
-                    else:
-                        # parent/subdir where parent doesn't have own entry
-                        target_output = f"{full_path}/index.qmd"
-            else:
-                # Regular .md file -> convert to .qmd, keep path structure
-                target_output = "/".join(parts)[:-2] + "qmd"
-
-            # Compute relative path from current output file to target
-            current_parts = current_output_path.split("/")
-            target_parts = target_output.split("/")
-
-            # Special case: if current is a subdir file and target is a single-component file at root
-            # Example: current="magistral/vision", target="magistral.qmd"
-            if len(current_parts) > 1 and len(target_parts) == 1:
-                # Current is in subdir, target is at root level
-                # Go up to root: ../ for each level
-                up_count = len(current_parts) - 1
-                rel_parts = [".."] * up_count + [target_parts[0]]
-                new_url = "/".join(rel_parts)
-            else:
-                # Find common prefix
-                i = 0
-                while (
-                    i < min(len(current_parts) - 1, len(target_parts))
-                    and current_parts[i] == target_parts[i]
-                ):
-                    i += 1
-
-                # Build relative path: go up (../) then down to target
-                up_count = len(current_parts) - 1 - i
-                rel_parts = [".."] * up_count + target_parts[i:]
-
-                if not rel_parts or rel_parts == [".."]:
-                    # Points to same directory or parent
-                    new_url = "/".join(rel_parts) if rel_parts else "."
-                else:
-                    new_url = "/".join(rel_parts)
-
-            return f"[{text}]({new_url})"
-        except (ValueError, IndexError):
-            return m.group(0)
-
-    return LINK_RE.sub(repl, md)
-
-
-def write_qmd(out_path: Path, title: str, body_md: str):
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    fm = f"---\ntitle: {title!r}\nexecute:\n  eval: false\nformat:\n  html:\n    toc: true\n---\n\n"
-    out_path.write_text(fm + body_md, encoding="utf-8")
-
-
-def update_quarto_yml(generated: list[tuple[str, str, str]]):
-    """
-    Update _quarto.yml with the generated example files in the correct order.
-    This keeps the sidebar in sync with the allowlist.
-
-    Model Guides is now nested under "Getting Started" section.
-    Creates nested sections for models with sub-entries (e.g., magistral, ministral3).
-    Parent pages are now flat files (e.g., ministral3.qmd) with sub-pages in subdirs.
-    """
-    quarto_yml = ROOT / "_quarto.yml"
-    if not quarto_yml.exists():
-        print(f"[WARN] {quarto_yml} not found, skipping update", file=sys.stderr)
-        return
-
-    content = quarto_yml.read_text(encoding="utf-8")
-
-    # First pass: find all parents that have sub-entries
-    parents_with_subs = set()
-    for path, _name, _title in generated:
-        if "/" in path:
-            parent = path.split("/")[0]
-            parents_with_subs.add(parent)
-
-    # Build the YAML contents while preserving allowlist order
-    lines = []
-    processed_sections = set()
-
-    for path, _name, title in generated:
-        # Check if this is a parent page that has sub-pages
-        if path in parents_with_subs:
-            # This is a parent page with sub-pages - create a nested section
-            if path not in processed_sections:
-                processed_sections.add(path)
-                section_title = (
-                    title or path.replace("-", " ").replace("_", " ").title()
-                )
-                lines.append(f'                - section: "{section_title}"')
-                lines.append("                  contents:")
-                # Add the parent page first
-                lines.append(f"                    - docs/models/{path}.qmd")
-                # Then add all sub-pages
-                for sub_path, _sub_name, _sub_title in generated:
-                    if "/" in sub_path and sub_path.split("/")[0] == path:
-                        lines.append(
-                            f"                    - docs/models/{sub_path}.qmd"
-                        )
-        elif "/" not in path:
-            # This is a flat item with no sub-pages
-            # Skip if it was already included as part of a parent section
-            if path not in processed_sections:
-                lines.append(f"                - docs/models/{path}.qmd")
-
-    yaml_content = "\n".join(lines) + "\n"
-
-    # Pattern to match only the Model Guides contents, stopping at the next item
-    # in Getting Started (lines starting with 12 spaces: same level as the section)
-    pattern = r'(            - section: "Model Guides"\n              contents:)([^\n]*|.*?)(?=\n            - |\n        - section:|\n\nformat:)'
-
-    def replacement(match):
-        prefix = match.group(1)
-        return prefix + "\n" + yaml_content
-
-    new_content = re.sub(pattern, replacement, content, flags=re.DOTALL)
-
-    if new_content != content:
-        quarto_yml.write_text(new_content, encoding="utf-8")
-        print(f"Updated {quarto_yml}")
-    else:
-        print(f"No changes needed for {quarto_yml}")
-
-
-def main():
-    allow = read_allowlist()
-    if not EXAMPLES_DIR.exists():
-        print(f"[WARN] {EXAMPLES_DIR} not found", file=sys.stderr)
-        return
-
-    (OUTPUT_DIR / "assets").mkdir(parents=True, exist_ok=True)
-
-    # First pass: identify which parents have their own entry vs only sub-entries
-    parent_entries = set()  # Parents that have their own entry
-    parent_with_subs = set()  # Parents that have sub-entries
-    allowlist_entries = set()  # All entries in allowlist
-
-    for item in allow:
-        if isinstance(item, str):
-            name = item
-        else:
-            name = item.get("name")
-
-        allowlist_entries.add(name)
-
-        if "/" in name:
-            parent = name.split("/")[0]
-            parent_with_subs.add(parent)
-        else:
-            parent_entries.add(name)
-
-    # Parents with subs that DON'T have their own entry -> use index.qmd
-    parent_index_only = parent_with_subs - parent_entries
-
-    generated = []
-    seen_dirs = set()  # Track which parent directories we've created index for
-
-    for item in allow:
-        if isinstance(item, str):
-            name = item
-            title = None
-        else:
-            name = item.get("name")
-            title = item.get("title")
-
-        if not name:
-            print(f"[WARN] Skipping item without name: {item}", file=sys.stderr)
-            continue
-
-        src_dir = EXAMPLES_DIR / name
-        if not src_dir.exists() or not src_dir.is_dir():
-            print(f"[WARN] Skipping {name} (not a directory)", file=sys.stderr)
-            continue
-
-        readme = find_readme(src_dir)
-        if not readme:
-            print(f"[WARN] Skipping {name} (no README.md)", file=sys.stderr)
-            continue
-
-        md = readme.read_text(encoding="utf-8")
-
-        # Determine output path first (needed for link rewriting)
-        parts = name.split("/")
-        if len(parts) == 1:
-            # Simple case: no subdirectory
-            out_path = OUTPUT_DIR / f"{parts[0]}.qmd"
-            sidebar_path = parts[0]
-        else:
-            # Has subdirectory: e.g., magistral/think
-            parent = parts[0]
-            child = "-".join(parts[1:])  # handle nested subdirs
-            out_path = OUTPUT_DIR / parent / f"{child}.qmd"
-            sidebar_path = f"{parent}/{child}"
-
-        # Remove the first H1 (we use frontmatter title instead)
-        md, _ = remove_first_h1(md)
-        # Rewrite links between README files
-        md = rewrite_readme_links(
-            md,
-            src_dir,
-            EXAMPLES_DIR,
-            parent_index_only,
-            name,
-            allowlist_entries,
-            sidebar_path,
-        )
-        md = rewrite_and_copy_assets(md, src_dir, OUTPUT_DIR)
-
-        # Handle parent page generation for sub-entries
-        if len(parts) > 1:
-            # Has subdirectory: e.g., magistral/think
-            parent = parts[0]
-
-            # Create parent.qmd if not already done and parent doesn't have own entry
-            if parent not in seen_dirs and parent in parent_index_only:
-                parent_readme = find_readme(EXAMPLES_DIR / parent)
-                if parent_readme:
-                    parent_md = parent_readme.read_text(encoding="utf-8")
-                    parent_md, _ = remove_first_h1(parent_md)
-                    parent_md = rewrite_readme_links(
-                        parent_md,
-                        EXAMPLES_DIR / parent,
-                        EXAMPLES_DIR,
-                        parent_index_only,
-                        parent,
-                        allowlist_entries,
-                        parent,
-                    )
-                    parent_md = rewrite_and_copy_assets(
-                        parent_md, EXAMPLES_DIR / parent, OUTPUT_DIR
-                    )
-                    parent_title = parent.replace("-", " ").replace("_", " ").title()
-                    write_qmd(OUTPUT_DIR / f"{parent}.qmd", parent_title, parent_md)
-                    generated.append((parent, parent, parent_title))
-                    seen_dirs.add(parent)
-
-        if not title:
-            title = name.replace("/", " ").replace("-", " ").title()
-
-        write_qmd(out_path, title, md)
-        generated.append((sidebar_path, name, title))
-
-    # Index page - preserve allowlist order
-    if generated:
-        listing = "\n".join(
-            [f"- [{title}]({path}.qmd)" for path, name, title in generated]
-        )
-        index_md = (
-            "# Model Guides\n\nBelow are the curated examples for training various model architectures:\n\n"
-            + listing
-            + "\n"
-        )
-        index_fm = (
-            "---\nexecute:\n  eval: false\nformat:\n  html:\n    toc: true\n---\n\n"
-        )
-        (OUTPUT_DIR / "index.qmd").write_text(index_fm + index_md, encoding="utf-8")
-
-        # Auto-update _quarto.yml to keep sidebar in sync
-        update_quarto_yml(generated)
-
-
-if __name__ == "__main__":
-    main()

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@318b7e2\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88\""
    ]
   },
   {

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -32,10 +32,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -28,10 +28,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -29,10 +29,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -28,10 +28,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 2
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -41,10 +41,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 8
 micro_batch_size: 1
 num_epochs: 1

examples/gpt-oss/gpt-oss-safeguard-20b-sft-lora-singlegpu.yaml

@@ -41,10 +41,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-trackio_project_name:
-trackio_run_name:
-trackio_space_id:
-
 gradient_accumulation_steps: 8
 micro_batch_size: 1
 num_epochs: 1

examples/internvl3_5/README.md

@@ -1,43 +0,0 @@
-# Finetune OpenGV's InternVL with Axolotl
-
-[InternVL 3.5](https://huggingface.co/OpenGVLab/InternVL3_5-8B-HF) is a family of powerful vision-language models supporting dynamic resolution and multi-image understanding by OpenGV. It features a ViT-style vision encoder and strong language model backbone for tasks like visual question answering, OCR, and scene text understanding.
-
-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).
-
-2. Install `timm` for vision model support:
-
-    ```bash
-    pip install timm==1.0.19
-    ```
-
-3. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-4. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/internvl3_5/internvl3_5-8b-qlora.yml
-    ```
-
-This config uses about 8.21 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
-
-### 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 dataset format follows the multi-modal format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [InternVL Paper](https://huggingface.co/papers/2508.18265)
-- [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/internvl3_5/internvl3_5-8b-qlora.yml

@@ -1,61 +0,0 @@
-base_model: OpenGVLab/InternVL3_5-8B-HF
-processor_type: AutoProcessor
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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
-
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|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
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/kimi-linear/README.md

@@ -1,47 +0,0 @@
-# Finetune MoonshotAI's Kimi Linear with Axolotl
-
-[Kimi Linear](https://huggingface.co/collections/moonshotai/kimi-linear-a3b) is a MoE model (48B total, 3B active) by MoonshotAI using a hybrid linear attention architecture to achieve a 1M token context length. It uses Kimi Delta Attention (KDA), a refined version of Gated DeltaNet that reduces KV cache size by up to 75% and boosts decoding throughput by up to 6x for long contexts.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-**Note:** Axolotl uses experimental training code for Kimi Linear as their original modeling code is inference-only.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install CCE via [docs](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy)
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/kimi-linear/kimi-48b-lora.yaml
-    ```
-
-This config uses about 98.7GiB VRAM.
-
-Let us know how it goes. Happy finetuning!
-
-### TIPS
-
-- Kimi Linear requires `trust_remote_code: true`.
-- You can run a full finetuning by removing the `adapter: lora` and `load_in_8bit: true`.
-- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html)
-- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template)
-
-## Optimization Guides
-
-See 👉 [docs](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-This is not yet compatible with MoE kernels from transformers v5.
-
-## Related Resources
-
-- [Kimi Linear Paper](https://huggingface.co/papers/2510.26692)
-- [Kimi Linear GitHub](https://github.com/MoonshotAI/Kimi-Linear)
-- [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/kimi-linear/kimi-48b-lora.yaml

@@ -1,81 +0,0 @@
-base_model: moonshotai/Kimi-Linear-48B-A3B-Instruct
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-trust_remote_code: true
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-    split: train
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.2
-output_dir: ./outputs/lora-out
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_ratio: 0.1
-evals_per_epoch: 2
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/llama-3/3b-fp8-fsdp2.yaml

@@ -29,6 +29,7 @@ flex_attention: true
 flex_attn_compile_kwargs:
   dynamic: false
   mode: max-autotune-no-cudagraphs
+save_strategy: no
 torch_compile: true
 
 wandb_project:

examples/magistral/think/README.md

@@ -5,7 +5,6 @@ This guide covers fine-tuning [Magistral Small 2507](https://huggingface.co/mist
 ## Prerequisites
 
 Before starting, ensure you have:
-
 - Installed Axolotl (see [main README](../README.md))
 
 ## Getting Started

examples/magistral/vision/README.md

@@ -5,8 +5,7 @@ This guide covers fine-tuning [Magistral Small 2509](https://huggingface.co/mist
 ## Prerequisites
 
 Before starting, ensure you have:
-
-- Installed Axolotl from source (see [main README](../README.md))
+- Installed Axolotl from source (see [main README](../README.md#getting-started))
 
 ## Getting started
 

examples/mimo/README.md

@@ -1,39 +0,0 @@
-# Finetune Xiaomi's MiMo with Axolotl
-
-[MiMo](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL) is a family of models trained from scratch for reasoning tasks, incorporating **Multiple-Token Prediction (MTP)** as an additional training objective for enhanced performance and faster inference. Pre-trained on ~25T tokens with a three-stage data mixture strategy and optimized reasoning pattern density.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/mimo/mimo-7b-qlora.yaml
-    ```
-
-This config uses about 17.2 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
-
-### 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 dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Limitations
-
-**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for MiMo in the near future.
-
-## Related Resources
-
-- [MiMo Paper](https://arxiv.org/abs/2505.07608)
-- [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/mimo/mimo-7b-qlora.yaml

@@ -1,67 +0,0 @@
-base_model: XiaomiMiMo/MiMo-7B-RL
-trust_remote_code: true
-revision_of_model: 6299b5a
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# CCE - N/A as of now
-# plugins:
-#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - 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: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/think/README.md

@@ -5,7 +5,6 @@ This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collectio
 ## Prerequisites
 
 Before starting, ensure you have:
-
 - Installed Axolotl (see [main README](../README.md))
 
 ## Getting Started

examples/ministral3/vision/README.md

@@ -5,8 +5,7 @@ This guide covers fine-tuning [Ministral3 2512](https://huggingface.co/collectio
 ## Prerequisites
 
 Before starting, ensure you have:
-
-- Installed Axolotl from source (see [main README](../README.md))
+- Installed Axolotl from source (see [main README](../README.md#getting-started))
 
 ## Getting started
 

examples/mistral/mistral-small/README.md

@@ -5,7 +5,6 @@ This guide covers fine-tuning [Mistral Small 3.1](mistralai/Mistral-Small-3.1-24
 ## Prerequisites
 
 Before starting, ensure you have:
-
 - Installed Axolotl (see [Installation docs](https://docs.axolotl.ai/docs/installation.html))
 
 ## Getting Started

examples/olmo3/README.md

@@ -16,7 +16,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
     axolotl train examples/olmo3/olmo3-7b-qlora.yaml
     ```
 
-This uses about 11.3 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
+Let us know how it goes. Happy finetuning! 🚀
 
 ### TIPS
 

examples/olmo3/olmo3-7b-qlora.yaml

@@ -42,10 +42,10 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-gradient_accumulation_steps: 2
+gradient_accumulation_steps: 4
 micro_batch_size: 2
 num_epochs: 1
-optimizer: adamw_8bit
+optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 

examples/plano/README.md

@@ -1,42 +0,0 @@
-# Finetune Katanemo's Plano-Orchestrator with Axolotl
-
-[Plano-Orchestrator](https://huggingface.co/collections/katanemo/plano-orchestrator) is a family of 4B and 30B-A3B routing and orchestration models designed for multi-agent systems. It analyzes user intent and conversation context to make precise routing decisions, excelling at multi-turn context understanding, multi-intent detection, and context-dependent routing.
-
-This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
-
-## Getting started
-
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-3. Run the finetuning example:
-
-    ```bash
-    axolotl train examples/plano/plano-4b-qlora.yaml
-    ```
-
-This config uses about 5.1 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
-
-### Orchestration Prompt
-
-Plano-Orchestrator uses a specific orchestration prompt format for routing/agent decisions. Please check the [official model card](https://huggingface.co/katanemo/Plano-Orchestrator-4B) for proper prompt formatting and the `ORCHESTRATION_PROMPT` template.
-
-### Tips
-
-- To use the larger [Plano-Orchestrator-30B-A3B](https://huggingface.co/katanemo/Plano-Orchestrator-30B-A3B) MoE model, simply change `base_model: katanemo/Plano-Orchestrator-30B-A3B` in the config and enable multi-GPU training if needed.
-- 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 dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [Plano GitHub](https://github.com/katanemo/plano)
-- [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/plano/plano-4b-qlora.yaml

@@ -1,65 +0,0 @@
-base_model: katanemo/Plano-Orchestrator-4B
-
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-load_in_8bit: false
-load_in_4bit: true
-
-chat_template: qwen3
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/lora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 2048
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - 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: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qwen2/adamw-pretrain-fsdp2.yaml

@@ -1,70 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-# Use random initialization for fair comparison
-reinit_weights: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-# Pretraining dataset
-pretraining_dataset:
-  - path: allenai/c4
-    name: en
-    type: pretrain
-    split: train
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/compare-adamw-pretrain
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project: dist_muon
-wandb_entity:
-wandb_watch:
-wandb_name: adamw
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 4
-num_epochs: 1
-max_steps: 305
-
-# AdamW optimizer settings (standard LR for AdamW)
-optimizer: adamw_torch_fused
-learning_rate: 0.0002
-weight_decay: 0.01
-lr_scheduler: cosine
-
-train_on_inputs: true
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 0
-saves_per_epoch: 1
-
-# Reproducibility
-seed: 42
-
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_cpu_ram_efficient_loading: false
-  fsdp_reshard_after_forward: true
-
-special_tokens:

examples/qwen2/muon-pretrain-fsdp2.yaml

@@ -1,70 +0,0 @@
-base_model: Qwen/Qwen2.5-0.5B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-# Use random initialization for fair comparison
-reinit_weights: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-# Pretraining dataset
-pretraining_dataset:
-  - path: allenai/c4
-    name: en
-    type: pretrain
-    split: train
-
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./outputs/compare-muon-pretrain
-
-sequence_len: 2048
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project: dist_muon
-wandb_entity:
-wandb_watch:
-wandb_name: muon
-wandb_log_model:
-
-gradient_accumulation_steps: 1
-micro_batch_size: 4
-num_epochs: 1
-max_steps: 305
-
-# Muon optimizer settings
-optimizer: muon
-learning_rate: 0.02
-weight_decay: 0.01
-lr_scheduler: cosine
-
-train_on_inputs: true
-group_by_length: false
-bf16: auto
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-logging_steps: 1
-flash_attention: true
-
-warmup_steps: 10
-evals_per_epoch: 0
-saves_per_epoch: 1
-
-# Reproducibility
-seed: 42
-
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_cpu_ram_efficient_loading: false
-  fsdp_reshard_after_forward: true
-
-special_tokens:

examples/trinity/README.md

@@ -29,10 +29,6 @@ Let us know how it goes. Happy finetuning! 🚀
 
 Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
-## Limitations
-
-**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for Trinity in the near future.
-
 ## Related Resources
 
 - [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)

examples/trinity/trinity-nano-preview-qlora.yaml

@@ -1,6 +1,5 @@
 base_model: arcee-ai/Trinity-Nano-Preview
 trust_remote_code: true
-revision_of_model: 2ee94b0
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name

requirements.txt

@@ -14,21 +14,20 @@ huggingface_hub>=0.36.0
 peft>=0.18.0
 tokenizers>=0.22.1
 transformers==4.57.1
-accelerate==1.12.0
-datasets==4.4.2
-deepspeed>=0.18.3
-trl==0.25.1
+accelerate==1.11.0
+datasets==4.4.1
+deepspeed>=0.17.0
+trl==0.25.0
 hf_xet==1.2.0
-kernels==0.11.5
-trackio>=0.13.0
-typing-extensions>=4.15.0
+kernels>=0.9.0
+trackio
 
 optimum==1.16.2
 hf_transfer
 sentencepiece
-gradio>=6.2.0,<7.0
+gradio==5.49.1
 
-modal==1.3.0.post1
+modal==1.0.2
 pydantic>=2.10.6
 addict
 fire
@@ -63,12 +62,13 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.15.0
+torchao==0.13.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 
 axolotl-contribs-lgpl==0.0.7
-axolotl-contribs-mit==0.0.6
+axolotl-contribs-mit==0.0.5
+
 # telemetry
 posthog==6.7.11
 

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"'
 )

setup.py

@@ -156,7 +156,7 @@ extras_require = {
         "came_pytorch==0.1.3",
     ],
     "ray": [
-        "ray[train]>=2.52.1",
+        "ray[train]",
     ],
     "vllm": [
         "vllm==0.10.0",

src/axolotl/cli/cloud/baseten/template/train_sft.py

@@ -24,7 +24,8 @@ if launcher_args:
     launcher_args_str = "-- " + " ".join(launcher_args)
 
 # 1. Define a base image for your training job
-BASE_IMAGE = "axolotlai/axolotl:main-py3.11-cu128-2.9.1"
+# must use torch 2.7.0 for vllm
+BASE_IMAGE = "axolotlai/axolotl:main-py3.11-cu126-2.7.1"
 
 # 2. Define the Runtime Environment for the Training Job
 # This includes start commands and environment variables.a

src/axolotl/cli/cloud/modal_.py

@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu128-2.9.1"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"

src/axolotl/cli/config.py

@@ -26,7 +26,6 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.tee import prepare_debug_log
-from axolotl.utils.trackio_ import setup_trackio_env_vars
 from axolotl.utils.trainer import prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
@@ -228,7 +227,6 @@ def load_cfg(
         cfg,
         capabilities={
             "bf16": is_torch_bf16_gpu_available(),
-            "fp8": compute_supports_fp8(),
             "n_gpu": int(os.environ.get("WORLD_SIZE", 1)),
             "compute_capability": gpu_version,
         },
@@ -247,7 +245,6 @@ def load_cfg(
     setup_wandb_env_vars(cfg)
     setup_mlflow_env_vars(cfg)
     setup_comet_env_vars(cfg)
-    setup_trackio_env_vars(cfg)
     plugin_set_cfg(cfg)
 
     TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
@@ -262,11 +259,3 @@ def load_cfg(
     )
 
     return cfg
-
-
-def compute_supports_fp8() -> bool:
-    try:
-        compute_capability = torch.cuda.get_device_capability()
-        return compute_capability >= (9, 0)
-    except RuntimeError:
-        return False

src/axolotl/cli/inference.py

@@ -288,8 +288,8 @@ def do_inference_gradio(
         title=cfg.get("gradio_title", "Axolotl Gradio Interface"),
     )
 
-    demo.launch(
-        footer_links=["gradio", "settings"],
+    demo.queue().launch(
+        show_api=False,
         share=cfg.get("gradio_share", True),
         server_name=cfg.get("gradio_server_name", "127.0.0.1"),
         server_port=cfg.get("gradio_server_port", None),

src/axolotl/cli/utils/diffusion.py

@@ -366,8 +366,8 @@ def launch_diffusion_gradio_ui(
             outputs=[masked_preview, html_out],
         )
 
-        demo.launch(
-            footer_links=["gradio", "settings"],
+        demo.queue().launch(
+            show_api=False,
             share=cfg.get("gradio_share", True),
             server_name=cfg.get("gradio_server_name", "127.0.0.1"),
             server_port=cfg.get("gradio_server_port", None),

src/axolotl/core/attention/flex_block_mask.py

@@ -0,0 +1,158 @@
+"""
+monkeypatch for flex + packing
+"""
+
+import sys
+from typing import Callable, Optional, Union
+
+import torch
+from torch.nn.attention.flex_attention import BlockMask
+from transformers import Cache, PretrainedConfig
+from transformers.masking_utils import (
+    ALL_MASK_ATTENTION_FUNCTIONS,
+    _preprocess_mask_arguments,
+    and_masks,
+    causal_mask_function,
+    or_masks,
+)
+from transformers.utils import is_torch_greater_or_equal
+
+_is_torch_greater_or_equal_than_2_6 = is_torch_greater_or_equal("2.6", accept_dev=True)
+
+
+def create_causal_mask(
+    config: PretrainedConfig,
+    input_embeds: torch.Tensor,
+    attention_mask: torch.Tensor,
+    cache_position: torch.Tensor,
+    past_key_values: Optional[Cache],
+    or_mask_function: Optional[Callable] = None,
+    and_mask_function: Optional[Callable] = None,
+) -> Optional[Union[torch.Tensor, BlockMask]]:
+    """
+    Create a standard causal mask based on the attention implementation used (stored in the config). If `past_key_values`
+    has an HybridCache structure, this function will return the mask corresponding to one of the "full_attention" layers (to align
+    to what is needed in the `modeling_xxx.py` files).
+
+    Args:
+        config (`PretrainedConfig`):
+            The model config.
+        input_embeds (`torch.Tensor`):
+            The input embeddings of shape (batch_size, query_length, hidden_dim). This is used only to infer the
+            batch size, query length and dtype.
+        attention_mask (`torch.Tensor`, optional):
+            The 2D attention mask corresponding to padded tokens of shape (batch_size, number_of_seen_tokens+q_length).
+            It can also be an already prepared 4D mask, in which case it is returned as-is.
+        cache_position (`torch.Tensor`):
+            A tensor of shape (query_length,) indicating the current indices of the input sequence elements.
+        past_key_values (`Cache`, optional):
+            The past key values, if we use a cache.
+        or_mask_function (`Callable`, optional):
+            An optional mask function to combine with the causal mask function (by doing the union of both). This is
+            useful to easily overlay another mask on top of the causal one, for example for image tokens handling.
+        and_mask_function (`Callable`, optional):
+            An optional mask function to combine with the causal mask function (by doing the intersection of both). This is
+            useful to easily overlay another mask on top of the causal one, for example for image tokens handling.
+    """
+    # If we have an HybridCache structure, here we want to create the mask for the full layers
+    if (
+        past_key_values
+        and hasattr(past_key_values, "is_sliding")
+        and False in past_key_values.is_sliding
+    ):
+        layer_idx = past_key_values.is_sliding.index(False)
+    else:
+        layer_idx = 0
+
+    original_attention_mask = (
+        None
+        if attention_mask is None
+        else attention_mask.clone().to(cache_position.device)
+    )
+    early_exit, attention_mask, kv_length, kv_offset = _preprocess_mask_arguments(
+        config, input_embeds, attention_mask, cache_position, past_key_values, layer_idx
+    )
+    if early_exit:
+        return attention_mask
+
+    batch_size, total_seq_len = cache_position.shape
+    key_length = total_seq_len
+    document_ids = torch.nn.functional.pad(
+        original_attention_mask, value=0, pad=(0, key_length)
+    )
+
+    batch_size, dtype = input_embeds.shape[0], input_embeds.dtype
+    if attention_mask is not None:
+
+        def causal_doc_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
+            """
+            Defines the logic of a block causal mask by combining both a standard causal mask
+            and a block diagonal document mask.
+            See :func:`~torchtune.modules.attention_utils.create_block_causal_mask`
+            for an illustration.
+            """
+            causal_mask_ = q_idx >= kv_idx  # not valid when decoding
+            document_mask = (
+                document_ids[batch_idx, q_idx] == document_ids[batch_idx, kv_idx]
+            )
+            final_mask = causal_mask_ & document_mask
+            return final_mask
+
+        mask_factory_function = causal_doc_mask_mod
+    else:
+        mask_factory_function = causal_mask_function
+    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[config._attn_implementation]
+
+    # Do not allow skip if we are compiling (this is to match BC)
+    allow_is_causal_skip = (
+        not past_key_values.is_compileable if past_key_values is not None else True
+    )
+
+    # Allow slight deviations from causal mask
+    if or_mask_function is not None:
+        if not _is_torch_greater_or_equal_than_2_6:
+            raise ValueError(
+                "Using `or_mask_function` or `and_mask_function` arguments require torch>=2.6"
+            )
+        mask_factory_function = or_masks(mask_factory_function, or_mask_function)
+        allow_is_causal_skip = False
+    if and_mask_function is not None:
+        if not _is_torch_greater_or_equal_than_2_6:
+            raise ValueError(
+                "Using `or_mask_function` or `and_mask_function` arguments require torch>=2.6"
+            )
+        mask_factory_function = and_masks(mask_factory_function, and_mask_function)
+        allow_is_causal_skip = False
+
+    # We now create the mask
+    causal_mask = mask_interface(
+        batch_size=batch_size,
+        cache_position=cache_position,
+        kv_length=kv_length,
+        kv_offset=kv_offset,
+        mask_function=mask_factory_function,
+        attention_mask=attention_mask,
+        allow_is_causal_skip=allow_is_causal_skip,  # additional kwarg for sdpa
+        dtype=dtype,  # Additional kwarg for eager
+        config=config,  # Pass the config as well, in case someone wants to easily have their own mask_interface
+    )
+    return causal_mask
+
+
+def patch_create_causal_mask(model_type):
+    import transformers.masking_utils
+
+    transformers.masking_utils.create_causal_mask = create_causal_mask
+
+    if model_type:
+        try:
+            # Dynamically import the module and attention class
+            module_path = f"transformers.models.{model_type}.modeling_{model_type}"
+            module = __import__(module_path)
+            module.create_causal_mask = create_causal_mask
+            del sys.modules[module_path]
+        except (ImportError, AttributeError) as e:
+            raise ValueError(
+                f"Could not import attention class for model_type: {model_type}. "
+                f"Error: {str(e)}"
+            ) from e

src/axolotl/core/builders/base.py

@@ -35,7 +35,6 @@ from axolotl.utils import (
     is_comet_available,
     is_mlflow_available,
     is_opentelemetry_available,
-    is_trackio_available,
 )
 from axolotl.utils.callbacks import (
     GCCallback,
@@ -148,14 +147,6 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
-        if self.cfg.use_trackio and is_trackio_available():
-            from axolotl.utils.callbacks.trackio_ import (
-                SaveAxolotlConfigtoTrackioCallback,
-            )
-
-            callbacks.append(
-                SaveAxolotlConfigtoTrackioCallback(self.cfg.axolotl_config_path)
-            )
         if self.cfg.use_otel_metrics and is_opentelemetry_available():
             from axolotl.utils.callbacks.opentelemetry import (
                 OpenTelemetryMetricsCallback,
@@ -290,22 +281,11 @@ class TrainerBuilderBase(abc.ABC):
                 adam_kwargs["eps"] = training_args_kwargs.get("adam_epsilon")
 
             if self.cfg.optimizer == "muon":
-                _, device_mesh = build_parallelism_config(self.cfg)
-
-                if device_mesh is not None:
-                    from axolotl.contribs.mit.muon.dist_muon import (
-                        DistMuonOptimizerFactory,
-                    )
-
-                    optimizer_cls = DistMuonOptimizerFactory
-                    optimizer_kwargs["device_mesh"] = device_mesh
-                else:
-                    from axolotl.contribs.mit.muon import (
-                        MuonOptimizerFactory,
-                    )
-
-                    optimizer_cls = MuonOptimizerFactory
+                from axolotl.contribs.mit.muon import (
+                    MuonOptimizerFactory,
+                )
 
+                optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "dion":
                 from axolotl.contribs.mit.dion import (
@@ -443,8 +423,6 @@ class TrainerBuilderBase(abc.ABC):
             report_to.append("tensorboard")
         if self.cfg.use_comet:
             report_to.append("comet_ml")
-        if self.cfg.use_trackio:
-            report_to.append("trackio")
 
         training_args_kwargs["report_to"] = report_to
 
@@ -452,8 +430,6 @@ class TrainerBuilderBase(abc.ABC):
             training_args_kwargs["run_name"] = self.cfg.wandb_name
         elif self.cfg.use_mlflow:
             training_args_kwargs["run_name"] = self.cfg.mlflow_run_name
-        elif self.cfg.use_trackio:
-            training_args_kwargs["run_name"] = self.cfg.trackio_run_name
         else:
             training_args_kwargs["run_name"] = None
 

src/axolotl/core/builders/causal.py

@@ -72,9 +72,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.include_tkps:
             callbacks.append(
                 TokensPerSecondCallback(
-                    self.cfg.tensor_parallel_size,
-                    self.cfg.context_parallel_size,
-                    resume_from_checkpoint=self.cfg.resume_from_checkpoint,
+                    self.cfg.tensor_parallel_size, self.cfg.context_parallel_size
                 )
             )
         return callbacks

src/axolotl/core/trainers/base.py

@@ -2,8 +2,6 @@
 
 from __future__ import annotations
 
-import json
-import math
 import os
 from collections import defaultdict
 from functools import partial, wraps
@@ -51,8 +49,6 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger(__name__)
 
-TOKENS_STATE_FILE = "tokens_state."
-
 REDUCTION_FNS = {
     "mean": torch.mean,
     "min": torch.min,
@@ -352,33 +348,24 @@ class AxolotlTrainer(
         #     return (loss, outputs) if return_outputs else loss
 
         # track number of tokens for tokens per second calculation
-        if self.args.include_tkps and model.training:
+        if self.args.include_tkps:
             inputs_key = "labels" if "labels" in inputs else "input_ids"
-            trainable_tokens = (inputs[inputs_key] != -100).sum()
-            total_tokens = inputs[inputs_key].numel()
-            total_tokens = torch.tensor(total_tokens, device=inputs[inputs_key].device)
-
+            num_tokens = (inputs[inputs_key] != -100).sum()
             if is_distributed():
                 torch.distributed.all_reduce(
-                    trainable_tokens, op=torch.distributed.ReduceOp.SUM
+                    num_tokens, op=torch.distributed.ReduceOp.SUM
                 )
-                torch.distributed.all_reduce(
-                    total_tokens, op=torch.distributed.ReduceOp.SUM
+            if hasattr(self.state, "num_tokens"):
+                self.state.num_tokens = (
+                    self.state.num_tokens + (inputs[inputs_key] != -100).sum().cpu()
                 )
+            else:
+                self.state.num_tokens = (inputs[inputs_key] != -100).sum().cpu()
 
-            if not hasattr(self.state, "tokens"):
-                self.state.tokens = {
-                    "trainable": torch.zeros(1),
-                    "total": torch.zeros(1),
-                }
-
-            # trainable tokens for throughput and total token slots for summaries
-            self.state.tokens["trainable"] = (
-                self.state.tokens["trainable"] + trainable_tokens.detach().cpu()
-            )
-            self.state.tokens["total"] = self.state.tokens["total"] + total_tokens.cpu()
-            # Store per-step trainable tokens for throughput calculation
-            self.state.tokens["trainable_tokens"] = trainable_tokens.detach().cpu()
+            if hasattr(self.state, "total_tokens"):
+                self.state.total_tokens += num_tokens
+            else:
+                self.state.total_tokens = num_tokens
 
         if self.args.orpo_alpha:
             return self.orpo_compute_loss(
@@ -616,7 +603,6 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-        metric_ndigits = int(os.getenv("AXOLOTL_METRIC_NDIGITS", "5"))
 
         for key, metric_data in self._stored_metrics[train_eval].items():
             values = torch.tensor(metric_data["values"])  # type: ignore[arg-type]
@@ -627,18 +613,7 @@ class AxolotlTrainer(
                 raise NotImplementedError(
                     "Metric reduction must be one of [mean, min, max, sum]"
                 )
-            logs[key] = round(fn(values).item(), metric_ndigits)
-
-        if "loss" in logs:
-            try:
-                logs["ppl"] = round(math.exp(logs["loss"]), metric_ndigits)
-            except OverflowError:
-                logs["ppl"] = float("inf")
-        if "eval_loss" in logs:
-            try:
-                logs["eval_ppl"] = round(math.exp(logs["eval_loss"]), metric_ndigits)
-            except OverflowError:
-                logs["eval_ppl"] = float("inf")
+            logs[key] = round(fn(values).item(), 4)
 
         if is_main_process():
             # Add memory usage
@@ -650,14 +625,10 @@ class AxolotlTrainer(
             except (ValueError, TypeError, FileNotFoundError):
                 pass
 
-        if (
-            self.args.include_tkps
-            and train_eval == "train"
-            and hasattr(self.state, "tokens")
-        ):
+        if self.args.include_tkps and train_eval == "train":
             # each rank will log its own tokens per second
             # for logging_steps > 1 we obtain a moving average of this metric
-            logs["tokens/train_per_sec_per_gpu"] = round(
+            logs["tokens_per_second_per_gpu"] = round(
                 self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
             )
             if (
@@ -699,19 +670,6 @@ class AxolotlTrainer(
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
-
-        # Save total_tokens state if tracking is enabled
-        if self.args.include_tkps and hasattr(self.state, "tokens"):
-            tokens_state = {
-                "total": int(torch.as_tensor(self.state.tokens.get("total", 0)).item()),
-                "trainable": int(
-                    torch.as_tensor(self.state.tokens.get("trainable", 0)).item()
-                ),
-            }
-            tokens_state_path = os.path.join(output_dir, TOKENS_STATE_FILE)
-            with open(tokens_state_path, "w", encoding="utf-8") as f:
-                json.dump(tokens_state, f)
-
         return super()._save_checkpoint(model, trial, **kwargs)
 
     # TODO(wing): remove once https://github.com/huggingface/transformers/pull/39866/files is merged

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

@@ -36,6 +36,4 @@ class DPOStrategy:
             training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
         if cfg.dpo_use_logits_to_keep is not None:
             training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
-        if cfg.dpo_use_liger_kernel is not None:
-            training_args_kwargs["use_liger_kernel"] = cfg.dpo_use_liger_kernel
         return training_args_kwargs

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"
 ```
 
 ## Usage
@@ -54,8 +54,6 @@ plugins:
 - granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
-- internvl
-- kimi_linear
 - lfm2
 - lfm2_moe
 - lfm2_vl

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@f643b88"`'
 )
 
 
@@ -96,11 +96,7 @@ class CutCrossEntropyPlugin(BasePlugin):
             )
 
             # The patch checks model_type internally
-
-            cce_patch(
-                cfg.model_config_type,
-                remote_model_id=cfg.base_model if cfg.trust_remote_code else None,
-            )
+            cce_patch(cfg.model_config_type)
 
     def patch_llama_like(
         self,
@@ -111,9 +107,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         """
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
-        def patch_generic(
-            maybe_model, patch_options, model_type: str, remote_model_id: str | None
-        ):
+        def patch_generic(maybe_model, patch_options, model_type: str):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
 

src/axolotl/integrations/densemixer/plugin.py

@@ -21,7 +21,7 @@ class DenseMixerPlugin(BasePlugin):
         if cfg.dense_mixer:
             if not importlib.util.find_spec("densemixer"):
                 raise RuntimeError(
-                    "DenseMixer is not installed. Install it with `pip install densemixer`"
+                    "DenseMixer is not installed. Install it with `pip install densemizer`"
                 )
 
             from densemixer.patching import (

src/axolotl/loaders/patch_manager.py

@@ -26,48 +26,6 @@ PLUGIN_MANAGER = PluginManager.get_instance()
 class PatchManager:
     """Manages the application of patches during the model loading process."""
 
-    @staticmethod
-    def apply_pre_config_load_patches(cfg: DictDefault):
-        """
-        Apply patches that must be set up before config loading.
-        This is for patches that intercept remote code loading from HuggingFace,
-        which needs to be in place before AutoConfig.from_pretrained() is called.
-
-        Args:
-            cfg: Configuration dictionary with model and training settings.
-        """
-        if (
-            hasattr(cfg, "base_model_config")
-            and cfg.base_model_config
-            and "kimi-linear" in cfg.base_model_config.lower()
-        ):
-            from axolotl.monkeypatch.models.kimi_linear.patch_kimi_linear import (
-                patch_kimi_config,
-            )
-
-            patch_kimi_config()
-
-    @staticmethod
-    def apply_pre_tokenizer_load_patches(cfg: DictDefault):
-        """
-        Apply patches that must be set up before tokenizer loading.
-        This is for patches that intercept remote code loading from HuggingFace,
-        which needs to be in place before AutoTokenizer.from_pretrained() is called.
-
-        Args:
-            cfg: Configuration dictionary with model and training settings.
-        """
-        if (
-            hasattr(cfg, "tokenizer_config")
-            and cfg.tokenizer_config
-            and "kimi-linear" in cfg.tokenizer_config.lower()
-        ):
-            from axolotl.monkeypatch.models.kimi_linear.patch_kimi_linear import (
-                patch_kimi_tokenizer,
-            )
-
-            patch_kimi_tokenizer()
-
     def __init__(
         self,
         cfg: DictDefault,
@@ -199,6 +157,12 @@ class PatchManager:
 
             flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
             patch_flex_wrapper(**flex_attn_compile_kwargs)
+            if self.cfg.sample_packing:
+                from axolotl.core.attention.flex_block_mask import (
+                    patch_create_causal_mask,
+                )
+
+                patch_create_causal_mask(self.cfg.model_config_type)
 
     def _apply_model_specific_patches(self):
         """Apply patches specific to model architectures."""
@@ -226,13 +190,6 @@ class PatchManager:
 
             apply_mistral_tokenizer_image_patch()
 
-        if self.cfg.model_config_type == "kimi_linear":
-            from axolotl.monkeypatch.models.kimi_linear.patch_kimi_linear import (
-                patch_kimi_model,
-            )
-
-            patch_kimi_model()
-
     def _apply_fp8_patches(self):
         """Apply patches for FP8 support."""
         if self.cfg.fp8:

src/axolotl/loaders/tokenizer.py

@@ -124,11 +124,6 @@ def modify_tokenizer_files(
 def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
     """Load and configure the tokenizer based on the provided config."""
 
-    # Apply patches that need to be in place before tokenizer loading
-    from axolotl.loaders.patch_manager import PatchManager
-
-    PatchManager.apply_pre_tokenizer_load_patches(cfg)
-
     def _load_mistral_common_tokenizer(cfg: DictDefault):
         """Load mistral-common tokenizer"""
         from axolotl.utils.mistral import HFMistralTokenizer

src/axolotl/loaders/utils.py

@@ -79,11 +79,7 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
             and hasattr(model_config, "vision_config")
             and hasattr(model_config.vision_config, "image_size")
         ):
-            image_size = model_config.vision_config.image_size
-            if isinstance(image_size, list):
-                cfg.image_size = tuple(image_size)
-            else:
-                cfg.image_size = image_size
+            cfg.image_size = model_config.vision_config.image_size
             LOG.debug(f"Loaded image size: {cfg.image_size} from model config")
 
     quant_config_exists = (

src/axolotl/monkeypatch/accelerate/parallelism_config.py

@@ -75,33 +75,3 @@ def patch_parallelism_config():
 
     ParallelismConfig._validate_accelerator = _validate_accelerator
     AcceleratorState.is_fsdp2 = property(patched_is_fsdp2)
-
-
-def patch_prepare_cp():
-    import functools
-
-    import torch
-    from accelerate import Accelerator
-
-    def patched_prepare_cp(self, *args):
-        if self.parallelism_config.cp_backend == "deepspeed":
-            return args
-
-        from accelerate.big_modeling import _attach_context_parallel_hooks
-        from torch.distributed.tensor.experimental import context_parallel
-        from torch.distributed.tensor.experimental._attention import set_rotate_method
-
-        cp_comm_strategy = self.parallelism_config.cp_handler.cp_comm_strategy
-        set_rotate_method(cp_comm_strategy)
-
-        self._cp_context = functools.partial(
-            context_parallel, mesh=self.torch_device_mesh["cp"]
-        )
-
-        for arg in args:
-            if isinstance(arg, torch.nn.Module):
-                _attach_context_parallel_hooks(arg)
-
-        return args
-
-    Accelerator._prepare_cp = patched_prepare_cp

src/axolotl/monkeypatch/models/kimi_linear/configuration_kimi.py

@@ -1,148 +0,0 @@
-"""
-Kimi-Linear configuration.
-
-Source: https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct/blob/main/configuration_kimi.py
-Revision: 6e163f3
-"""
-
-from typing import Optional
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class KimiLinearConfig(PretrainedConfig):
-    model_type = "kimi_linear"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        model_type="kimi_linear",
-        vocab_size=163840,
-        hidden_size=4096,
-        head_dim=None,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        hidden_act="silu",
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        tie_word_embeddings=False,
-        moe_intermediate_size: Optional[int] = None,
-        moe_renormalize: bool = True,
-        moe_router_activation_func: str = "sigmoid",
-        num_experts: Optional[int] = None,
-        num_experts_per_token: Optional[int] = None,
-        num_shared_experts: int = 0,
-        routed_scaling_factor: float = 1.0,
-        first_k_dense_replace: int = 0,
-        moe_layer_freq: int = 1,
-        use_grouped_topk: bool = True,
-        num_expert_group: int = 1,
-        topk_group: int = 1,
-        q_lora_rank: Optional[int] = None,
-        kv_lora_rank: Optional[int] = None,
-        qk_nope_head_dim: Optional[int] = None,
-        qk_rope_head_dim: Optional[int] = None,
-        v_head_dim: Optional[int] = None,
-        mla_use_nope: Optional[bool] = False,
-        num_nextn_predict_layers: int = 0,
-        linear_attn_config: Optional[dict] = None,
-        router_aux_loss_coef: float = 0.01,
-        **kwargs,
-    ):
-        self.model_type = model_type
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.head_dim = (
-            head_dim if head_dim is not None else hidden_size // num_attention_heads
-        )
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-
-        self.q_lora_rank = q_lora_rank
-        self.kv_lora_rank = kv_lora_rank
-        self.qk_nope_head_dim = qk_nope_head_dim
-        self.qk_rope_head_dim = qk_rope_head_dim
-        self.v_head_dim = v_head_dim
-        self.mla_use_nope = mla_use_nope
-        # moe config
-        self.num_experts = num_experts
-        self.num_experts_per_token = num_experts_per_token
-        self.moe_renormalize = moe_renormalize
-        self.num_shared_experts = num_shared_experts
-        self.routed_scaling_factor = routed_scaling_factor
-        self.moe_router_activation_func = moe_router_activation_func
-        assert self.moe_router_activation_func in ("softmax", "sigmoid")
-        self.moe_intermediate_size = moe_intermediate_size
-        self.first_k_dense_replace = first_k_dense_replace
-        self.moe_layer_freq = moe_layer_freq
-        self.use_grouped_topk = use_grouped_topk
-        self.num_expert_group = num_expert_group
-        self.topk_group = topk_group
-        self.num_nextn_predict_layers = num_nextn_predict_layers
-        self.router_aux_loss_coef = router_aux_loss_coef
-
-        if linear_attn_config is not None:
-            assert linear_attn_config["kda_layers"] is not None
-            assert linear_attn_config["full_attn_layers"] is not None
-        self.linear_attn_config = linear_attn_config
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-
-    @property
-    def is_mla(self):
-        return (
-            self.q_lora_rank is not None
-            or self.kv_lora_rank is not None
-            or self.qk_nope_head_dim is not None
-            or self.qk_rope_head_dim is not None
-            or self.v_head_dim is not None
-            or self.mla_use_nope is True
-        )
-
-    @property
-    def is_moe(self):
-        return self.num_experts is not None
-
-    @property
-    def is_linear_attn(self) -> bool:
-        return not (
-            self.linear_attn_config is None
-            or (
-                isinstance(self.linear_attn_config, dict)
-                and self.linear_attn_config["kda_layers"] is not None
-                and len(self.linear_attn_config["kda_layers"]) == 0
-            )
-        )
-
-    def is_kda_layer(self, layer_idx: int):
-        return (
-            self.linear_attn_config is not None
-            and (layer_idx + 1) in self.linear_attn_config["kda_layers"]
-        )

src/axolotl/monkeypatch/models/kimi_linear/patch_kimi_linear.py

@@ -1,85 +0,0 @@
-import importlib.resources
-import importlib.util
-import sys
-from pathlib import Path
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-KIMI_PATCH_PACKAGE = "axolotl.monkeypatch.models.kimi_linear"
-
-
-def get_patch_file_path(package_dot_path: str, filename: str) -> Path:
-    """
-    Gets the absolute path to a patch file using importlib.resources.files.
-    """
-    try:
-        return importlib.resources.files(package_dot_path) / filename
-    except ModuleNotFoundError:
-        return None
-
-
-def _load_local_module(module_name: str, filename: str):
-    """Helper to load a local module if not already loaded."""
-    if module_name in sys.modules:
-        return sys.modules[module_name]
-
-    patch_path = get_patch_file_path(KIMI_PATCH_PACKAGE, filename)
-    if patch_path and patch_path.exists():
-        spec = importlib.util.spec_from_file_location(module_name, patch_path)
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[module_name] = module
-        spec.loader.exec_module(module)
-        return module
-    return None
-
-
-def _patch_get_class_in_module():
-    """
-    Core patch function that hijacks Transformers' dynamic module loading.
-    """
-    from transformers.dynamic_module_utils import get_class_in_module
-
-    if hasattr(get_class_in_module, "_axolotl_patched"):
-        return
-
-    original_get_class_in_module = get_class_in_module
-
-    # Mapping of module path patterns to (module_name, filename)
-    KIMI_MODULE_MAP = {
-        "configuration_kimi": ("configuration_kimi", "configuration_kimi.py"),
-        "modeling_kimi": ("modeling_kimi", "modeling_kimi.py"),
-        "tokenization_kimi": ("tokenization_kimi", "tokenization_kimi.py"),
-    }
-
-    def patched_get_class_in_module(class_name, module_path, **kwargs):
-        """Patched version that returns our local modules instead of remote ones."""
-        for pattern, (module_name, filename) in KIMI_MODULE_MAP.items():
-            if pattern in module_path:
-                module = _load_local_module(module_name, filename)
-                if module:
-                    return getattr(module, class_name)
-                break  # Pattern matched but file not found, fall through
-
-        return original_get_class_in_module(class_name, module_path, **kwargs)
-
-    import transformers.dynamic_module_utils
-
-    transformers.dynamic_module_utils.get_class_in_module = patched_get_class_in_module
-    patched_get_class_in_module._axolotl_patched = True
-
-
-def patch_kimi():
-    """
-    Apply all Kimi patches.
-    Must be called BEFORE loading config/tokenizer/model.
-    """
-    _patch_get_class_in_module()
-    LOG.info("Kimi patches applied successfully!")
-
-
-# Keep these for backward compatibility if needed
-patch_kimi_config = patch_kimi
-patch_kimi_tokenizer = patch_kimi
-patch_kimi_model = patch_kimi

src/axolotl/monkeypatch/models/kimi_linear/tokenization_kimi.py

@@ -1,357 +0,0 @@
-"""
-Adapted Kimi-Linear tokenizer to use proper template defaults and misc fixes.
-
-Source: https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct/blob/main/tokenization_kimi.py
-Revision: 919416f
-"""
-
-import os
-from logging import getLogger
-from pathlib import Path
-from shutil import copyfile
-from typing import (
-    Any,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Tuple,
-    Union,
-    cast,
-)
-
-import tiktoken
-from tiktoken.load import load_tiktoken_bpe
-from tokenizers import AddedToken
-from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-logger = getLogger(__name__)
-VOCAB_FILES_NAMES = {"vocab_file": "tiktoken.model"}
-
-
-class TikTokenTokenizer(PreTrainedTokenizer):
-    """
-    Tokenizing and encoding/decoding text using the Tiktoken tokenizer. See megatron/tokenizer/tiktoken_tokenizer.py.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            The path to the Tiktoken model file.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|begin_of_text|>",`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|end_of_text|>"`):
-            The end of sequence token.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_249|>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead. The second to last item in special_tokens.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_250|>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        additional_special_tokens (list of `str`, *optional*):
-            A tuple or a list of additional tokens, which will be marked as `special`, meaning that they will be
-            skipped when decoding if `skip_special_tokens` is set to `True`.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-
-    model_input_names = ["input_ids", "attention_mask"]
-
-    special_tokens: Dict[str, int]
-
-    num_reserved_special_tokens = 256
-
-    pat_str = "|".join(
-        [
-            r"""[\p{Han}]+""",
-            r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
-            r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
-            r"""\p{N}{1,3}""",
-            r""" ?[^\s\p{L}\p{N}]+[\r\n]*""",
-            r"""\s*[\r\n]+""",
-            r"""\s+(?!\S)""",
-            r"""\s+""",
-        ]
-    )
-
-    def __init__(
-        self,
-        vocab_file,
-        bos_token: Union[str, AddedToken] = "[BOS]",  # nosec: B107
-        eos_token: Union[str, AddedToken] = "[EOS]",  # nosec: B107
-        unk_token: Union[str, AddedToken, None] = None,
-        pad_token: Union[str, AddedToken, None] = None,
-        additional_special_tokens: List[str] = None,
-        added_tokens_decoder: Optional[dict] = None,
-        **kwargs,
-    ):
-        assert os.path.isfile(vocab_file), vocab_file
-
-        if additional_special_tokens is None:
-            additional_special_tokens = [
-                "<|im_end|>",
-                "<|im_user|>",
-                "<|im_assistant|>",
-                "<|start_header_id|>",
-                "<|end_header_id|>",
-                "[EOT]",
-                "<|im_system|>",
-                "<|im_middle|>",
-            ]
-
-        special_tokens_mapping = {
-            i: added_tokens_decoder[i].content for i in added_tokens_decoder
-        }
-
-        self.vocab_file = vocab_file
-        mergeable_ranks = load_tiktoken_bpe(vocab_file)
-        num_base_tokens = len(mergeable_ranks)
-        self.special_tokens = {
-            special_tokens_mapping.get(i, f"<|reserved_token_{i}|>"): i
-            for i in range(
-                num_base_tokens, num_base_tokens + self.num_reserved_special_tokens + 2
-            )
-        }
-
-        self.model = tiktoken.Encoding(
-            name=Path(vocab_file).name,
-            pat_str=self.pat_str,
-            mergeable_ranks=mergeable_ranks,
-            special_tokens=self.special_tokens,
-        )
-        logger.info(f"Reloaded tiktoken model from {vocab_file}")
-
-        self.n_words: int = self.model.n_vocab
-        # BOS / EOS token IDs
-        self.bos_id: int = self.special_tokens[str(bos_token)]
-        self.eos_id: int = self.special_tokens[str(eos_token)]
-        logger.info(
-            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
-        )
-
-        self.pad_id: int = self.special_tokens[str(pad_token)]
-        self.unk_id: int = self.special_tokens[str(unk_token)]
-
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-
-        self.decoder = {}
-        for i in range(self.n_words):
-            # Taken from https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
-            decoding = "".join(
-                [
-                    self.byte_encoder[ord(char)]
-                    for char in self.model.decode_single_token_bytes(i).decode(
-                        "latin-1"
-                    )
-                ]
-            )
-            self.decoder[i] = decoding
-
-        self.encoder = {}
-        for i in range(self.n_words):
-            if i in self.decoder:
-                self.encoder[self.decoder[i]] = i
-
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            additional_special_tokens=additional_special_tokens,
-            **kwargs,
-        )
-        self.all_special_ids_set = set(self.all_special_ids)
-
-    def encode(
-        self, text: str, allow_special_tokens: bool = True, **kwargs
-    ) -> List[int]:
-        """
-        Encodes a string into a list of token IDs.
-
-        Args:
-            text (str): The input string to be encoded.
-
-        Returns:
-            list[int]: A list of token IDs.
-        """
-        # If there are other args, we should call super().encode because there are a lot of code
-        # to handle those args. supper().encode finally will call _tokenize and _convert_token_to_id.
-        # NOTE: our encode method is not compatible with the super().encode method,
-        #   e.g. split_special_tokens' default is True in our encode method.
-        if len(kwargs) > 0:
-            # logger.warning(f"Calling super().encode with {kwargs}")
-            return super().encode(text, **kwargs)
-
-        assert type(text) is str
-
-        # The tiktoken tokenizer can handle <=400k chars without
-        # pyo3_runtime.PanicException.
-        TIKTOKEN_MAX_ENCODE_CHARS = 400_000
-
-        # https://github.com/openai/tiktoken/issues/195
-        # Here we iterate over subsequences and split if we exceed the limit
-        # of max consecutive non-whitespace or whitespace characters.
-        MAX_NO_WHITESPACES_CHARS = 25_000
-
-        texts = self.pre_tokenizer_process(text)
-
-        all_substrs = []
-        for text in texts:
-            substrs = (
-                substr
-                for i in range(0, len(text), TIKTOKEN_MAX_ENCODE_CHARS)
-                for substr in self._split_whitespaces_or_nonwhitespaces(
-                    text[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS
-                )
-            )
-            all_substrs.extend(substrs)
-
-        t: List[int] = []
-        for substr in all_substrs:
-            if allow_special_tokens:
-                t.extend(
-                    # we should consider special token as a common token
-                    self.model.encode(
-                        substr,
-                        allowed_special="all",
-                    )
-                )
-            else:
-                t.extend(
-                    # we should consider special token as a common token
-                    self.model.encode(
-                        substr,
-                        disallowed_special=(),
-                    )
-                )
-
-        return t
-
-    def decode(self, token_ids: Union[int, List[int]], **kwargs) -> str:
-        """
-        Decodes a list of token IDs into a string.
-
-        Args:
-            token_ids (List[int]): The list of token IDs to be decoded.
-
-        Returns:
-            str: The decoded string.
-        """
-        # If there are other args, we should call super().decode because there are a lot of code
-        # to handle those args. supper().encode finally will call convert_tokens_to_string and _convert_id_to_token.
-        if len(kwargs) > 0:
-            return super().decode(token_ids, **kwargs)
-
-        if type(token_ids) is int:
-            token_ids = [token_ids]
-
-        return self.model.decode(cast(List[int], token_ids))
-
-    @staticmethod
-    def _split_whitespaces_or_nonwhitespaces(
-        s: str, max_consecutive_slice_len: int
-    ) -> Iterator[str]:
-        """
-        Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len`
-        consecutive whitespaces or consecutive non-whitespaces.
-        """
-        current_slice_len = 0
-        current_slice_is_space = s[0].isspace() if len(s) > 0 else False
-        slice_start = 0
-
-        for i in range(len(s)):
-            is_now_space = s[i].isspace()
-
-            if current_slice_is_space ^ is_now_space:
-                current_slice_len = 1
-                current_slice_is_space = is_now_space
-            else:
-                current_slice_len += 1
-                if current_slice_len > max_consecutive_slice_len:
-                    yield s[slice_start:i]
-                    slice_start = i
-                    current_slice_len = 1
-        yield s[slice_start:]
-
-    def pre_tokenizer_process(self, text: str) -> List[str]:
-        """
-        pre-tokenizes the input text into a list of tokens.
-        This method is used to split the input text into smaller chunks for internal processing.
-        """
-        return [text]
-
-    """ ----- Below are the abstract methods required by PreTrainedTokenizer ----- """
-
-    @property
-    def vocab_size(self) -> int:
-        return self.n_words
-
-    def get_vocab(self) -> Dict[str, int]:
-        return self.encoder
-
-    def _tokenize(self, text: str, **kwargs) -> List[str]:
-        return [self.decoder[t] for t in self.encode(text)]
-
-    def _convert_token_to_id(self, token: str) -> int:
-        return self.encoder.get(token, self.unk_id)
-
-    def _convert_id_to_token(self, index: int) -> str:
-        return self.decoder.get(index)
-
-    @staticmethod
-    def clean_up_tokenization(out_string: str) -> str:
-        return out_string
-
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode(
-            "utf-8", "replace"
-        )
-        return text
-
-    def save_vocabulary(
-        self, save_directory: str, filename_prefix: Optional[str] = None
-    ) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            raise ValueError(
-                f"vocabulary path ({save_directory}) should be a directory"
-            )
-        out_vocab_file = os.path.join(
-            save_directory,
-            (filename_prefix + "-" if filename_prefix else "")
-            + VOCAB_FILES_NAMES["vocab_file"],
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(
-            out_vocab_file
-        ) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    def apply_chat_template(
-        self,
-        conversation,
-        tools: Optional[list[dict]] = None,
-        tokenize: bool = True,
-        add_generation_prompt: bool = False,
-        **kwargs,
-    ):
-        tools = deep_sort_dict(tools)
-        return super().apply_chat_template(
-            conversation,
-            tools=tools,
-            tokenize=tokenize,
-            add_generation_prompt=add_generation_prompt,
-            **kwargs,
-        )
-
-
-def deep_sort_dict(obj: Any) -> Any:
-    if isinstance(obj, dict):
-        return {k: deep_sort_dict(v) for k, v in sorted(obj.items())}
-    if isinstance(obj, list):
-        return [deep_sort_dict(item) for item in obj]
-    return obj

src/axolotl/processing_strategies.py

@@ -8,7 +8,6 @@ from PIL.Image import Resampling
 from torch import Tensor, zeros_like
 from transformers import ProcessorMixin
 from transformers.image_utils import load_image
-from transformers.models.internvl import InternVLProcessor
 from transformers.models.smolvlm import SmolVLMProcessor
 from transformers.models.voxtral import VoxtralProcessor
 
@@ -455,37 +454,6 @@ class Mistral3ProcessingStrategy(ProcessingStrategy):
         return labels
 
 
-class InternVLProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for InternVL"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
-
-        if not hasattr(processor, "image_ids"):
-            raise ValueError("'image_ids' missing from InternVL Processor.")
-
-        self.image_token_ids = processor.image_ids
-
-    def process_labels(self, input_ids):
-        labels = input_ids.clone()
-
-        labels[labels == self.processor.tokenizer.pad_token_id] = -100
-
-        for ids in self.image_token_ids:
-            labels[labels == ids] = -100
-
-        # Note: Check if need to mask 'video_token' as it gets converted to
-        # image patches during media processing
-
-        return labels
-
-
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -533,11 +501,6 @@ def get_processing_strategy(
             **processing_kwargs,
         )
 
-    if isinstance(processor, InternVLProcessor):
-        return InternVLProcessingStrategy(
-            **processing_kwargs,
-        )
-
     # llama3_2_vision, llama4, llava
     # mistral_v7_tekken, pixtral, lfm2vl
     return ProcessingStrategy(

src/axolotl/utils/__init__.py

@@ -24,10 +24,6 @@ def is_opentelemetry_available():
     )
 
 
-def is_trackio_available():
-    return importlib.util.find_spec("trackio") is not None
-
-
 def get_pytorch_version() -> tuple[int, int, int]:
     """
     Get Pytorch version as a tuple of (major, minor, patch).

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -1,7 +1,5 @@
 """A callback for calculating tokens per second during training."""
 
-import json
-import os
 import time
 
 import torch
@@ -12,52 +10,22 @@ from transformers import (
     TrainingArguments,
 )
 
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-TOKENS_STATE_FILE = "tokens_state.json"
-
 
 class TokensPerSecondCallback(TrainerCallback):
     """
     A callback to measure and log tokens per second during training.
-    Also handles saving/restoring total_tokens state across checkpoint resumes.
     """
 
-    def __init__(
-        self, tensor_parallel_size, context_parallel_size, resume_from_checkpoint=None
-    ):
+    def __init__(self, tensor_parallel_size, context_parallel_size):
         super().__init__()
         self.step_time = 0.0
         self.start_time = 0.0
         self.non_data_parallel_size = 1
-        self.resume_from_checkpoint = resume_from_checkpoint
         if tensor_parallel_size is not None:
             self.non_data_parallel_size *= tensor_parallel_size
         if context_parallel_size is not None:
             self.non_data_parallel_size *= context_parallel_size
 
-    def on_train_begin(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):  # pylint: disable=unused-argument
-        """Restore total_tokens state when resuming from checkpoint."""
-        if not isinstance(self.resume_from_checkpoint, str):
-            return
-        tokens_state_path = os.path.join(self.resume_from_checkpoint, TOKENS_STATE_FILE)
-        if os.path.isfile(tokens_state_path):
-            with open(tokens_state_path, "r", encoding="utf-8") as f:
-                tokens_state = json.load(f)
-            state.tokens = {
-                "total": torch.tensor(tokens_state.get("total", 0)),
-                "trainable": torch.tensor(tokens_state.get("trainable", 0)),
-            }
-            LOG.info(f"Restored total_tokens: {state.tokens['total']}")
-
     def on_step_begin(
         self,
         args: TrainingArguments,
@@ -65,8 +33,6 @@ class TokensPerSecondCallback(TrainerCallback):
         control: TrainerControl,
         **kwargs,
     ):  # pylint: disable=unused-argument
-        if not hasattr(state, "tokens"):
-            state.tokens = {"trainable": torch.zeros(1), "total": torch.zeros(1)}
         self.start_time = time.perf_counter()
         state.last_tokens_per_second = torch.zeros(1)
 
@@ -77,10 +43,9 @@ class TokensPerSecondCallback(TrainerCallback):
         control: TrainerControl,
         **kwargs,
     ):  # pylint: disable=unused-argument
-        tokens = getattr(state, "tokens", None)
-        if tokens and "trainable_tokens" in tokens:
+        if hasattr(state, "num_tokens"):
             step_time = time.perf_counter() - self.start_time
-            num_tokens_per_device = tokens["trainable_tokens"].clone()
+            num_tokens_per_device = state.num_tokens.clone()
             # non data parallel groups have duplicated tokens, so we avoid double-counting
             num_tokens_per_device = num_tokens_per_device / self.non_data_parallel_size
             state.last_tokens_per_second = num_tokens_per_device / step_time
@@ -95,15 +60,5 @@ class TokensPerSecondCallback(TrainerCallback):
     ):  # pylint: disable=unused-argument
         # after logging, clear the running metrics
         if hasattr(state, "last_tokens_per_second"):
-            logs["tokens/train_per_sec_per_gpu"] = state.last_tokens_per_second.item()
             state.last_tokens_per_second.zero_()
-        tokens = getattr(state, "tokens", None)
-        # Clear per-step tokens after logging
-        if tokens and "trainable_tokens" in tokens:
-            tokens["trainable_tokens"] = torch.zeros_like(tokens["trainable_tokens"])
-
-        if tokens and "total" in tokens:
-            logs["tokens/total"] = tokens["total"].item()
-
-        if tokens and "trainable" in tokens:
-            logs["tokens/trainable"] = tokens["trainable"].item()
+            state.num_tokens = torch.zeros(1)

src/axolotl/utils/callbacks/trackio_.py

@@ -1,44 +0,0 @@
-"""Trackio module for trainer callbacks"""
-
-from typing import TYPE_CHECKING
-
-import trackio
-from transformers import TrainerCallback, TrainerControl, TrainerState
-
-from axolotl.utils.distributed import is_main_process
-from axolotl.utils.environment import is_package_version_ge
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from axolotl.core.training_args import AxolotlTrainingArguments
-
-LOG = get_logger(__name__)
-
-
-class SaveAxolotlConfigtoTrackioCallback(TrainerCallback):
-    """Callback for trackio integration"""
-
-    def __init__(self, axolotl_config_path):
-        self.axolotl_config_path = axolotl_config_path
-
-    def on_train_begin(
-        self,
-        args: "AxolotlTrainingArguments",
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        if is_main_process():
-            try:
-                if not is_package_version_ge("trackio", "0.11.0"):
-                    LOG.warning(
-                        "Trackio version 0.11.0 or higher is required to save config files. "
-                        "Please upgrade trackio: pip install --upgrade trackio"
-                    )
-                    return control
-
-                trackio.save(self.axolotl_config_path)
-                LOG.info("The Axolotl config has been saved to Trackio.")
-            except (FileNotFoundError, ConnectionError, AttributeError) as err:
-                LOG.warning(f"Error while saving Axolotl config to Trackio: {err}")
-        return control

src/axolotl/utils/config/__init__.py

@@ -151,11 +151,6 @@ def normalize_config(cfg):
     if not cfg.base_model_config:
         cfg.base_model_config = cfg.base_model
 
-    # Apply pre-config load patches (e.g., for Kimi Linear remote code patching)
-    from axolotl.loaders.patch_manager import PatchManager
-
-    PatchManager.apply_pre_config_load_patches(cfg)
-
     model_config = load_model_config(cfg)
 
     cfg.tokenizer_config = (

src/axolotl/utils/data/utils.py

@@ -180,18 +180,20 @@ def truncate_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 def handle_long_seq_in_dataset(
     dataset: Dataset, sequence_len: int, cfg: DictDefault
 ) -> Dataset:
-    """Remove sequences longer than configured maximum from dataset.
-
-    Args:
-        dataset: Dataset to filter.
-        sequence_len: Maximum length for sequences to keep
-        cfg: Dictionary mapping `axolotl` config keys to values.
-
+    """
+    Remove or truncate sequences that exceed the configured maximum length from a dataset.
+    
+    Parameters:
+        dataset (Dataset): Dataset to process; if it lacks an "input_ids" column or is streaming, it is returned unchanged.
+        sequence_len (int): Maximum allowed sequence length; sequences longer than this are either removed or truncated.
+        cfg (DictDefault): Configuration object with keys:
+            - excess_length_strategy: "drop", "truncate", or "raise" — determines how to handle overlong sequences.
+            - min_sample_len: minimum allowed sequence length (used when truncating or dropping).
+            - dataset_num_proc: number of processes to use for non-streaming datasets.
+            - is_preprocess: when true, bypasses cached preprocessing during filtering.
+    
     Returns:
-        Filtered dataset with long sequences handled according to the excess_length_strategy value:
-            'drop' (default)    excludes any sequence longer than sequence_len
-            'truncate'          truncates them down to sequence_len
-            'raise'             raises a ValueError if any sequence was found that was longer than sequence_len
+        Dataset: The input dataset with sequences longer than `sequence_len` removed or truncated according to `cfg`.
     """
     if (
         hasattr(dataset, "column_names")
@@ -234,12 +236,7 @@ def handle_long_seq_in_dataset(
 
     drop_long_kwargs = {}
     if filter_map_kwargs:
-        action = (
-            "Checking Sequence Lengths"
-            if excess_length_strategy == "raise"
-            else "Dropping Long Sequences"
-        )
-        drop_long_kwargs["desc"] = f"{action} (>{sequence_len})"
+        drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
     if excess_length_strategy == "truncate":
         process_fn = functools.partial(
@@ -269,4 +266,4 @@ def handle_long_seq_in_dataset(
             )
             LOG.warning(f"{action.title()} {dropped} samples from dataset")
 
-    return dataset
+    return dataset

src/axolotl/utils/logging.py

@@ -2,17 +2,9 @@
 
 import functools
 import logging
-import warnings
 
 from axolotl.utils.distributed import is_main_process
 
-# Suppress noisy bitsandbytes warnings about dtype casting during quantization
-warnings.filterwarnings(
-    "ignore",
-    message=".*MatMul8bitLt: inputs will be cast from.*",
-    category=UserWarning,
-)
-
 # Adapted from Accelerate
 # https://github.com/huggingface/accelerate/blob/main/src/accelerate/logging.py
 

src/axolotl/utils/quantization.py

@@ -9,10 +9,6 @@ from torchao.quantization import quantize_
 from torchao.quantization.qat import (
     QATConfig,
 )
-from torchao.quantization.qat import fake_quantizer
-from torchao.quantization.qat.fake_quantizer import (
-    Int4WeightFakeQuantizer as AoInt4WeightFakeQuantizer,
-)
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     Float8DynamicActivationInt4WeightConfig,
@@ -21,27 +17,6 @@ from torchao.quantization.quant_api import (
 
 from axolotl.utils.schemas.enums import TorchAOQuantDType
 
-
-class Int4WeightFakeQuantizer(AoInt4WeightFakeQuantizer):
-    """
-    Adds 'enabled' attribute to Int4WeightFakeQuantizer (removed in torchao 0.15).
-    Allows toggling fake quantization on/off for fake_quant_after_n_steps.
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.enabled = True
-
-    def forward(self, w: torch.Tensor) -> torch.Tensor:
-        if not self.enabled:
-            return w
-        return super().forward(w)
-
-
-# Replace the original Int4WeightFakeQuantizer in the fake_quantizer module
-# so that torchao's quantize_() function will use our version
-fake_quantizer.Int4WeightFakeQuantizer = Int4WeightFakeQuantizer
-
 quantization_config_to_str = {
     Int8DynamicActivationInt4WeightConfig: "int8int4",
     Float8DynamicActivationFloat8WeightConfig: "fp8fp8",

src/axolotl/utils/schemas/config.py

@@ -2,7 +2,6 @@
 
 from typing import Annotated, Any, Literal
 
-from accelerate.utils import is_fp8_available
 from annotated_types import MinLen
 from packaging import version
 from pydantic import (
@@ -34,7 +33,6 @@ from axolotl.utils.schemas.integrations import (
     MLFlowConfig,
     OpenTelemetryConfig,
     RayConfig,
-    TrackioConfig,
     WandbConfig,
 )
 from axolotl.utils.schemas.internal import EnvCapabilities, GPUCapabilities
@@ -64,7 +62,6 @@ class AxolotlInputConfig(
     WandbConfig,
     MLFlowConfig,
     CometConfig,
-    TrackioConfig,
     OpenTelemetryConfig,
     LISAConfig,
     GradioConfig,
@@ -176,12 +173,6 @@ class AxolotlInputConfig(
     dpo_use_logits_to_keep: bool | None = None
     dpo_label_smoothing: float | None = None
     dpo_norm_loss: bool | None = None
-
-    dpo_use_liger_kernel: bool | None = Field(
-        default=None,
-        json_schema_extra={"description": "Whether to use Liger kernel for DPO loss."},
-    )
-
     dpo_padding_free: bool | None = None
     dpo_generate_during_eval: bool | None = None
 
@@ -454,10 +445,10 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
-    excess_length_strategy: Literal["drop", "truncate", "raise"] | None = Field(
+    excess_length_strategy: Literal["drop", "truncate"] | None = Field(
         default=None,
         json_schema_extra={
-            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len; 'raise' raises a ValueError. Defaults to 'drop' for backward compatibility."
+            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len. Defaults to 'drop' for backward compatibility."
         },
     )
     eval_sequence_len: int | None = Field(
@@ -1101,16 +1092,6 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                 )
         return self
 
-    @model_validator(mode="after")
-    def check_fp8(self):
-        if self.fp8 and not self.capabilities.fp8:
-            raise ValueError("fp8 requested, but fp8 is not supported on this GPU")
-        elif self.fp8 and self.capabilities.fp8 and not is_fp8_available():
-            raise ValueError(
-                "fp8 requested, but missing one of ms-amp, transformers-engine or torchao."
-            )
-        return self
-
     @model_validator(mode="before")
     @classmethod
     def check_sample_packing_w_sdpa_bf16(cls, data):

src/axolotl/utils/schemas/integrations.py

@@ -200,23 +200,3 @@ class OpenTelemetryConfig(BaseModel):
             "description": "Port for the Prometheus metrics HTTP server"
         },
     )
-
-
-class TrackioConfig(BaseModel):
-    """Trackio configuration subset"""
-
-    use_trackio: bool | None = None
-    trackio_project_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Your trackio project name"},
-    )
-    trackio_run_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Set the name of your trackio run"},
-    )
-    trackio_space_id: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Hugging Face Space ID to sync dashboard to (optional, runs locally if not provided)"
-        },
-    )

src/axolotl/utils/schemas/validation.py

@@ -751,19 +751,12 @@ class OptimizationValidationMixin:
     @model_validator(mode="before")
     @classmethod
     def check_muon_deepspeed_fsdp(cls, data):
-        if data.get("optimizer") == "muon":
-            if data.get("deepspeed"):
-                raise ValueError(
-                    "Muon optimizer is currently incompatible with DeepSpeed"
-                )
-            if data.get("fsdp") or data.get("fsdp_config"):
-                fsdp_version = data.get("fsdp_version")
-                if fsdp_version is None:
-                    fsdp_version = data.get("fsdp_config", {}).get("fsdp_version", 1)
-                if str(fsdp_version) != "2":
-                    raise ValueError(
-                        "Muon optimizer is only compatible with FSDP2. Set fsdp_version: 2 to use Muon with FSDP."
-                    )
+        if data.get("optimizer") == "muon" and (
+            data.get("deepspeed") or data.get("fsdp") or data.get("fsdp_config")
+        ):
+            raise ValueError(
+                "Muon optimizer is currently incompatible with DeepSpeed and FSDP"
+            )
         return data
 
     @model_validator(mode="before")
@@ -801,36 +794,6 @@ class OptimizationValidationMixin:
             )
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_cross_entropy_conflicts(cls, data):
-        """Check for mutual exclusivity between cross entropy patch options.
-
-        Only one of the following can be enabled at a time:
-        - cut_cross_entropy (CutCrossEntropyPlugin)
-        - chunked_cross_entropy
-        - liger_cross_entropy (LigerPlugin)
-        - liger_fused_linear_cross_entropy (LigerPlugin)
-        """
-        ce_options = {
-            "cut_cross_entropy": data.get("cut_cross_entropy"),
-            "chunked_cross_entropy": data.get("chunked_cross_entropy"),
-            "liger_cross_entropy": data.get("liger_cross_entropy"),
-            "liger_fused_linear_cross_entropy": data.get(
-                "liger_fused_linear_cross_entropy"
-            ),
-        }
-
-        enabled_options = [k for k, v in ce_options.items() if v]
-
-        if len(enabled_options) > 1:
-            raise ValueError(
-                f"Only one cross entropy optimization can be enabled at a time. "
-                f"Found {len(enabled_options)} enabled: {', '.join(enabled_options)}. "
-                "Please disable all but one."
-            )
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_fsdp_version(cls, data):
@@ -877,6 +840,40 @@ class OptimizationValidationMixin:
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_version_in_fsdp_config(cls, data):
+        fsdp_config = data.get("fsdp_config") or {}
+        if fsdp_config and fsdp_config.get("fsdp_version"):
+            LOG.warning(
+                "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
+                "Please configure `fsdp_version` as a top-level field."
+            )
+            data["fsdp_version"] = fsdp_config.pop("fsdp_version")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_config_kwargs_prefix(cls, data):
+        if fsdp_config := data.get("fsdp_config"):
+            should_fix = False
+            for key, _ in fsdp_config.items():
+                if key.startswith("fsdp_"):
+                    should_fix = True
+                    LOG.warning_once(
+                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
+                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
+                    )
+            if should_fix:
+                update_fsdp_config = {}
+                for key, value in fsdp_config.items():
+                    if key.startswith("fsdp_") and key != "fsdp_version":
+                        update_fsdp_config[key.replace("fsdp_", "")] = value
+                    else:
+                        update_fsdp_config[key] = value
+                data["fsdp_config"] = update_fsdp_config
+        return data
+
     @model_validator(mode="after")
     def check_fsdp_offload_w_8bit_optimizer(self):
         if (
@@ -978,40 +975,6 @@ class OptimizationValidationMixin:
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_version_in_fsdp_config(cls, data):
-        fsdp_config = data.get("fsdp_config") or {}
-        if fsdp_config and fsdp_config.get("fsdp_version"):
-            LOG.warning(
-                "Configuring `fsdp_version` in `fsdp_config` is deprecated. "
-                "Please configure `fsdp_version` as a top-level field."
-            )
-            data["fsdp_version"] = fsdp_config.pop("fsdp_version")
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_fsdp_config_kwargs_prefix(cls, data):
-        if fsdp_config := data.get("fsdp_config"):
-            should_fix = False
-            for key, _ in fsdp_config.items():
-                if key.startswith("fsdp_"):
-                    should_fix = True
-                    LOG.warning_once(
-                        "Configuring FSDP fields with the `fsdp_` prefix is deprecated. "
-                        "Please omit the `fsdp_` prefix from the any fields in `fsdp_config`."
-                    )
-            if should_fix:
-                update_fsdp_config = {}
-                for key, value in fsdp_config.items():
-                    if key.startswith("fsdp_") and key != "fsdp_version":
-                        update_fsdp_config[key.replace("fsdp_", "")] = value
-                    else:
-                        update_fsdp_config[key] = value
-                data["fsdp_config"] = update_fsdp_config
-        return data
-
 
 class SystemValidationMixin:
     """Validation methods related to system and hardware configuration."""

src/axolotl/utils/trackio_.py

@@ -1,17 +0,0 @@
-"""Module for trackio utilities"""
-
-import os
-
-from axolotl.utils.dict import DictDefault
-
-
-def setup_trackio_env_vars(cfg: DictDefault):
-    for key in cfg.keys():
-        if key.startswith("trackio_"):
-            value = cfg.get(key, "")
-
-            if value and isinstance(value, str) and len(value) > 0:
-                os.environ[key.upper()] = value
-
-    if cfg.trackio_project_name and len(cfg.trackio_project_name) > 0:
-        cfg.use_trackio = True

src/axolotl/utils/trainer.py

@@ -201,19 +201,33 @@ def add_pose_position_ids(
 
 
 def add_length(sample):
+    """
+    Set the "length" field on a sample to the number of input tokens.
+    
+    Parameters:
+        sample (Mapping-like): A sample containing an "input_ids" sequence.
+    
+    Returns:
+        sample (dict-like): The same sample with "length" set to len(sample["input_ids"]).
+    """
     sample["length"] = len(sample["input_ids"])
     return sample
 
 
 def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2, raise_on_drop=False):
     """
-    Drop samples whose sequence length is either too long (> sequence_len)
-    or too short (< min_sequence_len).
-
-    Works for both single-example (list[int]) or batched (list[list[int]]).
-
-    If raise_on_drop is set, the code raises a ValueError if a sample is
-    encountered that is too long and would have been dropped.
+    Return whether a sample (single or batched) should be kept based on sequence length constraints.
+    
+    Determines if each sequence's length falls within [min_sequence_len, sequence_len]. Supports a single example (list[int]) or a batch (list[list[int]]). If the sample's "input_ids" is empty, the sample is treated as dropped. When raise_on_drop is True, encountering any sequence longer than sequence_len raises a ValueError.
+    
+    Parameters:
+        sample (dict): A mapping containing "input_ids" with either a single sequence or a batch of sequences.
+        sequence_len (int): Maximum allowed sequence length (inclusive).
+        min_sequence_len (int): Minimum allowed sequence length (inclusive).
+        raise_on_drop (bool): If True, raise ValueError when a sequence exceeds sequence_len.
+    
+    Returns:
+        bool or list[bool]: For a single example, returns True if its length is within the bounds, False otherwise. For a batch, returns a list of booleans indicating which sequences should be kept.
     """
     min_sequence_len = min_sequence_len or 2
 
@@ -645,9 +659,6 @@ def setup_parallelism_envs(cfg):
         set_accelerate_parallelism_config = True
         os.environ["PARALLELISM_CONFIG_CP_SIZE"] = str(cfg.context_parallel_size)
         os.environ["ACCELERATE_ALLOW_CP_STANDALONE"] = "true"
-        from axolotl.monkeypatch.accelerate.parallelism_config import patch_prepare_cp
-
-        patch_prepare_cp()
     if set_accelerate_parallelism_config:
         os.environ["ACCELERATE_USE_PARALLELISM_CONFIG"] = "true"
 
@@ -729,4 +740,4 @@ def setup_trainer(
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset
 
-    return trainer_builder.build(total_num_steps)
+    return trainer_builder.build(total_num_steps)

tests/conftest.py

@@ -62,7 +62,7 @@ def snapshot_download_w_retry(*args, **kwargs):
     """
     with hf_offline_context(True):
         try:
-            return snapshot_download(*args, local_files_only=True, **kwargs)
+            return snapshot_download(*args, **kwargs)
         except LocalEntryNotFoundError:
             pass
     with hf_offline_context(False):

tests/core/test_builders.py

@@ -474,8 +474,10 @@ def rand_reward_func(prompts, completions) -> list[float]:
 
             assert trainer.optimizer_cls_and_kwargs is not None
 
-            from axolotl.contribs.mit.muon import MuonOptimizerFactory
-            from axolotl.contribs.mit.muon.muon import Muon
+            from axolotl.contribs.mit.muon import (
+                Muon,
+                MuonOptimizerFactory,
+            )
 
             optimizer_cls, optimizer_kwargs = trainer.optimizer_cls_and_kwargs
             assert optimizer_cls is MuonOptimizerFactory
@@ -554,8 +556,10 @@ class TestHFCausalTrainerBuilder:
 
         assert trainer.optimizer_cls_and_kwargs is not None
 
-        from axolotl.contribs.mit.muon import MuonOptimizerFactory
-        from axolotl.contribs.mit.muon.muon import Muon
+        from axolotl.contribs.mit.muon import (
+            Muon,
+            MuonOptimizerFactory,
+        )
 
         optimizer_cls, optimizer_kwargs = trainer.optimizer_cls_and_kwargs
         assert optimizer_cls is MuonOptimizerFactory

tests/e2e/multigpu/test_dist_muon_fsdp2.py

@@ -1,168 +0,0 @@
-"""Test module for DistMuon optimizer with FSDP2 multi-GPU functionality."""
-
-import os
-from pathlib import Path
-
-import torch
-import yaml
-from accelerate.test_utils import execute_subprocess_async
-from tbparse import SummaryReader
-from transformers.testing_utils import get_torch_dist_unique_port
-
-from axolotl.utils.dict import DictDefault
-
-from tests.e2e.utils import most_recent_subdir, require_torch_2_7_0
-
-AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
-
-
-def verify_training_success(temp_dir):
-    """Verify that training completed successfully by checking artifacts and loss."""
-    output_path = Path(temp_dir)
-
-    model_files = list(output_path.glob("*.bin")) + list(
-        output_path.glob("*.safetensors")
-    )
-    assert len(model_files) > 0, "No model files found - training may have failed"
-
-    checkpoint_files = list(output_path.glob("checkpoint-*"))
-    assert len(checkpoint_files) > 0, (
-        "No checkpoint files found - training may have failed"
-    )
-
-    tb_log_path = most_recent_subdir(temp_dir + "/runs")
-    if tb_log_path:
-        event_files = sorted(os.listdir(tb_log_path))
-        if event_files:
-            event_file = os.path.join(tb_log_path, event_files[0])
-            reader = SummaryReader(event_file)
-            df = reader.scalars
-            train_loss_df = df[df.tag == "train/train_loss"]
-            if len(train_loss_df) > 0:
-                final_loss = train_loss_df.value.values[-1]
-                assert not torch.isnan(torch.tensor(final_loss)), (
-                    f"Training loss is NaN: {final_loss}"
-                )
-
-
-class TestDistMuon:
-    """Test class for DistMuon optimizer with FSDP2 functionality."""
-
-    @require_torch_2_7_0
-    def test_fft_sft(self, temp_dir):
-        cfg = DictDefault(
-            {
-                "base_model": "Qwen/Qwen2.5-0.5B",
-                "sequence_len": 2048,
-                "val_set_size": 0.01,
-                "datasets": [
-                    {
-                        "path": "tatsu-lab/alpaca",
-                        "type": "alpaca",
-                        "split": "train[:10%]",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.02,
-                "optimizer": "muon",
-                "weight_decay": 0.01,
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "fsdp_version": 2,
-                "fsdp_config": {
-                    "offload_params": False,
-                    "cpu_ram_efficient_loading": False,
-                    "transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
-                    "state_dict_type": "FULL_STATE_DICT",
-                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
-                    "reshard_after_forward": True,
-                },
-                "use_tensorboard": True,
-                "bf16": True,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "2",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
-
-        verify_training_success(temp_dir)
-
-    @require_torch_2_7_0
-    def test_lora_sft(self, temp_dir):
-        cfg = DictDefault(
-            {
-                "base_model": "Qwen/Qwen2.5-0.5B",
-                "sequence_len": 2048,
-                "val_set_size": 0.01,
-                "datasets": [
-                    {
-                        "path": "tatsu-lab/alpaca",
-                        "type": "alpaca",
-                        "split": "train[:10%]",
-                    },
-                ],
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "num_epochs": 1,
-                "max_steps": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.02,
-                "optimizer": "muon",
-                "weight_decay": 0.01,
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "fsdp_version": 2,
-                "fsdp_config": {
-                    "offload_params": False,
-                    "cpu_ram_efficient_loading": False,
-                    "transformer_layer_cls_to_wrap": "Qwen2DecoderLayer",
-                    "state_dict_type": "FULL_STATE_DICT",
-                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
-                    "reshard_after_forward": True,
-                },
-                "use_tensorboard": True,
-                "bf16": True,
-            }
-        )
-
-        # write cfg to yaml file
-        Path(temp_dir).mkdir(parents=True, exist_ok=True)
-        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
-            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-        execute_subprocess_async(
-            [
-                "axolotl",
-                "train",
-                str(Path(temp_dir) / "config.yaml"),
-                "--num-processes",
-                "2",
-                "--main-process-port",
-                f"{get_torch_dist_unique_port()}",
-            ]
-        )
-
-        verify_training_success(temp_dir)

tests/e2e/patched/test_resume.py

@@ -2,7 +2,6 @@
 E2E tests for resuming training
 """
 
-import os
 import re
 import subprocess
 
@@ -10,7 +9,6 @@ from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
-from axolotl.utils.callbacks.tokens_per_second import TOKENS_STATE_FILE
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
@@ -60,7 +58,6 @@ class TestResumeLlama:
                 "use_tensorboard": True,
                 "save_safetensors": True,
                 "save_first_step": False,
-                "include_tkps": True,
             }
         )
         if is_torch_bf16_gpu_available():
@@ -71,19 +68,8 @@ class TestResumeLlama:
         normalize_config(cfg)
         dataset_meta = load_datasets(cfg=cfg)
 
-        initial_total_num_tokens = cfg.total_num_tokens
-        assert initial_total_num_tokens is not None, (
-            "total_num_tokens should be calculated during load_datasets"
-        )
-
         train(cfg=cfg, dataset_meta=dataset_meta)
 
-        checkpoint_path = f"{temp_dir}/checkpoint-9"
-        tokens_state_path = os.path.join(checkpoint_path, TOKENS_STATE_FILE)
-        assert os.path.isfile(tokens_state_path), (
-            f"{TOKENS_STATE_FILE} should exist in checkpoint at {tokens_state_path}"
-        )
-
         resume_cfg = cfg | DictDefault(
             {
                 "resume_from_checkpoint": f"{temp_dir}/checkpoint-9/",
@@ -91,24 +77,7 @@ class TestResumeLlama:
         )
         normalize_config(resume_cfg)
 
-        assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
-            f"total_num_tokens should be preserved on resume. "
-            f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
-        )
-
-        resume_dataset_meta = load_datasets(cfg=resume_cfg)
-
-        assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
-            f"total_num_tokens should not be recalculated when resuming. "
-            f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
-        )
-
-        train(cfg=resume_cfg, dataset_meta=resume_dataset_meta)
-
-        assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
-            f"total_num_tokens should remain unchanged after resume training. "
-            f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
-        )
+        train(cfg=resume_cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
 
         tb_log_path_1 = most_recent_subdir(temp_dir + "/runs")

tests/hf_offline_utils.py

@@ -6,6 +6,8 @@ import os
 from contextlib import contextmanager
 from functools import wraps
 
+from huggingface_hub.utils import reset_sessions
+
 
 def reload_modules(hf_hub_offline):
     # Force reload of the modules that check this variable
@@ -19,6 +21,7 @@ def reload_modules(hf_hub_offline):
     huggingface_hub.constants.HF_HUB_OFFLINE = hf_hub_offline
     importlib.reload(datasets.config)
     datasets.config.HF_HUB_OFFLINE = hf_hub_offline
+    reset_sessions()
 
 
 def enable_hf_offline(test_func):

tests/test_data.py

@@ -7,7 +7,6 @@ import unittest
 from transformers import LlamaTokenizer
 
 from axolotl.utils.data import encode_streaming, md5
-from axolotl.utils.trainer import drop_long_seq
 
 from tests.hf_offline_utils import enable_hf_offline
 
@@ -64,42 +63,6 @@ class TestEncodePretraining(unittest.TestCase):
             md5("hello world", "utf-8"), "5eb63bbbe01eeed093cb22bb8f5acdc3"
         )
 
-    def test_excess_length_strategy(self):
-        """Test that excess_length_strategy results in a value error when set to 'raise'."""
-
-        # -- single sequence --
-        # This should work
-        data = {"input_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}
-        drop_long_seq(data, 32, raise_on_drop=True)
-
-        # This should return True, since data fits
-        dropped = drop_long_seq(data, 32)
-        self.assertTrue(dropped)
-
-        # This should raise
-        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-
-        # This should return False, since data doesn't fit
-        dropped = drop_long_seq(data, 15)
-        self.assertFalse(dropped)
-
-        # -- batch sequence --
-        # This should work
-        data = {
-            "input_ids": [
-                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
-                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
-            ]
-        }
-        drop_long_seq(data, 32, raise_on_drop=True)
-
-        # This should raise
-        self.assertRaises(ValueError, drop_long_seq, data, 15, raise_on_drop=True)
-
-        # This should keep the first but drop the second entry
-        dropped = drop_long_seq(data, 15)
-        self.assertEqual(dropped, [True, False])
-
 
 if __name__ == "__main__":
     unittest.main()

tests/test_datasets.py

@@ -13,9 +13,7 @@ from transformers import PreTrainedTokenizer
 
 from axolotl.loaders.tokenizer import load_tokenizer
 from axolotl.utils.data.rl import prepare_preference_datasets
-from axolotl.utils.data.sft import (
-    _load_tokenized_prepared_datasets,
-)
+from axolotl.utils.data.sft import _load_tokenized_prepared_datasets
 from axolotl.utils.dict import DictDefault
 
 from tests.constants import (

tests/test_validation_dataset.py

@@ -363,5 +363,5 @@ class TestOptimizerValidation(BaseValidation):
             }
         )
 
-        with pytest.raises(ValueError, match=r".*only compatible with FSDP2.*"):
+        with pytest.raises(ValueError, match=r".*is currently incompatible with*"):
             validate_config(cfg)

tests/utils/schemas/validation/test_fsdp.py

@@ -123,17 +123,6 @@ class TestFSDPValidation:
         assert cfg.fsdp_config.transformer_layer_cls_to_wrap == "LlamaDecoderLayer"
         assert cfg.fsdp_config.reshard_after_forward is True
 
-    def test_muon_fsdp1_rejected(self, min_base_cfg):
-        cfg = min_base_cfg | DictDefault(
-            optimizer="muon",
-            fsdp_version=1,
-            fsdp_config={"reshard_after_forward": True},
-        )
-        with pytest.raises(
-            ValueError, match="Muon optimizer is only compatible with FSDP2"
-        ):
-            validate_config(cfg)
-
     @pytest.mark.parametrize(
         "rl",
         [