Merge branch 'main' into feat/glm45

.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/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/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:
@@ -112,9 +111,6 @@ 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
@@ -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:
@@ -208,9 +200,6 @@ jobs:
           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/glm45/README.md

@@ -0,0 +1,48 @@
+# Finetune GLM4.5 with Axolotl
+
+[UNSTABLE]
+
+```bash
+# LoRA SFT (4xH200 @ 84GB/GPU)
+axolotl train examples/glm45/glm4.5-lora-fsdp2.yaml
+
+# FFT SFT (4xH200)
+# Checkpointing error on backward pass
+# Without checkpointing => OOM
+axolotl train examples/glm45/glm4.5-fft-fsdp2.yaml
+```
+
+## Dataset
+
+In addition to normal OpenAI Messages format, GLM4.5 support an extra parameter for thinking in assistant section.
+
+```json
+{
+    "role": "assistant",
+    "reasoning_content": "...",  // or have </think>...</think> in `content`
+    "content": "...",
+}
+```
+
+Note:
+- The role name for tools in this template is `tool`.
+- You will see this Axolotl WARNING. This is to be as expected as the template does not use EOS.
+```bash
+EOS token '<|endoftext|>' not found in chat_template. Please check if your template/EOS token is correct.
+```
+- Make sure you set the below extra attributes if needed
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    message_property_mappings:
+      role: role
+      content: content
+
+    #   tool_calls: tool_calls  # uncomment if using tools
+    #   reasoning_content: reasoning_content  # uncomment if have reasoning
+
+# Uncomment if training on tool role (you would rarely if ever need this)
+# eot_tokens:
+#   - <|observation|>
+```

examples/glm45/glm4.5-fft-fsdp2.yaml

@@ -0,0 +1,59 @@
+base_model: zai-org/GLM-4.5-Air
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
+
+datasets:
+  - path: winglian/pirate-ultrachat-10k
+    type: chat_template
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./outputs/qlora-out
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+# gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: true
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Glm4MoeDecoderLayer
+  state_dict_type: SHARDED_STATE_DICT
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/glm45/glm4.5-lora-fsdp2.yaml

@@ -1,37 +1,25 @@
-base_model: moonshotai/Kimi-Linear-48B-A3B-Instruct
-
+base_model: zai-org/GLM-4.5-Air
 # 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
+experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding
 
 datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
+  - path: winglian/pirate-ultrachat-10k
     type: chat_template
-    split: train
-
 dataset_prepared_path: last_run_prepared
-val_set_size: 0.2
-output_dir: ./outputs/lora-out
+val_set_size: 0
+output_dir: ./outputs/qlora-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
@@ -41,29 +29,28 @@ lora_target_modules:
   - k_proj
   - o_proj
 
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+
 wandb_project:
 wandb_entity:
 wandb_watch:
 wandb_name:
 wandb_log_model:
 
-gradient_accumulation_steps: 2
-micro_batch_size: 2
+gradient_accumulation_steps: 1
+micro_batch_size: 1
 num_epochs: 1
-optimizer: adamw_8bit
+optimizer: adamw_torch_4bit
 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:
+# gradient_checkpointing: true
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
 flash_attention: true
 
@@ -71,11 +58,17 @@ loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3
 
 warmup_ratio: 0.1
-evals_per_epoch: 2
+evals_per_epoch: 1
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens:
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: true
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Glm4MoeDecoderLayer
+  state_dict_type: SHARDED_STATE_DICT
+  reshard_after_forward: true
+  # activation_checkpointing: false

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/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/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

@@ -14,22 +14,22 @@ 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
+kernels>=0.9.0
 trackio>=0.13.0
-typing-extensions>=4.15.0
+typing_extensions>=4.14.0
 
 optimum==1.16.2
 hf_transfer
 sentencepiece
 gradio>=6.2.0,<7.0
 
-modal==1.3.0.post1
-pydantic>=2.10.6
+modal==1.0.2
+pydantic>=2.10.6,<2.12
 addict
 fire
 PyYAML>=6.0
@@ -63,7 +63,7 @@ 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
 

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/common/architectures.py

@@ -14,6 +14,7 @@ MOE_ARCH_BLOCK = {
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
+    "glm4_moe": "Glm4MoeMoE",
     "deepseek_v3": "DeepseekV3MoE",
     "gpt_oss": "GptOssDecoderLayer",
     "lfm2_moe": "Lfm2MoeSparseMoeBlock",

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/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

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import json
 import math
 import os
 from collections import defaultdict
@@ -51,8 +50,6 @@ 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 +349,24 @@ 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(
@@ -650,14 +638,10 @@ 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 +683,6 @@ 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/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
@@ -44,6 +44,7 @@ plugins:
 - gemma3n_text
 - glm
 - glm4
+- glm_moe
 - glm4_moe
 - glm4v
 - glm4v_moe
@@ -54,8 +55,6 @@ 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/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/monkeypatch/multipack.py

@@ -37,6 +37,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "deepseek_v3",
     "glm",
     "glm4",
+    "glm4_moe",
     "smollm3",
     "granite",
     "granitemoe",

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/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/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/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/validation.py

@@ -801,36 +801,6 @@ 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):

src/axolotl/utils/trainer.py

@@ -645,9 +645,6 @@ 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"
 

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/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):