wip ao upgrade

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,11 +15,6 @@
 <!--- Include details of your testing environment, tests ran to see how -->
 <!--- your change affects other areas of the code, etc. -->
 
-## AI Usage Disclaimer
-
-<!--- Was AI (e.g., ChatGPT, Claude, Copilot) used to generate or assist with this PR? -->
-<!--- Please indicate: No / Yes (specify which tool and to what extent) -->
-
 ## Screenshots (if appropriate)
 
 ## Types of changes

.github/workflows/base.yml

@@ -21,8 +21,6 @@ jobs:
     timeout-minutes: 480
     # this job needs to be run on self-hosted GPU runners...
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -34,7 +32,6 @@ jobs:
             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"
-            platforms: "linux/amd64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -42,7 +39,6 @@ jobs:
             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-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -50,15 +46,6 @@ 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-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.12"
-            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-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -66,15 +53,6 @@ jobs:
             pytorch: 2.9.1
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -101,7 +79,6 @@ jobs:
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -112,7 +89,6 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
@@ -127,8 +103,6 @@ jobs:
     if: ${{ github.repository_owner == 'axolotl-ai-cloud' && (github.event_name != 'pull_request' || !github.event.pull_request.draft) }}
     timeout-minutes: 480
     runs-on: ubuntu-latest-m
-    env:
-      HAS_DOCKERHUB_CREDS: ${{ secrets.DOCKERHUB_USERNAME != '' && secrets.DOCKERHUB_TOKEN != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -140,7 +114,6 @@ jobs:
             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-uv-base"
-            platforms: "linux/amd64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -148,7 +121,6 @@ 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"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -156,15 +128,6 @@ jobs:
             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"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "129"
-            cuda_version: 12.9.1
-            cudnn_version: ""
-            python_version: "3.12"
-            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"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "130"
             cuda_version: 13.0.0
             cudnn_version: ""
@@ -172,15 +135,6 @@ jobs:
             pytorch: 2.9.1
             torch_cuda_arch_list: "9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: "130"
-            cuda_version: 13.0.0
-            cudnn_version: ""
-            python_version: "3.12"
-            pytorch: 2.9.1
-            torch_cuda_arch_list: "9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -192,7 +146,6 @@ jobs:
             axolotlai/axolotl-base-uv
       - name: Login to Docker Hub
         uses: docker/login-action@v2
-        if: ${{ github.event_name != 'pull_request' && env.HAS_DOCKERHUB_CREDS == 'true' }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -203,7 +156,6 @@ jobs:
         with:
           context: .
           file: ./docker/${{ matrix.dockerfile }}
-          platforms: ${{ matrix.platforms }}
           push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}

.github/workflows/main.yml

@@ -20,32 +20,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-            is_latest: true
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
+#          - 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
@@ -71,7 +61,6 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: ${{ matrix.platforms }}
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
             CUDA=${{ matrix.cuda }}
@@ -98,32 +87,22 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
-            platforms: "linux/amd64"
+            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.0
             axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-            is_latest: true
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
+#          - 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
@@ -148,7 +127,6 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: ${{ matrix.platforms }}
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}
@@ -169,11 +147,11 @@ jobs:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.9.1
+            pytorch: 2.8.0
             axolotl_extras:
-            is_latest: true
-          - cuda: 130
-            cuda_version: 13.0.0
+            is_latest:
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
@@ -202,7 +180,6 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          platforms: linux/amd64,linux/arm64
           build-args: |
             BASE_TAG=${{ github.ref_type == 'tag' && 'main' || github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
             CUDA=${{ matrix.cuda }}

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

@@ -35,26 +35,14 @@ jobs:
             pytorch: 2.8.0
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
+            nightly_build: "true"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
             pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
-            num_gpus: 2
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
-            axolotl_extras: "fbgemm-gpu"
-            num_gpus: 2
-            dockerfile: "Dockerfile-uv.jinja"
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            axolotl_extras:
-#            axolotl_extras: fbgemm-gpu
+            axolotl_extras: fbgemm-gpu
             num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -76,8 +64,8 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |
           modal run -m cicd.multigpu

.github/workflows/pypi.yml

@@ -40,7 +40,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging==26.0
+          pip3 install wheel packaging==23.2
           pip3 install --no-build-isolation -e .
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
@@ -48,9 +48,9 @@ jobs:
         id: tag
         run: echo ::set-output name=TAG_NAME::$(echo $GITHUB_REF | cut -d / -f 3)
 
-      - name: Update version in VERSION file
+      - name: Update version in setup.py
         run: |
-          echo "${{ steps.tag.outputs.TAG_NAME }}" | sed 's/^v//' > VERSION
+          sed -i -E 's/version="([0-9.]+)",/version="${{ steps.tag.outputs.TAG_NAME }}",/g' setup.py
 
       - name: Build a source dist
         run: |

.github/workflows/tests-nightly.yml

@@ -48,7 +48,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |

.github/workflows/tests.yml

@@ -54,13 +54,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
+        python_version: ["3.11"]
         pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -87,7 +82,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |
@@ -115,10 +110,10 @@ jobs:
 
       - name: Pre-Download dataset fixture
         run: |
-          hf download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
+          huggingface-cli download --repo-type=dataset axolotl-ai-internal/axolotl-oss-dataset-fixtures
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -132,7 +127,7 @@ jobs:
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5
@@ -149,13 +144,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.11", "3.12"]
+        python_version: ["3.11"]
         pytorch_version: ["2.8.0", "2.9.0", "2.9.1"]
-        exclude:
-          - python_version: "3.12"
-            pytorch_version: "2.8.0"
-          - python_version: "3.12"
-            pytorch_version: "2.9.0"
     timeout-minutes: 20
 
     steps:
@@ -182,7 +172,7 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 setuptools_scm build wheel psutil
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel psutil
 
       - name: Install PyTorch
         run: |
@@ -210,7 +200,7 @@ jobs:
           axolotl --help
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
       - name: Run tests
         run: |
@@ -219,10 +209,10 @@ jobs:
           pytest -v --durations=10 tests/cli/
 
       - name: Show HF cache
-        run: hf cache ls
+        run: hf cache scan
 
   gate-skip-e2e:
-    needs: [pre-commit]
+    needs: [pre-commit, pytest, pytest-sdist]
     runs-on: ubuntu-latest
     outputs:
       skip: ${{ steps.compute.outputs.skip }}
@@ -258,16 +248,16 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
-    needs: [pre-commit, pytest]
+    needs: [pre-commit, pytest, pytest-sdist, gate-skip-e2e]
 
     strategy:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
-            pytorch: 2.9.1
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
             num_gpus: 1
             axolotl_extras:
             dockerfile: "Dockerfile-uv.jinja"
@@ -326,12 +316,6 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
-          - cuda: 130
-            cuda_version: 13.0.0
-            python_version: "3.11"
-            pytorch: 2.9.1
-            num_gpus: 1
-            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -369,9 +353,9 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 129
-            cuda_version: 12.9.1
-            python_version: "3.12"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:

.runpod/README.md

@@ -123,7 +123,7 @@ datasets:
 | --------------------------------- | -------------------------- | ----------------------------------- |
 | `dataset_prepared_path`           | `"data/last_run_prepared"` | Path for prepared dataset           |
 | `push_dataset_to_hub`             | `""`                       | Push dataset to HF hub              |
-| `dataset_num_proc`                | `4`                        | Number of preprocessing processes   |
+| `dataset_processes`               | `4`                        | Number of preprocessing processes   |
 | `dataset_keep_in_memory`          | `false`                    | Keep dataset in memory              |
 | `shuffle_merged_datasets`         | `true`                     | Shuffle merged datasets             |
 | `shuffle_before_merging_datasets` | `false`                    | Shuffle each dataset before merging |

.runpod/src/config/config.yaml

@@ -39,6 +39,7 @@
 #     type: # linear | dynamic
 #     factor: # float
 
+
 # # Whether you are training a 4-bit GPTQ quantized model
 # gptq: true
 # gptq_groupsize: 128 # group size
@@ -106,7 +107,7 @@
 # push_dataset_to_hub: # repo path
 # # The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
 # # if not set.
-# dataset_num_proc: # defaults to os.cpu_count() if not set
+# dataset_processes: # defaults to os.cpu_count() if not set
 # # push checkpoints to hub
 # hub_model_id: # repo path to push finetuned model
 # # how to push checkpoints to hub
@@ -223,6 +224,9 @@
 # eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 # eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 
+# # Save model as safetensors (require safetensors package)
+# save_safetensors:
+
 # # Whether to mask out or include the human's prompt from the training labels
 # train_on_inputs: false
 # # Group similarly sized data to minimize padding.
@@ -348,6 +352,8 @@
 # # Allow overwrite yml config using from cli
 # strict:
 
+
+
 base_model: ${BASE_MODEL}
 base_model_ignore_patterns: ${BASE_MODEL_IGNORE_PATTERNS}
 base_model_config: ${BASE_MODEL_CONFIG}
@@ -406,7 +412,7 @@ chat_template_jinja: ${CHAT_TEMPLATE_JINJA}
 default_system_message: ${DEFAULT_SYSTEM_MESSAGE}
 dataset_prepared_path: ${DATASET_PREPARED_PATH}
 push_dataset_to_hub: ${PUSH_DATASET_TO_HUB}
-dataset_num_proc: ${DATASET_NUM_PROC}
+dataset_processes: ${DATASET_PROCESSES}
 dataset_keep_in_memory: ${DATASET_KEEP_IN_MEMORY}
 hub_model_id: ${HUB_MODEL_ID}
 hub_strategy: ${HUB_STRATEGY}
@@ -506,6 +512,7 @@ profiler_steps: ${PROFILER_STEPS}
 loss_watchdog_threshold: ${LOSS_WATCHDOG_THRESHOLD}
 loss_watchdog_patience: ${LOSS_WATCHDOG_PATIENCE}
 
+save_safetensors: ${SAVE_SAFETENSORS}
 train_on_inputs: ${TRAIN_ON_INPUTS}
 group_by_length: ${GROUP_BY_LENGTH}
 gradient_checkpointing: ${GRADIENT_CHECKPOINTING}

README.md

@@ -88,7 +88,7 @@ Features:
 #### Using pip
 
 ```bash
-pip3 install -U packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 
 # Download example axolotl configs, deepspeed configs

VERSION

@@ -1 +0,0 @@
-0.15.0.dev0

_quarto.yml

@@ -251,6 +251,7 @@ website:
                 - docs/models/olmo3.qmd
                 - docs/models/trinity.qmd
                 - docs/models/arcee.qmd
+                - docs/models/mistral.qmd
                 - section: "Ministral3"
                   contents:
                     - docs/models/ministral3.qmd
@@ -265,7 +266,6 @@ website:
                 - docs/models/mistral-small.qmd
                 - docs/models/voxtral.qmd
                 - docs/models/devstral.qmd
-                - docs/models/mistral.qmd
                 - docs/models/llama-4.qmd
                 - docs/models/llama-2.qmd
                 - docs/models/qwen3-next.qmd
@@ -320,7 +320,6 @@ website:
             - docs/multipack.qmd
             - docs/mixed_precision.qmd
             - docs/optimizers.qmd
-            - docs/attention.qmd
 
         - section: "Advanced Features"
           contents:

cicd/Dockerfile-uv.jinja

@@ -31,7 +31,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN uv pip install packaging==26.0 setuptools==75.8.0
+RUN uv pip install packaging==23.2 setuptools==75.8.0
 RUN uv pip install torchvision
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

cicd/Dockerfile.jinja

@@ -32,7 +32,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
         sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
     fi
 
-RUN pip install packaging==26.0 setuptools==75.8.0 psutil
+RUN pip install packaging==23.2 setuptools==75.8.0 psutil
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/multigpu.py

@@ -17,8 +17,7 @@ template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
 template_env = jinja2.Environment(
     loader=template_loader, autoescape=select_autoescape()
 )
-dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
-df_template = template_env.get_template(dockerfile)
+df_template = template_env.get_template("Dockerfile.jinja")
 
 df_args = {
     "AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
@@ -28,11 +27,8 @@ df_args = {
     "CUDA": os.environ.get("CUDA", "126"),
     "GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
     "GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
-    "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
     "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
     "HF_HOME": "/workspace/data/huggingface-cache/hub",
-    "PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
-    "DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
 }
 
 dockerfile_contents = df_template.render(**df_args)

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=3 \
+pytest -v --durations=10 -n2 --maxfail=4 \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/ \
   --ignore=/workspace/axolotl/tests/e2e/multigpu/patched/ \
   /workspace/axolotl/tests/e2e/multigpu/ \

docker/Dockerfile

@@ -6,7 +6,6 @@ ARG AXOLOTL_EXTRAS=""
 ARG AXOLOTL_ARGS=""
 ARG CUDA="118"
 ARG PYTORCH_VERSION="2.1.2"
-ARG TARGETARCH
 
 ENV PYTORCH_VERSION=$PYTORCH_VERSION
 
@@ -21,17 +20,13 @@ RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
 
 WORKDIR /workspace/axolotl
 
-# If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
-RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+# If AXOLOTL_EXTRAS is set, append it in brackets
+RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi && \
-    if [ "$AXOLOTL_EXTRAS" != "" ]; then \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
-    fi && \    python scripts/unsloth_install.py | sh && \
+    python scripts/unsloth_install.py | sh && \
     python scripts/cutcrossentropy_install.py | sh && \
     pip install pytest && \
     pip cache purge

docker/Dockerfile-base

@@ -2,16 +2,14 @@ ARG CUDA_VERSION="11.8.0"
 ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
-ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
-ARG TARGETARCH
-ARG PYTHON_VERSION="3.11"
+ARG PYTHON_VERSION="3.10"
 ARG PYTORCH_VERSION="2.1.2"
-ARG CUDA="128"
+ARG CUDA="118"
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -24,17 +22,11 @@ RUN apt-get update \
         librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm \
     && rm -rf /var/cache/apt/archives \
     && rm -rf /var/lib/apt/lists/* \
-    && if [ "$TARGETARCH" = "amd64" ]; then \
-        MINICONDA_ARCH="x86_64"; \
-    elif [ "$TARGETARCH" = "arm64" ]; then \
-        MINICONDA_ARCH="aarch64"; \
-    else \
-        echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-    fi \
-    && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh \
+    && wget \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \
-    && bash Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh -b \
-    && rm -f Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
     && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main \
     && conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r \
     && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
@@ -43,7 +35,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==26.0 setuptools==75.8.0 wheel psutil && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel psutil && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
     python3 -m pip cache purge
 
@@ -59,34 +51,8 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN case "$PYTORCH_VERSION" in \
-        2.9.[0-9]*) \
-            if [ "$CUDA" = "128" ]; then \
-                if [ "$TARGETARCH" = "amd64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl"; \
-                    WHL_VERSION="v0.5.4"; \
-                elif [ "$TARGETARCH" = "arm64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl"; \
-                    WHL_VERSION="v0.6.4"; \
-                else \
-                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-                fi; \
-                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
-                pip3 install --no-cache-dir ${WHL_FILE}; \
-                rm ${WHL_FILE}; \
-            elif [ "$CUDA" = "130" ]; then \
-                if [ "$TARGETARCH" = "amd64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl"; \
-                    WHL_VERSION="v0.5.4"; \
-                elif [ "$TARGETARCH" = "arm64" ]; then \
-                    WHL_FILE="flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl"; \
-                    WHL_VERSION="v0.6.4"; \
-                else \
-                    echo "Unsupported architecture: $TARGETARCH"; exit 1; \
-                fi; \
-                wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}; \
-                pip3 install --no-cache-dir ${WHL_FILE}; \
-                rm ${WHL_FILE}; \
-            fi \
-            ;; \
-    esac
+RUN if [ "$PYTORCH_VERSION" =~ ^2\.9\.[0-9]+$ ] && [ "$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; \
+    fi

docker/Dockerfile-base-nightly

@@ -30,7 +30,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==26.0 setuptools==75.8.0 wheel && \
+RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
     python3 -m pip install --no-cache-dir -U torch --extra-index-url https://download.pytorch.org/whl/nightly/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \

docker/Dockerfile-uv-base

@@ -2,7 +2,6 @@ ARG CUDA_VERSION="12.6.3"
 ARG CUDNN_VERSION=""
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
-ARG TARGETARCH
 
 FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
@@ -32,35 +31,12 @@ ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
 RUN uv pip install packaging setuptools wheel psutil \
     && uv pip install torch==${PYTORCH_VERSION} torchvision \
+    && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
+    && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
 
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main"; \
-        uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
+RUN if [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
     fi
-
-RUN case "$PYTORCH_VERSION" in \
-        2.9.[0-9]*) \
-            if [ "$TARGETARCH" = "amd64" ]; then \
-                if [ "$CUDA" = "128" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip 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; \
-                elif [ "$CUDA" = "130" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_x86_64.whl; \
-                fi \
-            elif [ "$TARGETARCH" = "arm64" ]; then \
-                if [ "$CUDA" = "128" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_aarch64.whl; \
-                elif [ "$CUDA" = "130" ]; then \
-                    wget -nv https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.6.4/flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    uv pip install --no-cache-dir flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                    rm flash_attn-2.8.3+cu130torch2.9-cp311-cp311-linux_aarch64.whl; \
-                fi \
-            fi \
-            ;; \
-    esac

docs/amd_hpc.qmd

@@ -86,7 +86,7 @@ export HF_DATASETS_OFFLINE=1
 Download a base model using the Hugging Face CLI:
 
 ```bash
-hf download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B --local-dir ~/hfdata/llama3.1-8B
 ```
 
 ### 10. Create Axolotl Configuration

docs/attention.qmd

@@ -1,140 +0,0 @@
----
-title: Attention
-description: Supported attention modules in Axolotl
----
-
-## SDP Attention
-
-This is the default built-in attention in PyTorch.
-
-```yaml
-sdp_attention: true
-```
-
-For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
-
-## Flash Attention 2
-
-Uses efficient kernels to compute attention.
-
-```yaml
-flash_attention: true
-```
-
-For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
-
-### Nvidia
-
-Requirements: Ampere, Ada, or Hopper GPUs
-
-Note: For Turing GPUs or lower, please use other attention methods.
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
-::: {.callout-tip}
-
-If you get `undefined symbol` while training, ensure you installed PyTorch prior to Axolotl. Alternatively, try reinstall or downgrade a version.
-
-:::
-
-#### Flash Attention 3
-
-Requirements: Hopper only and CUDA 12.8 (recommended)
-
-```bash
-git clone https://github.com/Dao-AILab/flash-attention.git
-cd flash-attention/hopper
-
-python setup.py install
-```
-
-### AMD
-
-Requirements: ROCm 6.0 and above.
-
-See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
-
-## Flex Attention
-
-A flexible PyTorch API for attention used in combination with `torch.compile`.
-
-```yaml
-flex_attention: true
-
-# recommended
-torch_compile: true
-```
-
-::: {.callout-note}
-
-We recommend using latest stable version of PyTorch for best performance.
-
-:::
-
-For more details: [PyTorch docs](https://pytorch.org/blog/flexattention/)
-
-## SageAttention
-
-Attention kernels with QK Int8 and PV FP16 accumulator.
-
-```yaml
-sage_attention: true
-```
-
-Requirements: Ampere, Ada, or Hopper GPUs
-
-```bash
-pip install sageattention==2.2.0 --no-build-isolation
-```
-
-::: {.callout-warning}
-
-Only LoRA/QLoRA recommended at the moment. We found loss drop to 0 for full finetuning. See [GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
-
-:::
-
-For more details: [Sage Attention](https://github.com/thu-ml/SageAttention)
-
-::: {.callout-note}
-
-We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
-
-:::
-
-
-## xFormers
-
-```yaml
-xformers_attention: true
-```
-
-::: {.callout-tip}
-
-We recommend using with Turing GPUs or below (such as on Colab).
-
-:::
-
-For more details: [xFormers](https://github.com/facebookresearch/xformers)
-
-## Shifted Sparse Attention
-
-::: {.callout-warning}
-
-We plan to deprecate this! If you use this feature, we recommend switching to methods above.
-
-:::
-
-Requirements: LLaMA model architecture
-
-```yaml
-flash_attention: true
-s2_attention: true
-```
-
-::: {.callout-tip}
-
-No sample packing support!
-
-:::

docs/cli.qmd

@@ -210,8 +210,6 @@ axolotl lm-eval config.yml
 Configuration options:
 
 ```yaml
-lm_eval_model: # model to evaluate (local or hf path)
-
 # List of tasks to evaluate
 lm_eval_tasks:
   - arc_challenge
@@ -220,7 +218,7 @@ lm_eval_batch_size: # Batch size for evaluation
 output_dir: # Directory to save evaluation results
 ```
 
-See [LM Eval Harness integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#language-model-evaluation-harness-lm-eval) for full configuration details.
+See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
 
 ### delinearize-llama4
 

docs/installation.qmd

@@ -165,7 +165,7 @@ We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
    ```
 4. (Optional) Login to Hugging Face:
    ```{.bash}
-   hf auth login
+   huggingface-cli login
    ```
 
 ## Troubleshooting {#sec-troubleshooting}

docs/lora_optims.qmd

@@ -89,10 +89,6 @@ lora_o_kernel: true
 Currently, LoRA kernels are not supported for RLHF training, only SFT.
 :::
 
-::: {.callout-warning}
-LoRA kernels do not support remote modeling code.
-:::
-
 ## Requirements
 
 - One or more NVIDIA or AMD GPUs (in order to use the Triton kernels)

docs/multimodal.qmd

@@ -19,7 +19,6 @@ format:
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
-- [GLM-4.6V](#sec-glm-4-6v)
 - [SmolVLM2](#sec-smolvlm2)
 - [LFM2-VL](#sec-lfm2-vl)
 - [Intern-VL](#sec-intern-vl)
@@ -184,18 +183,6 @@ base_model: Qwen/Qwen3-VL-4B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
-### GLM-4.6V {#sec-glm-4-6v}
-
-Both GLM-4.6V (106B MoE) and GLM-4.6V-Flash (9B) are supported.
-
-```yaml
-# GLM-4.6V (106B MoE version)
-base_model: zai-org/GLM-4.6V
-
-# OR GLM-4.6V-Flash (9B version)
-base_model: zai-org/GLM-4.6V-Flash
-```
-
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}

docs/rlhf.qmd

@@ -17,7 +17,6 @@ feedback. Various methods include, but not limited to:
 - [Kahneman-Tversky Optimization (KTO)](#kto)
 - [Odds Ratio Preference Optimization (ORPO)](#orpo)
 - [Group Relative Policy Optimization (GRPO)](#grpo)
-- [Group Reward-Decoupled Policy Optimization (GDPO)](#gdpo)
 
 
 ## RLHF using Axolotl
@@ -721,102 +720,6 @@ trl:
 
 For more information, see [GRPO docs](https://huggingface.co/docs/trl/v0.17.0/en/grpo_trainer#loss-types).
 
-### GDPO
-
-GDPO (Group Reward-Decoupled Policy Optimization) extends GRPO for multi-reward training. It addresses the **reward advantage collapse** problem by normalizing each reward function independently before combining them.
-
-::: {.callout-tip}
-Use GDPO when training with multiple reward functions. For single reward, GRPO and GDPO produce equivalent results.
-:::
-
-Paper: [https://arxiv.org/pdf/2501.05242](https://arxiv.org/pdf/2501.05242)
-
-GDPO uses TRL's native `multi_objective_aggregation` parameter under the hood. When you set `rl: gdpo`, axolotl automatically configures TRL to use `normalize_then_sum` aggregation.
-
-```yaml
-base_model: Qwen/Qwen2.5-1.5B-Instruct
-
-vllm:
-    host: 0.0.0.0
-    port: 8000
-    tensor_parallel_size: 2
-    gpu_memory_utilization: 0.85
-
-rl: gdpo
-
-trl:
-    beta: 0.001
-    max_completion_length: 256
-    use_vllm: true
-    num_generations: 4
-    reward_funcs:
-        - rewards.format_reward
-        - rewards.correctness_reward
-    reward_weights: [1.0, 2.0]
-
-datasets:
-    - path: openai/gsm8k
-      name: main
-      type: rewards.oai_gsm8k_transform
-```
-
-You can also use GRPO with explicit aggregation control:
-
-```yaml
-rl: grpo
-trl:
-    multi_objective_aggregation: normalize_then_sum  # GDPO behavior
-    # or: sum_then_normalize  # Default GRPO behavior
-```
-
-#### GDPO vs GRPO
-
-| Aspect | GRPO | GDPO |
-|--------|------|------|
-| **Aggregation** | `sum_then_normalize` | `normalize_then_sum` |
-| **Multi-reward** | May collapse advantages | Preserves reward signals |
-| **Single reward** | Standard behavior | Equivalent to GRPO |
-
-#### Why GDPO?
-
-When using multiple rewards with GRPO, different reward combinations can produce identical advantages:
-
-```
-# Example: format + correctness rewards
-[format=0, correct=3] → sum=3
-[format=1, correct=2] → sum=3  ← GRPO sees these as equal!
-[format=2, correct=1] → sum=3
-[format=3, correct=0] → sum=3
-```
-
-GDPO normalizes each reward independently, preserving their relative differences.
-
-#### Reward Functions
-
-GDPO uses the same reward function format as GRPO:
-
-```python
-# rewards.py
-def format_reward(completions, **kwargs) -> list[float]:
-    return [1.0 if len(c) > 10 else 0.0 for c in completions]
-
-def correctness_reward(completions, answers, **kwargs) -> list[float]:
-    rewards = []
-    for completion, answer in zip(completions, answers):
-        # Your scoring logic here
-        rewards.append(score)
-    return rewards
-```
-
-#### Sequence Parallelism
-
-GDPO supports sequence parallelism for long-context training:
-
-```yaml
-rl: gdpo
-context_parallel_size: 2
-```
-
 ### SimPO
 
 SimPO uses [CPOTrainer](https://huggingface.co/docs/trl/main/en/cpo_trainer) but with alternative loss function.

examples/apertus/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/arcee/README.md

@@ -17,7 +17,7 @@ Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the A
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

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@0d4ce4b\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2\""
    ]
   },
   {

examples/devstral/README.md

@@ -16,7 +16,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/devstral/devstral-small-qlora.yml

@@ -52,7 +52,6 @@ gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
-scaling_softmax: true
 
 loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3

examples/eaft/eaft-example.yml

@@ -1,77 +0,0 @@
-base_model: google/gemma-3-1b-it
-
-model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
-
-# gemma3 doesn't seem to play nice with ddp
-ddp_find_unused_parameters: true
-
-chat_template: gemma3
-eot_tokens:
-  - <end_of_turn>
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path:
-val_set_size: 0
-output_dir: ./outputs/eaft-gemma-3-1b
-
-use_eaft: true
-eaft_alpha: 1.0
-eaft_k: 20
-
-sequence_len: 1024
-sample_packing: false
-
-adapter:
-lora_model_dir:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-eval_batch_size: 1
-max_steps: 1000
-evaluation_strategy: "no"
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-weight_decay: 0.0
-debug:
-deepspeed:
-fsdp:
-fsdp_config:
-special_tokens:

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

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-1b-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,7 +1,6 @@
 base_model: google/gemma-3-270m-it
 
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
@@ -30,7 +29,7 @@ output_dir: ./outputs/out
 adapter: qlora
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+lora_dropout: 0.05
 lora_target_linear: true
 
 sequence_len: 2048

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

@@ -2,7 +2,6 @@ base_model: google/gemma-3-4b-it
 
 # Need to set else transformers tries to load vision too
 model_type: Gemma3ForCausalLM
-cls_model_config: Gemma3TextConfig
 
 load_in_4bit: true
 
@@ -33,8 +32,8 @@ sample_packing: true
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
-lora_target_linear: true
+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:

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

@@ -31,7 +31,7 @@ pad_to_sequence_len: false
 
 lora_r: 32
 lora_alpha: 16
-lora_dropout: 0
+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:

examples/gemma3n/README.md

@@ -10,7 +10,7 @@ Gemma-3n is a family of multimodal models from Google found on [HuggingFace](htt
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/glm46v/README.md

@@ -1,44 +0,0 @@
-# Finetune GLM-4.6V with Axolotl
-
-GLM-4.6V is a family of vision-language models from ZhipuAI found on [HuggingFace](https://huggingface.co/zai-org/GLM-4.6V). This guide shows how to fine-tune it with Axolotl for vision-language tasks.
-
-
-
-## Getting started
-
-1. Install Axolotl from source following the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
-
-2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
-
-
-3. Run the fine-tuning:
-
-    glm-4-6v-flash(9B)
-    ```bash
-    axolotl train examples/glm46v/glm-4-6v-flash-qlora.yaml
-    ```
-
-Let us know how it goes. Happy finetuning! 🚀
-
-## Tips
-
-- Vision datasets should follow the format described in the [multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format)
-- 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 in the [dataset loading docs](https://docs.axolotl.ai/docs/dataset_loading.html).
-
-## Supported Models
-
-- **GLM-4.6V**: Full vision-language model (`zai-org/GLM-4.6V`)
-- **GLM-4.6V-Flash**: Faster variant (`zai-org/GLM-4.6V-Flash`)
-
-## Optimization Guides
-
-Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
-
-## Related Resources
-
-- [ZhipuAI GLM-4.6V](https://huggingface.co/zai-org/GLM-4.6V)
-- [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/glm46v/glm-4-6v-flash-ddp.yaml

@@ -1,53 +0,0 @@
-base_model: zai-org/GLM-4.6V-Flash
-trust_remote_code: true
-
-processor_type: AutoProcessor
-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
-ddp_find_unused_parameters: true
-
-output_dir: ./outputs/glm-4-6v-flash-qlora
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-
-adapter: qlora
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-sequence_len: 2048
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-logging_steps: 1
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 0
-saves_per_epoch: 1
-weight_decay: 0.0

examples/glm46v/glm-4-6v-flash-qlora.yaml

@@ -1,50 +0,0 @@
-base_model: zai-org/GLM-4.6V-Flash
-trust_remote_code: true
-
-processor_type: AutoProcessor
-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
-
-output_dir: ./outputs/glm-4-6v-flash-qlora
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-
-adapter: qlora
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-sequence_len: 2048
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-logging_steps: 1
-sdp_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 0
-saves_per_epoch: 1
-weight_decay: 0.0

examples/gpt-oss/README.md

@@ -14,7 +14,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/granite4/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/hunyuan/README.md

@@ -13,7 +13,7 @@ Tencent released a family of opensource models called HunYuan with varying param
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/jamba/qlora_fsdp_large.yaml

@@ -19,6 +19,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: jamba-large-fsdp-qlora-ft
+save_safetensors: true
 adapter: qlora
 sequence_len: 2048
 sample_packing: true

examples/llama-3/qlora-1b-gdpo.yaml

@@ -1,68 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B-Instruct
-
-chat_template: llama3
-
-rl: gdpo
-
-trl:
-  beta: 0.001
-  max_completion_length: 128
-  num_generations: 2
-  temperature: 0.7
-  top_p: 0.95
-
-  use_vllm: false
-
-
-  multi_objective_aggregation: normalize_then_sum
-
-  reward_funcs:
-    - rwd.format_reward
-    - rwd.correctness_reward
-  reward_weights: [1.0, 2.0]
-
-  log_completions: true
-  num_completions_to_print: 3
-  scale_rewards: true
-
-datasets:
-  - path: openai/gsm8k
-    name: main
-    split: train[:1000]
-    type: rwd.gsm8k_transform
-
-val_set_size: 0.0
-output_dir: ./outputs/llama3-gdpo-out
-
-sequence_len: 512
-sample_packing: false
-pad_to_sequence_len: false
-
-gradient_accumulation_steps: 8
-micro_batch_size: 1
-num_epochs: 1
-max_steps: 100
-
-optimizer: adamw_torch_fused
-lr_scheduler: cosine
-learning_rate: 5e-5
-weight_decay: 0.01
-warmup_steps: 10
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-flash_attention: true
-logging_steps: 1
-save_steps: 50
-save_safetensors: true
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-
-seed: 42

examples/llama-3/qlora-fsdp-405b.yaml

@@ -12,6 +12,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./outputs/out/qlora-llama3_1-405b
+save_safetensors: true
 
 adapter: qlora
 

examples/magistral/README.md

@@ -14,7 +14,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for these
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.7.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

examples/mamba/config.yml

@@ -47,5 +47,6 @@ saves_per_epoch: 1
 weight_decay: 0.0
 special_tokens:
 tokens:
+save_safetensors: False
 
 # save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/ministral3/ministral3-3b-qlora.yaml

@@ -59,7 +59,6 @@ gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
 flash_attention: true
-scaling_softmax: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/qwen3-next/README.md

@@ -15,7 +15,7 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
 
 # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy

examples/swanlab/README.md

@@ -1,285 +0,0 @@
-# SwanLab Integration Examples
-
-This directory contains example configurations demonstrating SwanLab integration with Axolotl.
-
-## Examples Overview
-
-### 1. DPO with Completion Logging
-**File**: `dpo-swanlab-completions.yml`
-
-Demonstrates DPO (Direct Preference Optimization) training with RLHF completion table logging.
-
-**Features**:
-- Basic SwanLab experiment tracking
-- Completion table logging (prompts, chosen/rejected responses, rewards)
-- Memory-bounded buffer for long training runs
-- Cloud sync configuration
-
-**Best for**: RLHF practitioners who want to analyze model outputs qualitatively
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-completions.yml
-```
-
----
-
-### 2. LoRA with Performance Profiling
-**File**: `lora-swanlab-profiling.yml`
-
-Demonstrates standard LoRA fine-tuning with performance profiling enabled.
-
-**Features**:
-- SwanLab experiment tracking
-- Automatic profiling of trainer methods
-- Profiling metrics visualization
-- Performance optimization guidance
-
-**Best for**: Engineers optimizing training performance and comparing different configurations
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-accelerate launch -m axolotl.cli.train examples/swanlab/lora-swanlab-profiling.yml
-```
-
----
-
-### 3. Full-Featured DPO Production Setup
-**File**: `dpo-swanlab-full-featured.yml`
-
-Comprehensive production-ready configuration with ALL SwanLab features enabled.
-
-**Features**:
-- Experiment tracking with team workspace
-- RLHF completion logging
-- Performance profiling
-- Lark (Feishu) team notifications
-- Private deployment support
-- Production checklist and troubleshooting
-
-**Best for**: Production RLHF training with team collaboration
-
-**Quick start**:
-```bash
-export SWANLAB_API_KEY=your-api-key
-export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-export SWANLAB_LARK_SECRET=your-webhook-secret
-accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-full-featured.yml
-```
-
----
-
-### 4. Custom Trainer Profiling (Python)
-**File**: `custom_trainer_profiling.py`
-
-Python code examples showing how to add SwanLab profiling to custom trainers.
-
-**Features**:
-- `@swanlab_profile` decorator examples
-- Context manager profiling for fine-grained timing
-- `ProfilingConfig` for advanced filtering and throttling
-- Multiple profiling patterns and best practices
-
-**Best for**: Advanced users creating custom trainers
-
-**Usage**:
-```python
-from custom_trainer_profiling import CustomTrainerWithProfiling
-# See file for detailed examples and patterns
-```
-
----
-
-## Feature Matrix
-
-| Example | Tracking | Completion Logging | Profiling | Lark Notifications | Team Workspace |
-|---------|----------|-------------------|-----------|-------------------|----------------|
-| dpo-swanlab-completions.yml | ✅ | ✅ | ✅ (auto) | ➖ (commented) | ➖ (commented) |
-| lora-swanlab-profiling.yml | ✅ | ➖ (disabled) | ✅ (auto) | ➖ (commented) | ➖ (commented) |
-| dpo-swanlab-full-featured.yml | ✅ | ✅ | ✅ (auto) | ✅ | ✅ |
-| custom_trainer_profiling.py | N/A | N/A | ✅ (manual) | N/A | N/A |
-
----
-
-## Configuration Quick Reference
-
-### Basic SwanLab Setup
-```yaml
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-use_swanlab: true
-swanlab_project: my-project
-swanlab_experiment_name: my-experiment
-swanlab_mode: cloud  # cloud, local, offline, disabled
-```
-
-### RLHF Completion Logging
-```yaml
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 steps
-swanlab_completion_max_buffer: 128    # Memory-bounded buffer
-```
-
-### Lark Team Notifications
-```yaml
-swanlab_lark_webhook_url: https://open.feishu.cn/...
-swanlab_lark_secret: your-webhook-secret  # Required for production
-```
-
-### Team Workspace
-```yaml
-swanlab_workspace: my-research-team
-```
-
-### Private Deployment
-```yaml
-swanlab_web_host: https://swanlab.yourcompany.com
-swanlab_api_host: https://api.swanlab.yourcompany.com
-```
-
----
-
-## Authentication
-
-### Recommended: Environment Variable
-```bash
-export SWANLAB_API_KEY=your-api-key
-export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-export SWANLAB_LARK_SECRET=your-webhook-secret
-```
-
-### Alternative: Config File (less secure)
-```yaml
-swanlab_api_key: your-api-key
-swanlab_lark_webhook_url: https://open.feishu.cn/...
-swanlab_lark_secret: your-webhook-secret
-```
-
----
-
-## Common Use Cases
-
-### Use Case 1: Migrate from WandB to SwanLab
-Start with `lora-swanlab-profiling.yml`, add your model/dataset config, disable WandB:
-```yaml
-use_swanlab: true
-use_wandb: false
-```
-
-### Use Case 2: Analyze DPO Model Outputs
-Use `dpo-swanlab-completions.yml`, adjust completion logging interval based on your training length:
-```yaml
-swanlab_completion_log_interval: 50   # More frequent for short training
-swanlab_completion_log_interval: 200  # Less frequent for long training
-```
-
-### Use Case 3: Optimize Training Performance
-Use `lora-swanlab-profiling.yml`, run multiple experiments with different optimizations:
-- Baseline: `flash_attention: false, gradient_checkpointing: false`
-- Flash Attention: `flash_attention: true`
-- Gradient Checkpointing: `gradient_checkpointing: true`
-- Both: `flash_attention: true, gradient_checkpointing: true`
-
-Compare profiling metrics in SwanLab dashboard.
-
-### Use Case 4: Production RLHF with Team Collaboration
-Use `dpo-swanlab-full-featured.yml`, set up team workspace and Lark notifications:
-```yaml
-swanlab_workspace: ml-team
-swanlab_lark_webhook_url: ...
-swanlab_lark_secret: ...
-```
-
----
-
-## Viewing Your Experiments
-
-### Cloud Mode
-Visit [https://swanlab.cn](https://swanlab.cn) and navigate to your project.
-
-**Dashboard sections**:
-- **Metrics**: Training loss, learning rate, profiling metrics
-- **Tables**: RLHF completions (for DPO/KTO/ORPO/GRPO)
-- **Config**: Hyperparameters and configuration
-- **System**: Resource usage (GPU, memory, CPU)
-- **Files**: Logged artifacts
-
-### Local Mode
-```bash
-swanlab watch ./swanlog
-# Open browser to http://localhost:5092
-```
-
----
-
-## Troubleshooting
-
-### SwanLab not initializing
-```bash
-# Check API key
-echo $SWANLAB_API_KEY
-
-# Verify SwanLab is installed
-pip show swanlab
-
-# Check config
-grep -A 5 "use_swanlab" your-config.yml
-```
-
-### Completions not appearing
-- Verify you're using an RLHF trainer (DPO/KTO/ORPO/GRPO)
-- Check `swanlab_log_completions: true`
-- Wait for `swanlab_completion_log_interval` steps
-- Look for "Registered SwanLab RLHF completion logging" in logs
-
-### Lark notifications not working
-- Test webhook manually: `curl -X POST "$SWANLAB_LARK_WEBHOOK_URL" ...`
-- Verify `SWANLAB_LARK_SECRET` is set correctly
-- Check bot is added to Lark group chat
-- Look for "Registered Lark notification callback" in logs
-
-### Profiling metrics not appearing
-- Verify `use_swanlab: true`
-- Check SwanLab is initialized (look for init log message)
-- Profiling metrics are under "profiling/" namespace
-- Profiling auto-enabled when SwanLab is enabled
-
----
-
-## Performance Notes
-
-### Overhead Comparison
-
-| Feature | Overhead per Step | Memory Usage |
-|---------|------------------|--------------|
-| Basic tracking | < 0.1% | ~10 MB |
-| Completion logging | < 0.5% | ~64 KB (buffer=128) |
-| Profiling | < 0.1% | ~1 KB |
-| **Total** | **< 0.7%** | **~10 MB** |
-
-### Best Practices
-1. Use ONE logging tool in production (disable WandB/MLflow when using SwanLab)
-2. Adjust completion log interval based on training length (100-200 steps)
-3. Keep completion buffer size reasonable (128-512)
-4. Profile critical path methods first (training_step, compute_loss)
-5. Use ProfilingConfig to throttle high-frequency operations
-
----
-
-## Further Reading
-
-- **Full Documentation**: [src/axolotl/integrations/swanlab/README.md](../../src/axolotl/integrations/swanlab/README.md)
-- **SwanLab Docs**: [https://docs.swanlab.cn](https://docs.swanlab.cn)
-- **Axolotl Docs**: [https://axolotl-ai-cloud.github.io/axolotl/](https://axolotl-ai-cloud.github.io/axolotl/)
-- **DPO Paper**: [Direct Preference Optimization](https://arxiv.org/abs/2305.18290)
-
----
-
-## Contributing
-
-Found an issue or have an improvement? Please submit a PR or open an issue:
-- [Axolotl Issues](https://github.com/axolotl-ai-cloud/axolotl/issues)
-- [SwanLab Issues](https://github.com/SwanHubX/SwanLab/issues)

examples/swanlab/custom_trainer_profiling.py

@@ -1,299 +0,0 @@
-"""Example: Custom Trainer with SwanLab Profiling
-
-This example demonstrates how to add SwanLab profiling to your custom trainer.
-
-Features:
-- @swanlab_profile decorator for automatic profiling
-- swanlab_profiling_context for fine-grained profiling
-- ProfilingConfig for advanced filtering and throttling
-
-Usage:
-    1. Create your custom trainer extending AxolotlTrainer
-    2. Add @swanlab_profile decorators to methods you want to profile
-    3. Use swanlab_profiling_context for fine-grained profiling within methods
-    4. Enable SwanLab in your config (use_swanlab: true)
-
-See also:
-    - examples/swanlab/lora-swanlab-profiling.yml for config
-    - src/axolotl/integrations/swanlab/profiling.py for implementation
-"""
-
-from axolotl.core.trainers.base import AxolotlTrainer
-from axolotl.integrations.swanlab.profiling import (
-    ProfilingConfig,
-    swanlab_profile,
-    swanlab_profiling_context,
-    swanlab_profiling_context_advanced,
-)
-
-
-class CustomTrainerWithProfiling(AxolotlTrainer):
-    """Custom trainer with SwanLab profiling enabled.
-
-    This trainer demonstrates three profiling patterns:
-    1. Decorator-based profiling (@swanlab_profile)
-    2. Context manager profiling (swanlab_profiling_context)
-    3. Advanced profiling with filtering (ProfilingConfig)
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Create custom profiling config for high-frequency operations
-        self.fast_op_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.5,  # Only log if duration > 0.5ms
-            log_interval=50,  # Log every 50th call
-        )
-
-    # ========================================================================
-    # Pattern 1: Decorator-based Profiling
-    # ========================================================================
-    # Best for: Methods you always want to profile
-    # Overhead: ~2-5 microseconds per call (negligible)
-
-    @swanlab_profile
-    def training_step(self, model, inputs):
-        """Main training step - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.training_step
-        """
-        return super().training_step(model, inputs)
-
-    @swanlab_profile
-    def compute_loss(self, model, inputs, return_outputs=False):
-        """Loss computation - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.compute_loss
-        """
-        return super().compute_loss(model, inputs, return_outputs)
-
-    @swanlab_profile
-    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
-        """Prediction step - always profile.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prediction_step
-        """
-        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)
-
-    # ========================================================================
-    # Pattern 2: Fine-grained Context Manager Profiling
-    # ========================================================================
-    # Best for: Profiling specific code blocks within a method
-    # Use case: When you want to profile forward vs backward separately
-
-    def complex_training_step(self, model, inputs):
-        """Training step with fine-grained profiling.
-
-        Profiling metrics:
-        - profiling/Time taken: CustomTrainerWithProfiling.forward_pass
-        - profiling/Time taken: CustomTrainerWithProfiling.backward_pass
-        - profiling/Time taken: CustomTrainerWithProfiling.optimizer_step
-        """
-        # Profile just the forward pass
-        with swanlab_profiling_context(self, "forward_pass"):
-            outputs = model(**inputs)
-            loss = outputs.loss
-
-        # Profile just the backward pass
-        with swanlab_profiling_context(self, "backward_pass"):
-            loss.backward()
-
-        # Profile optimizer step
-        with swanlab_profiling_context(self, "optimizer_step"):
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-
-        return outputs
-
-    # ========================================================================
-    # Pattern 3: Advanced Profiling with Filtering
-    # ========================================================================
-    # Best for: High-frequency operations where you want to throttle logging
-    # Use case: Methods called 100+ times per step
-
-    def _prepare_inputs(self, inputs):
-        """Prepare inputs - throttled profiling.
-
-        This method is called frequently (once per batch), so we throttle
-        profiling to reduce overhead:
-        - Only log if duration > 0.5ms (skip very fast operations)
-        - Only log every 50th call (reduce logging frequency)
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prepare_inputs
-        """
-        with swanlab_profiling_context_advanced(
-            self, "prepare_inputs", config=self.fast_op_config
-        ):
-            return super()._prepare_inputs(inputs)
-
-    def _prepare_input_for_model(self, input_ids):
-        """Another high-frequency operation - throttled profiling.
-
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.prepare_input_for_model
-        """
-        with swanlab_profiling_context_advanced(
-            self, "prepare_input_for_model", config=self.fast_op_config
-        ):
-            # Your custom input preparation logic
-            return input_ids
-
-    # ========================================================================
-    # Pattern 4: Exception-safe Profiling
-    # ========================================================================
-    # Profiling is exception-safe: duration is logged even if method raises
-
-    @swanlab_profile
-    def potentially_failing_method(self):
-        """This method may raise an exception.
-
-        SwanLab profiling will still log the duration before re-raising.
-        Profiling metric: profiling/Time taken: CustomTrainerWithProfiling.potentially_failing_method
-        """
-        # Do some work
-        result = self._do_risky_computation()
-
-        # If this raises, profiling duration is still logged
-        if result < 0:
-            raise ValueError("Invalid result")
-
-        return result
-
-    def _do_risky_computation(self):
-        """Placeholder for risky computation."""
-        return 42
-
-
-# ============================================================================
-# Advanced Example: Custom ProfilingConfig Per Method
-# ============================================================================
-
-
-class AdvancedProfilingTrainer(AxolotlTrainer):
-    """Trainer with method-specific profiling configurations."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Different profiling configs for different method types
-        self.critical_path_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.0,  # Log everything on critical path
-            log_interval=1,  # Log every call
-        )
-
-        self.fast_path_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=1.0,  # Only log if > 1ms
-            log_interval=100,  # Log every 100th call
-        )
-
-        self.debug_config = ProfilingConfig(
-            enabled=True,
-            min_duration_ms=0.0,  # Log everything
-            log_interval=1,  # Log every call
-        )
-
-    def training_step(self, model, inputs):
-        """Critical path - log everything."""
-        with swanlab_profiling_context_advanced(
-            self, "training_step", config=self.critical_path_config
-        ):
-            return super().training_step(model, inputs)
-
-    def _prepare_inputs(self, inputs):
-        """Fast path - throttle logging."""
-        with swanlab_profiling_context_advanced(
-            self, "prepare_inputs", config=self.fast_path_config
-        ):
-            return super()._prepare_inputs(inputs)
-
-    def _debug_method(self, data):
-        """Debug-only method - verbose logging."""
-        with swanlab_profiling_context_advanced(
-            self, "debug_method", config=self.debug_config
-        ):
-            # Your debug logic
-            pass
-
-
-# ============================================================================
-# How to Use This Custom Trainer
-# ============================================================================
-
-"""
-To use this custom trainer:
-
-1. Save this file to your project (e.g., my_custom_trainer.py)
-
-2. Create a config file that uses your custom trainer:
-
-    # config.yml
-    base_model: NousResearch/Llama-3.2-1B
-
-    # ... other config ...
-
-    plugins:
-      - axolotl.integrations.swanlab.SwanLabPlugin
-
-    use_swanlab: true
-    swanlab_project: my-profiling-experiment
-
-    # Optional: Specify custom trainer
-    # (Or modify axolotl to use your custom trainer class)
-
-3. Run training:
-
-    export SWANLAB_API_KEY=your-api-key
-    accelerate launch -m axolotl.cli.train config.yml
-
-4. View profiling metrics in SwanLab dashboard:
-   - profiling/Time taken: CustomTrainerWithProfiling.training_step
-   - profiling/Time taken: CustomTrainerWithProfiling.forward_pass
-   - profiling/Time taken: CustomTrainerWithProfiling.backward_pass
-   - etc.
-
-5. Compare profiling metrics across runs:
-   - Run baseline without optimizations
-   - Run with flash_attention enabled
-   - Run with gradient_checkpointing enabled
-   - Compare profiling metrics to see performance impact
-"""
-
-# ============================================================================
-# Tips for Effective Profiling
-# ============================================================================
-
-"""
-1. Profile the critical path first:
-   - training_step, compute_loss, prediction_step
-   - These methods are called most frequently and have biggest impact
-
-2. Use throttling for high-frequency operations:
-   - Methods called 100+ times per step
-   - Use log_interval=50 or log_interval=100
-   - Reduces profiling overhead and dashboard clutter
-
-3. Filter noise with min_duration_ms:
-   - Set min_duration_ms=1.0 to skip very fast operations
-   - Focus on operations that actually take time
-
-4. Compare across runs:
-   - Run same config multiple times to check consistency
-   - Compare different optimization strategies
-   - Track profiling trends over time
-
-5. Monitor distributed training:
-   - Check for per-rank timing differences
-   - Look for stragglers (slower ranks)
-   - Identify synchronization bottlenecks
-
-6. Disable profiling in production:
-   - from axolotl.integrations.swanlab.profiling import DEFAULT_PROFILING_CONFIG
-   - DEFAULT_PROFILING_CONFIG.enabled = False
-
-7. Exception handling:
-   - Profiling is exception-safe
-   - Duration logged even if method raises
-   - Useful for debugging methods that fail intermittently
-"""

examples/swanlab/dpo-swanlab-completions.yml

@@ -1,168 +0,0 @@
-# SwanLab DPO Training Example with Completion Logging
-#
-# This example demonstrates DPO (Direct Preference Optimization) training
-# with SwanLab integration for experiment tracking and completion table logging.
-#
-# Features enabled:
-# - SwanLab experiment tracking
-# - RLHF completion table logging (prompts, chosen/rejected responses, rewards)
-# - Lark (Feishu) team notifications (optional)
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-completions.yml
-
-# Model Configuration
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>
-
-# Quantization
-load_in_8bit: true
-load_in_4bit: false
-
-# LoRA Configuration
-adapter: lora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-# DPO Configuration
-chat_template: llama3
-rl: dpo
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-# Dataset and Output
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-swanlab-out
-
-# Training Configuration
-sequence_len: 4096
-sample_packing: false
-micro_batch_size: 2
-gradient_accumulation_steps: 4
-num_epochs: 4
-
-# Optimization
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# Precision
-bf16: auto
-tf32: false
-
-# Performance
-gradient_checkpointing: true
-flash_attention: true
-
-# Checkpointing and Logging
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# ============================================================================
-# SwanLab Integration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# Basic SwanLab Configuration
-use_swanlab: true
-swanlab_project: dpo-training
-swanlab_experiment_name: llama-3-dpo-completions-demo
-swanlab_description: "DPO training with completion table logging"
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# SwanLab Authentication
-# Recommended: Set via environment variable
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# Optional: Team workspace
-# swanlab_workspace: my-research-team
-
-# ============================================================================
-# RLHF Completion Table Logging
-# ============================================================================
-#
-# Automatically logs model completions to SwanLab for qualitative analysis:
-# - Prompts from your DPO dataset
-# - Chosen responses (preferred)
-# - Rejected responses (non-preferred)
-# - Reward differences
-#
-# View the table in SwanLab dashboard under "rlhf_completions"
-
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 training steps
-swanlab_completion_max_buffer: 128    # Keep last 128 completions in memory
-
-# Memory Usage Notes:
-# - Buffer size 128: ~64 KB (default, recommended)
-# - Buffer size 512: ~256 KB (for more historical completions)
-# - Buffer size 1024: ~512 KB (maximum for very long training runs)
-
-# Performance Notes:
-# - Completion logging overhead: < 0.5% per training step
-# - Only logs every N steps to minimize impact
-# - Memory-bounded buffer prevents memory leaks
-
-# ============================================================================
-# Optional: Lark (Feishu) Team Notifications
-# ============================================================================
-#
-# Get real-time training notifications in your team chat
-# Uncomment to enable:
-
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret  # Recommended for production
-
-# Notifications sent for:
-# - Training start
-# - Training completion
-# - Training errors
-# - Metric milestones (if configured)
-
-# ============================================================================
-# Optional: Private SwanLab Deployment
-# ============================================================================
-#
-# For enterprise users with private SwanLab deployment:
-
-# swanlab_web_host: https://swanlab.yourcompany.com
-# swanlab_api_host: https://api.swanlab.yourcompany.com
-
-# ============================================================================
-# Disable WandB if you're migrating from it
-# ============================================================================
-
-# wandb_project:
-# wandb_entity:
-# use_wandb: false

examples/swanlab/dpo-swanlab-full-featured.yml

@@ -1,329 +0,0 @@
-# SwanLab Full-Featured DPO Training Example
-#
-# This example demonstrates ALL SwanLab integration features:
-# - Experiment tracking with cloud sync
-# - RLHF completion table logging
-# - Performance profiling
-# - Lark (Feishu) team notifications
-# - Team workspace collaboration
-#
-# Use this as a reference for production RLHF training setups.
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-#   export SWANLAB_LARK_SECRET=your-webhook-secret
-#   accelerate launch -m axolotl.cli.train examples/swanlab/dpo-swanlab-full-featured.yml
-
-# ============================================================================
-# Model Configuration
-# ============================================================================
-
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-model_type: LlamaForCausalLM
-tokenizer_type: AutoTokenizer
-
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot_id|>
-
-# Quantization for efficient training
-load_in_8bit: true
-load_in_4bit: false
-
-# ============================================================================
-# LoRA Configuration
-# ============================================================================
-
-adapter: lora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true  # Target all linear layers
-
-# ============================================================================
-# DPO (Direct Preference Optimization) Configuration
-# ============================================================================
-
-chat_template: llama3
-rl: dpo  # Enable DPO trainer
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_dpo_test
-    type: chat_template.default
-    field_messages: conversation
-    field_chosen: chosen
-    field_rejected: rejected
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      system:
-        - system
-      user:
-        - user
-      assistant:
-        - assistant
-
-# ============================================================================
-# Dataset and Output Configuration
-# ============================================================================
-
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./outputs/dpo-swanlab-full-featured-out
-
-# ============================================================================
-# Training Configuration
-# ============================================================================
-
-sequence_len: 4096
-sample_packing: false
-
-micro_batch_size: 2
-gradient_accumulation_steps: 4
-num_epochs: 4
-
-# ============================================================================
-# Optimization
-# ============================================================================
-
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# ============================================================================
-# Precision and Performance
-# ============================================================================
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-flash_attention: true
-
-# ============================================================================
-# Checkpointing and Logging
-# ============================================================================
-
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# ============================================================================
-# SwanLab Integration - Full Configuration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# ------------------------------------------------------------------------------
-# Basic SwanLab Configuration
-# ------------------------------------------------------------------------------
-
-use_swanlab: true
-swanlab_project: dpo-production
-swanlab_experiment_name: llama-3-dpo-full-featured-v1
-swanlab_description: |
-  Production DPO training with all SwanLab features enabled:
-  - Completion table logging for qualitative analysis
-  - Performance profiling for optimization
-  - Lark notifications for team collaboration
-
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# ------------------------------------------------------------------------------
-# Team Collaboration
-# ------------------------------------------------------------------------------
-
-# Workspace for team collaboration (shared experiments)
-swanlab_workspace: ml-research-team
-
-# Authentication (recommended: use environment variable)
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# ------------------------------------------------------------------------------
-# RLHF Completion Table Logging
-# ------------------------------------------------------------------------------
-# Automatically logs model completions for qualitative analysis:
-# - Prompts from your DPO dataset
-# - Chosen responses (preferred)
-# - Rejected responses (non-preferred)
-# - Reward differences
-#
-# View in SwanLab dashboard under "rlhf_completions" table
-
-swanlab_log_completions: true
-swanlab_completion_log_interval: 100  # Log every 100 steps
-swanlab_completion_max_buffer: 256    # Larger buffer for long training runs
-
-# Buffer size recommendations:
-# - 128: Default, ~64 KB memory (recommended for most cases)
-# - 256: ~128 KB memory (this config, good for longer training)
-# - 512: ~256 KB memory (maximum for very long runs)
-
-# ------------------------------------------------------------------------------
-# Lark (Feishu) Team Notifications
-# ------------------------------------------------------------------------------
-# Get real-time training notifications in your team chat
-#
-# Notifications sent for:
-# - Training start
-# - Training completion
-# - Training errors
-# - Metric milestones (if configured)
-
-# Recommended: Set via environment variables
-#   export SWANLAB_LARK_WEBHOOK_URL=https://open.feishu.cn/...
-#   export SWANLAB_LARK_SECRET=your-webhook-secret
-
-# Or set in config (less secure):
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret  # REQUIRED for production
-
-# Security note: ALWAYS use swanlab_lark_secret in production to prevent
-# unauthorized parties from sending fake notifications to your team chat.
-
-# ------------------------------------------------------------------------------
-# Performance Profiling
-# ------------------------------------------------------------------------------
-# Profiling is automatically enabled when SwanLab is enabled.
-# Metrics logged to SwanLab under "profiling/" namespace:
-#   profiling/Time taken: AxolotlTrainer.training_step
-#   profiling/Time taken: AxolotlTrainer.compute_loss
-#   profiling/Time taken: AxolotlTrainer.prediction_step
-#
-# Use these metrics to:
-# - Identify bottlenecks in training loop
-# - Compare performance across different configurations
-# - Monitor performance regressions over time
-# - Debug unexpected slowdowns
-
-# For custom profiling in your own trainer, see:
-#   examples/swanlab/custom_trainer_profiling.py
-
-# ------------------------------------------------------------------------------
-# Optional: Private SwanLab Deployment
-# ------------------------------------------------------------------------------
-# For enterprise users with private SwanLab deployment:
-
-# swanlab_web_host: https://swanlab.yourcompany.com
-# swanlab_api_host: https://api.swanlab.yourcompany.com
-
-# ------------------------------------------------------------------------------
-# Optional: Model Checkpointing to SwanLab
-# ------------------------------------------------------------------------------
-# Log model checkpoints to SwanLab (coming soon)
-
-swanlab_log_model: false
-
-# ============================================================================
-# Disable Other Logging Tools (Recommended)
-# ============================================================================
-# Using multiple logging tools simultaneously can impact performance:
-# - Expected overhead: ~1-2% per logger
-# - Potential config/callback conflicts
-#
-# For production training, use ONLY SwanLab:
-
-# wandb_project:
-# use_wandb: false
-#
-# use_mlflow: false
-#
-# use_comet: false
-
-# ============================================================================
-# Expected Training Behavior
-# ============================================================================
-
-# With this configuration, you should see:
-#
-# 1. SwanLab Initialization (rank 0 only):
-#    INFO: SwanLab initialized for project: dpo-production
-#    INFO: SwanLab experiment: llama-3-dpo-full-featured-v1
-#    INFO: SwanLab mode: cloud
-#    INFO: SwanLab workspace: ml-research-team
-#
-# 2. Completion Logging (rank 0 only):
-#    INFO: Registered SwanLab RLHF completion logging callback for DPOTrainer
-#          (log_interval=100, max_buffer=256)
-#
-# 3. Lark Notifications (rank 0 only):
-#    INFO: Registered Lark notification callback with HMAC authentication
-#
-# 4. Distributed Training Detection (if multi-GPU):
-#    INFO: Distributed training detected (world_size=N)
-#    INFO: Only rank 0 will initialize SwanLab
-#    INFO: Other ranks will skip SwanLab to avoid conflicts
-#
-# 5. Training Start Notification (Lark):
-#    Your team chat receives: "Training started: llama-3-dpo-full-featured-v1"
-#
-# 6. Periodic Completion Logging:
-#    Every 100 steps, completion table is updated in SwanLab dashboard
-#
-# 7. Training Complete Notification (Lark):
-#    Your team chat receives: "Training completed: llama-3-dpo-full-featured-v1"
-#    With link to SwanLab dashboard and final metrics
-#
-# 8. SwanLab Dashboard Shows:
-#    - Training metrics (loss, learning rate, etc.)
-#    - Completion table (rlhf_completions)
-#    - Profiling metrics (profiling/Time taken: ...)
-#    - Hyperparameters and configuration
-#    - System resource usage
-
-# ============================================================================
-# Production Checklist
-# ============================================================================
-
-# Before deploying to production, verify:
-# ✅ SwanLab API key is set via environment variable (not in config)
-# ✅ Lark webhook secret is set (required for HMAC authentication)
-# ✅ Workspace is set to your team's workspace
-# ✅ Experiment name is descriptive and unique
-# ✅ Only SwanLab is enabled (other loggers disabled)
-# ✅ Completion logging buffer size is appropriate for your training duration
-# ✅ Private deployment hosts are set (if using enterprise SwanLab)
-# ✅ Test run completes successfully and shows up in SwanLab dashboard
-# ✅ Lark notifications are received in team chat
-# ✅ Profiling metrics are logged correctly
-
-# ============================================================================
-# Troubleshooting
-# ============================================================================
-
-# If SwanLab initialization fails:
-# 1. Check SWANLAB_API_KEY environment variable is set
-# 2. Verify swanlab_project is set in config
-# 3. Check swanlab_mode is valid (cloud/local/offline/disabled)
-# 4. Verify internet connectivity (for cloud mode)
-
-# If Lark notifications not received:
-# 1. Check SWANLAB_LARK_WEBHOOK_URL is set correctly
-# 2. Verify SWANLAB_LARK_SECRET matches your Lark bot settings
-# 3. Test webhook manually: curl -X POST "$SWANLAB_LARK_WEBHOOK_URL" ...
-# 4. Check training logs for "Registered Lark notification callback"
-# 5. Verify bot is added to the target Lark group chat
-
-# If completions not appearing in SwanLab:
-# 1. Verify you're using an RLHF trainer (DPO/KTO/ORPO/GRPO)
-# 2. Check swanlab_log_completions is true
-# 3. Wait for log_interval steps (default: 100)
-# 4. Check training logs for "Registered SwanLab RLHF completion logging"
-
-# If profiling metrics not appearing:
-# 1. Verify use_swanlab is true
-# 2. Check SwanLab is initialized (check logs)
-# 3. Look under "profiling/" namespace in dashboard
-# 4. Profiling may be disabled if DEFAULT_PROFILING_CONFIG.enabled = False
-
-# For more help:
-# - SwanLab docs: https://docs.swanlab.cn
-# - Axolotl SwanLab integration: src/axolotl/integrations/swanlab/README.md
-# - GitHub issues: https://github.com/axolotl-ai-cloud/axolotl/issues

examples/swanlab/lora-swanlab-profiling.yml

@@ -1,178 +0,0 @@
-# SwanLab LoRA Training Example with Performance Profiling
-#
-# This example demonstrates standard LoRA fine-tuning with SwanLab integration
-# for performance profiling and optimization.
-#
-# Features enabled:
-# - SwanLab experiment tracking
-# - Performance profiling (training step, forward/backward pass timing)
-# - Real-time metrics visualization
-#
-# To run:
-#   export SWANLAB_API_KEY=your-api-key
-#   accelerate launch -m axolotl.cli.train examples/swanlab/lora-swanlab-profiling.yml
-
-# Model Configuration
-base_model: NousResearch/Llama-3.2-1B
-
-# Dataset Configuration
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-
-val_set_size: 0.1
-output_dir: ./outputs/lora-swanlab-profiling-out
-
-# LoRA Configuration
-adapter: lora
-lora_r: 16
-lora_alpha: 32
-lora_dropout: 0.05
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-# Training Configuration
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: true
-
-micro_batch_size: 2
-gradient_accumulation_steps: 2
-num_epochs: 1
-
-# Optimization
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-warmup_ratio: 0.1
-weight_decay: 0.0
-
-# Precision
-bf16: auto
-tf32: false
-
-# Performance
-gradient_checkpointing: true
-flash_attention: true
-
-# Checkpointing and Logging
-logging_steps: 1
-evals_per_epoch: 4
-saves_per_epoch: 1
-
-# Loss Monitoring
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-# ============================================================================
-# SwanLab Integration
-# ============================================================================
-
-plugins:
-  - axolotl.integrations.swanlab.SwanLabPlugin
-
-# Basic SwanLab Configuration
-use_swanlab: true
-swanlab_project: lora-profiling
-swanlab_experiment_name: llama-3.2-1b-profiling-demo
-swanlab_description: "LoRA fine-tuning with performance profiling"
-swanlab_mode: cloud  # Options: cloud, local, offline, disabled
-
-# SwanLab Authentication
-# Recommended: Set via environment variable
-#   export SWANLAB_API_KEY=your-api-key
-# Or set in config (less secure):
-# swanlab_api_key: your-api-key
-
-# Optional: Team workspace
-# swanlab_workspace: my-ml-team
-
-# ============================================================================
-# Performance Profiling
-# ============================================================================
-#
-# SwanLab automatically profiles trainer methods when enabled.
-# Profiling metrics appear in SwanLab dashboard under "profiling/" namespace.
-#
-# Built-in profiling:
-# - Minimal overhead (< 0.1% per step)
-# - High-precision timing (microsecond accuracy)
-# - Exception-safe (logs duration even if method fails)
-#
-# View profiling metrics in SwanLab dashboard:
-#   profiling/Time taken: AxolotlTrainer.training_step
-#   profiling/Time taken: AxolotlTrainer.compute_loss
-#   profiling/Time taken: AxolotlTrainer.prediction_step
-#
-# For custom profiling in your own trainer, see:
-#   examples/swanlab/custom_trainer_profiling.py
-
-# Completion logging is disabled for non-RLHF trainers
-swanlab_log_completions: false  # Only works with DPO/KTO/ORPO/GRPO
-
-# ============================================================================
-# Optional: Compare with Multiple Runs
-# ============================================================================
-#
-# To compare profiling metrics across different configurations:
-#
-# 1. Run baseline without flash attention:
-#    swanlab_experiment_name: llama-3.2-1b-no-flash-attn
-#    flash_attention: false
-#
-# 2. Run with gradient checkpointing:
-#    swanlab_experiment_name: llama-3.2-1b-grad-checkpoint
-#    gradient_checkpointing: true
-#
-# 3. Run with both:
-#    swanlab_experiment_name: llama-3.2-1b-optimized
-#    flash_attention: true
-#    gradient_checkpointing: true
-#
-# Then compare profiling metrics in SwanLab dashboard to see performance impact
-
-# ============================================================================
-# Optional: Lark (Feishu) Team Notifications
-# ============================================================================
-#
-# Get notified when profiling experiments complete:
-
-# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
-# swanlab_lark_secret: your-webhook-secret
-
-# ============================================================================
-# Profiling Best Practices
-# ============================================================================
-#
-# 1. Run multiple epochs to see profiling trends over time
-# 2. Ignore first ~10 steps (warmup period, slower)
-# 3. Look for outliers (steps that take significantly longer)
-# 4. Compare profiling metrics before/after optimization changes
-# 5. Monitor per-rank profiling in distributed training
-#
-# Common bottlenecks to profile:
-# - training_step: Overall step time (should be consistent)
-# - compute_loss: Loss computation (scales with sequence length)
-# - prediction_step: Evaluation time (can be slow for large val sets)
-#
-# If you see inconsistent timing:
-# - Check for data loading bottlenecks
-# - Monitor GPU utilization (may be CPU-bound)
-# - Check for gradient accumulation effects
-# - Verify CUDA kernel synchronization
-
-# ============================================================================
-# Disable WandB if you're migrating from it
-# ============================================================================
-
-# wandb_project:
-# use_wandb: false

examples/voxtral/README.md

@@ -12,7 +12,7 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ```bash
 # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==26.0"]
+requires = ["setuptools>=64", "wheel", "setuptools_scm>=8", "packaging==23.2"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -24,9 +24,6 @@ Repository = "https://github.com/axolotl-ai-cloud/axolotl.git"
 py-modules = ["setuptools_axolotl_dynamic_dependencies"]
 include-package-data = true
 
-[tool.setuptools.dynamic]
-version = { file = "VERSION" }
-
 [tool.setuptools.cmdclass]
 build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"
 
@@ -60,6 +57,3 @@ indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
 docstring-code-format = false
-
-[tool.uv.extra-build-dependencies]
-axolotl = ["huggingface_hub"]

requirements.txt

@@ -1,25 +1,25 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.49.1
-triton>=3.4.0
+bitsandbytes==0.48.2
+triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.7.0
+liger-kernel==0.6.4
 # END section
 
-packaging==26.0
-huggingface_hub>=1.1.7
-peft>=0.18.1
+packaging==23.2
+
+huggingface_hub>=0.36.0
+peft>=0.18.0
 tokenizers>=0.22.1
-transformers @ git+https://github.com/winglian/transformers.git@refactor-inner-training-loop-reorder-only
+transformers==4.57.1
 accelerate==1.12.0
-datasets==4.5.0
+datasets==4.4.2
 deepspeed>=0.18.3
-trl==0.28.0
+trl==0.25.1
 hf_xet==1.2.0
 kernels==0.11.5
-
 trackio>=0.13.0
 typing-extensions>=4.15.0
 
@@ -63,7 +63,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.16.0
+torchao==0.15.0
 openenv-core==0.1.0
 schedulefree==1.4.1
 
@@ -72,4 +72,4 @@ axolotl-contribs-mit==0.0.6
 # telemetry
 posthog==6.7.11
 
-mistral-common==1.8.8
+mistral-common==1.8.6

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@0d4ce4b"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"'
 )

setup.py

@@ -1,5 +1,6 @@
 """setup.py for axolotl"""
 
+import ast
 import os
 import platform
 import re
@@ -25,7 +26,6 @@ def parse_requirements(extras_require_map):
                 _install_requires.append(line)
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
-        install_xformers = platform.machine() != "aarch64"
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [
@@ -62,68 +62,44 @@ def parse_requirements(extras_require_map):
             else:
                 raise ValueError("Invalid version format")
 
-            torch_parts = torch_version.split("+")
-            if len(torch_parts) == 2:
-                torch_cuda_version = torch_parts[1]
-                _dependency_links.append(
-                    f"https://download.pytorch.org/whl/{torch_cuda_version}"
-                )
-
             if (major, minor) >= (2, 9):
                 extras_require_map.pop("fbgemm-gpu")
-                extras_require_map["fbgemm-gpu"] = [
-                    "fbgemm-gpu==1.4.0",
-                    "fbgemm-gpu-genai==1.4.2",
-                ]
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
                 extras_require_map["vllm"] = ["vllm==0.11.1"]
-                if not install_xformers:
-                    _install_requires.pop(_install_requires.index(xformers_version))
-                extras_require_map["vllm"] = ["vllm==0.13.0"]
-                if patch == 0:
-                    extras_require_map["vllm"] = ["vllm==0.13.0"]
-                else:
-                    extras_require_map["vllm"] = ["vllm==0.14.0"]
             elif (major, minor) >= (2, 8):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.3.0"]
                 extras_require_map["vllm"] = ["vllm==0.11.0"]
-                if not install_xformers:
-                    _install_requires.pop(_install_requires.index(xformers_version))
             elif (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
-                    if install_xformers:
-                        _install_requires.append("xformers==0.0.30")
+                    _install_requires.append("xformers==0.0.30")
                     # vllm 0.9.x is incompatible with latest transformers
                     extras_require_map.pop("vllm")
                 else:
-                    if install_xformers:
-                        _install_requires.append("xformers==0.0.31")
+                    _install_requires.append("xformers==0.0.31")
                     extras_require_map["vllm"] = ["vllm==0.10.1"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if install_xformers:
-                    _install_requires.append("xformers==0.0.29.post3")
+                _install_requires.append("xformers==0.0.29.post3")
                 # since we only support 2.6.0+cu126
                 _dependency_links.append("https://download.pytorch.org/whl/cu126")
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
-                if install_xformers:
-                    if patch == 0:
-                        _install_requires.append("xformers==0.0.28.post2")
-                    else:
-                        _install_requires.append("xformers>=0.0.28.post3")
+                if patch == 0:
+                    _install_requires.append("xformers==0.0.28.post2")
+                else:
+                    _install_requires.append("xformers>=0.0.28.post3")
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 4):
                 extras_require_map.pop("vllm")
-                if install_xformers:
-                    if patch == 0:
-                        _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.append("xformers>=0.0.27")
-                    else:
-                        _install_requires.pop(_install_requires.index(xformers_version))
-                        _install_requires.append("xformers==0.0.28.post1")
+                if patch == 0:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers>=0.0.27")
+                else:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                    _install_requires.append("xformers==0.0.28.post1")
             else:
                 raise ValueError("axolotl requires torch>=2.4")
 
@@ -134,11 +110,15 @@ def parse_requirements(extras_require_map):
 
 def get_package_version():
     with open(
-        Path(os.path.dirname(os.path.abspath(__file__))) / "VERSION",
+        Path(os.path.dirname(os.path.abspath(__file__)))
+        / "src"
+        / "axolotl"
+        / "__init__.py",
         "r",
         encoding="utf-8",
     ) as fin:
-        version_ = fin.read().strip()
+        version_match = re.search(r"^__version__\s*=\s*(.*)$", fin.read(), re.MULTILINE)
+    version_ = ast.literal_eval(version_match.group(1))
     return version_
 
 

src/axolotl/__init__.py

@@ -1,11 +1,7 @@
 """Axolotl - Train and fine-tune large language models"""
 
 import pkgutil
-from importlib.metadata import PackageNotFoundError, version
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-try:
-    __version__ = version("axolotl")
-except PackageNotFoundError:
-    __version__ = "unknown"
+__version__ = "0.13.0.dev"

src/axolotl/cli/__init__.py

@@ -5,6 +5,6 @@ import os
 from axolotl.logging_config import configure_logging
 
 os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
-os.environ.setdefault("HF_XET_HIGH_PERFORMANCE", "1")
+os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
 
 configure_logging()

src/axolotl/cli/checks.py

@@ -44,7 +44,7 @@ def check_user_token() -> bool:
         return bool(user_info)
     except LocalTokenNotFoundError:
         LOG.warning(
-            "Error verifying HuggingFace token. Remember to log in using `hf auth login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
+            "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
     except HTTPError:

src/axolotl/cli/merge_lora.py

@@ -24,6 +24,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
     """
     model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    safe_serialization = cfg.save_safetensors is True
 
     LOG.info("Running merge of LoRA with base model...")
     model = model.merge_and_unload(progressbar=True)
@@ -41,6 +42,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         LOG.info(f"Saving merged model to: {str(Path(cfg.output_dir) / 'merged')}...")
         model.save_pretrained(
             str(Path(cfg.output_dir) / "merged"),
+            safe_serialization=safe_serialization,
             progressbar=True,
         )
         tokenizer.save_pretrained(

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -14,6 +14,8 @@ from accelerate import PartialState
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
     is_torch_version,
 )
 from huggingface_hub import split_torch_state_dict_into_shards
@@ -38,15 +40,17 @@ class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
 def _distributed_checkpoint_to_merged_weights(
     checkpoint_dir: Union[str, Path],
     save_path: str,
+    safe_serialization: bool = False,
     max_shard_size: str = "5GB",
 ) -> Path:
     """
     Passthrough to `torch.distributed.checkpoint.format_utils.dcp_to_torch_save`. Will
-    save under `save_path` as `model.safetensors`.
+    save under `save_path` as either `model.safetensors` or `pytorch_model.bin`.
 
     Args:
         checkpoint_dir: Directory where distributed checkpoint is saved.
         save_path: Path to save model to.
+        safe_serialization: Whether to save in safetensors format.
         max_shard_size: Max size of model shards to save.
 
     Returns:
@@ -72,7 +76,11 @@ def _distributed_checkpoint_to_merged_weights(
         if isinstance(value, torch.Tensor) and value.dtype != torch.bfloat16:
             state_dict[key] = value.to(torch.bfloat16)
 
-    filename_pattern = SAFE_WEIGHTS_NAME.replace(".safetensors", "{suffix}.safetensors")
+    weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+        ".safetensors", "{suffix}.safetensors"
+    )
     state_dict_split = split_torch_state_dict_into_shards(
         state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
     )
@@ -90,12 +98,19 @@ def _distributed_checkpoint_to_merged_weights(
 
     for shard_file, tensors in filename_to_tensors:
         shard = {tensor: state_dict[tensor] for tensor in tensors}
-        safe_save_file(
-            shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
-        )
+
+        if safe_serialization:
+            safe_save_file(
+                shard, os.path.join(save_path_, shard_file), metadata={"format": "pt"}
+            )
+        else:
+            torch.save(shard, os.path.join(save_path_, shard_file))
 
     if index is not None:
-        save_index_file = os.path.join(save_path_, SAFE_WEIGHTS_INDEX_NAME)
+        save_index_file = (
+            SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+        )
+        save_index_file = os.path.join(save_path_, save_index_file)
         # Save the index as well
         with open(save_index_file, "w", encoding="utf-8") as fout:
             content = json.dumps(index, indent=2, sort_keys=True) + "\n"
@@ -108,11 +123,13 @@ def _distributed_checkpoint_to_merged_weights(
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,
+    safe_serialization: bool = False,
     remove_checkpoint_dir: bool = False,
 ):
     """
     Merge the weights from sharded FSDP model checkpoints into a single combined checkpoint. Should be used if
-    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors`.
+    `SHARDED_STATE_DICT` was used for the model. Weights will be saved to `{output_path}/model.safetensors` if
+    `safe_serialization` else `pytorch_model.bin`.
 
     Note: this is a CPU-bound process.
 
@@ -121,6 +138,8 @@ def merge_fsdp_weights(
             The directory containing the FSDP checkpoints (can be either the model or optimizer).
         output_path (`str`):
             The path to save the merged checkpoint.
+        safe_serialization (`bool`, *optional*, defaults to `True`):
+            Whether to save the merged weights with safetensors (recommended).
         remove_checkpoint_dir (`bool`, *optional*, defaults to `False`):
             Whether to remove the checkpoint directory after merging.
 
@@ -158,7 +177,7 @@ def merge_fsdp_weights(
     if state.is_main_process:
         LOG.info(f"Merging FSDP weights from {checkpoint_dir_}")
         save_path = _distributed_checkpoint_to_merged_weights(
-            checkpoint_dir_, output_path
+            checkpoint_dir_, output_path, safe_serialization
         )
         LOG.info(f"Successfully merged FSDP weights and saved to {save_path}")
         if remove_checkpoint_dir:
@@ -191,6 +210,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
     merge_fsdp_weights(
         checkpoint_dir=str(fsdp_dir),
         output_path=output_path,
+        safe_serialization=True,
     )
     state = PartialState()
     state.wait_for_everyone()

src/axolotl/cli/quantize.py

@@ -102,10 +102,12 @@ def do_quantize(
     LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}.")
     model.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
     )
     tokenizer.save_pretrained(
         str(Path(output_dir) / "quantized"),
+        safe_serialization=False,
         progressbar=True,
         save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
@@ -119,7 +121,7 @@ def do_quantize(
             hub_model_id.rstrip("-")
             + f"-{quantization_config_to_str[type(quantization_config)]}"
         )
-        model.push_to_hub(hub_model_id)
+        model.push_to_hub(hub_model_id, safe_serialization=False)
         tokenizer.push_to_hub(hub_model_id)
         if processor:
             processor.push_to_hub(hub_model_id)

src/axolotl/core/builders/base.py

@@ -216,7 +216,7 @@ class TrainerBuilderBase(abc.ABC):
     def _configure_warmup_and_logging(
         self, total_num_steps: int, training_args_kwargs: dict
     ):
-        warmup_steps: int | float = 0
+        warmup_steps = 0
         warmup_ratio = 0.0
         if self.cfg.warmup_steps is not None:
             warmup_steps = self.cfg.warmup_steps
@@ -230,10 +230,6 @@ class TrainerBuilderBase(abc.ABC):
         else:
             warmup_ratio = 0.03
 
-        # transformers v5
-        if warmup_ratio > 0.0 and warmup_steps == 0:
-            warmup_steps = warmup_ratio
-
         if warmup_steps == 1:
             warmup_steps = 2
 
@@ -246,6 +242,7 @@ class TrainerBuilderBase(abc.ABC):
                 else max(min(int(0.005 * total_num_steps), 10), 1)
             )
 
+        training_args_kwargs["warmup_ratio"] = warmup_ratio
         training_args_kwargs["warmup_steps"] = warmup_steps
 
     def _configure_precision_settings(self, training_args_kwargs: dict):
@@ -409,9 +406,6 @@ class TrainerBuilderBase(abc.ABC):
             if self.cfg.hub_strategy:
                 training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
 
-            if self.cfg.hub_revision:
-                training_args_kwargs["hub_revision"] = self.cfg.hub_revision
-
     def _configure_save_and_eval_strategy(self, training_args_kwargs: dict):
         # save_strategy and save_steps
         if self.cfg.save_steps:
@@ -536,7 +530,9 @@ class TrainerBuilderBase(abc.ABC):
             "loraplus_lr_ratio",
             "loraplus_lr_embedding",
             "output_dir",
+            "save_safetensors",
             "save_only_model",
+            "include_tokens_per_second",
             "weight_decay",
             "seed",
             "dion_momentum",
@@ -549,7 +545,6 @@ class TrainerBuilderBase(abc.ABC):
 
         arg_map = {
             "dion_learning_rate": "dion_lr",
-            "include_num_input_tokens_seen": "include_tokens_per_second",
         }
         for kwarg, cfg_arg in arg_map.items():
             if hasattr(self.cfg, cfg_arg) and getattr(self.cfg, cfg_arg) is not None:

src/axolotl/core/builders/causal.py

@@ -246,8 +246,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ddp_find_unused_parameters
         )
 
-        if self.cfg.group_by_length:
-            training_arguments_kwargs["train_sampling_strategy"] = "group_by_length"
+        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)
@@ -374,18 +373,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = multiple
 
-        if self.cfg.use_eaft:
-            from functools import partial
-
-            from axolotl.monkeypatch.loss.eaft import eaft_loss
-
-            configured_eaft_loss = partial(
-                eaft_loss,
-                alpha=self.cfg.eaft_alpha if self.cfg.eaft_alpha is not None else 1.0,
-                k=self.cfg.eaft_k if self.cfg.eaft_k is not None else 20,
-            )
-            trainer_kwargs["compute_loss_func"] = configured_eaft_loss
-
         trainer_cls = self._get_trainer_cls()
 
         trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
@@ -450,9 +437,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 or self.cfg.micro_batch_size > 1
             ):
                 return DataCollatorForSeq2Seq(self.tokenizer, **kwargs)
-            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn) or (
-                self.cfg.micro_batch_size == 1 and is_eval is False
-            ):
+            if not (self.cfg.sample_packing and self.cfg.pretrain_multipack_attn):
                 return None
 
         if self.cfg.model_config_type == "mamba":

src/axolotl/core/builders/rl.py

@@ -11,6 +11,7 @@ from axolotl.core.trainers import (
 )
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
+from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
 from axolotl.utils.callbacks.qat import QATCallback
@@ -51,13 +52,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls = None
         trainer_cls_args = [self.model]
 
-        if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
+        if self.cfg.rl is RLType.GRPO:
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.context_parallel_size > 1
             )
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
+
             trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
 
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
@@ -134,17 +134,19 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-            blocklist_args_kwargs.append("max_prompt_length")
+            # Handle when max_prompt_length == max_length from defaults
+            # CPOTrainer requires strictly less than
+            if (
+                training_args_kwargs["max_prompt_length"]
+                == training_args_kwargs["max_length"]
+            ):
+                training_args_kwargs["max_prompt_length"] -= 1
 
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
 
-            blocklist_args_kwargs.append("max_prompt_length")
-
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
-            # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs.append("max_prompt_length")
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -153,16 +155,10 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kto_undesirable_weight or 1.0
             )
 
-        elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
-            from axolotl.core.trainers.grpo import GRPOStrategy
-
+        elif self.cfg.rl is RLType.GRPO:
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
-            if self.cfg.rl is RLType.GDPO:
-                training_args_kwargs.setdefault(
-                    "multi_objective_aggregation", "normalize_then_sum"
-                )
 
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
             training_args_cls = AxolotlDPOConfig

src/axolotl/core/trainers/base.py

@@ -25,7 +25,7 @@ from torch.utils.data import (
 from transformers import PreTrainedModel, Trainer
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length, seed_worker
-from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
+from transformers.utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME, is_peft_available
 from trl.trainer.utils import pad_to_length
 from typing_extensions import override
 
@@ -660,10 +660,11 @@ class AxolotlTrainer(
             logs["tokens/train_per_sec_per_gpu"] = round(
                 self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
             )
-            if "total" in self.state.tokens:
-                logs["tokens/total"] = int(self.state.tokens["total"].item())
-            if "trainable" in self.state.tokens:
-                logs["tokens/trainable"] = int(self.state.tokens["trainable"].item())
+            if (
+                hasattr(self.state, "total_tokens")
+                and self.state.total_tokens is not None
+            ):
+                logs["total_tokens"] = int(self.state.total_tokens.item())
 
         del self._stored_metrics[train_eval]
 
@@ -719,13 +720,6 @@ class AxolotlTrainer(
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         LOG.info(f"Saving model checkpoint to {output_dir}")
-        if state_dict is None:
-            state_dict = self.accelerator.get_state_dict(self.model)
-        if state_dict is not None:
-            state_dict = {
-                k: v.clone() if isinstance(v, torch.Tensor) else v
-                for k, v in state_dict.items()
-            }
         supported_classes = (
             (PreTrainedModel,)
             if not is_peft_available()
@@ -745,38 +739,43 @@ class AxolotlTrainer(
                 ).save_pretrained(
                     output_dir,
                     state_dict=state_dict,
+                    safe_serialization=self.args.save_safetensors,
                 )
             else:
                 LOG.info(
                     "Trainer.model is not a `PreTrainedModel`, only saving its state dict."
                 )
-                safetensors.torch.save_file(
-                    state_dict,
-                    os.path.join(output_dir, SAFE_WEIGHTS_NAME),
-                    metadata={"format": "pt"},
-                )
+                if self.args.save_safetensors:
+                    safetensors.torch.save_file(
+                        state_dict,
+                        os.path.join(output_dir, SAFE_WEIGHTS_NAME),
+                        metadata={"format": "pt"},
+                    )
+                else:
+                    torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
         else:
             self.model.save_pretrained(
                 output_dir,
                 state_dict=state_dict,
+                safe_serialization=self.args.save_safetensors,
                 is_main_process=self.accelerator.is_main_process,
             )
 
-        if self.processing_class is not None:
-            self.processing_class.save_pretrained(output_dir)
-        elif (
-            self.data_collator is not None
-            and hasattr(self.data_collator, "tokenizer")
-            and self.data_collator.tokenizer is not None
-        ):
-            LOG.info(
-                "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
-            )
-            save_jinja_files = True
-            if self.axolotl_cfg:
-                save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
-            self.data_collator.tokenizer.save_pretrained(
-                output_dir, save_jinja_files=save_jinja_files
-            )
-        # Good practice: save your training arguments together with the trained model
-        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
+            if self.processing_class is not None:
+                self.processing_class.save_pretrained(output_dir)
+            elif (
+                self.data_collator is not None
+                and hasattr(self.data_collator, "tokenizer")
+                and self.data_collator.tokenizer is not None
+            ):
+                LOG.info(
+                    "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
+                )
+                save_jinja_files = True
+                if self.axolotl_cfg:
+                    save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
+                self.data_collator.tokenizer.save_pretrained(
+                    output_dir, save_jinja_files=save_jinja_files
+                )
+            # Good practice: save your training arguments together with the trained model
+            torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

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

@@ -57,18 +57,16 @@ class AxolotlDPOTrainer(
     def tokenize_row(
         features,
         processing_class,
-        max_prompt_length: int | None = None,
-        max_completion_length: int | None = None,
-        add_special_tokens: bool = True,
-        is_chat: bool = False,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length=max_prompt_length,
-            max_completion_length=max_completion_length,
-            add_special_tokens=add_special_tokens,
-            is_chat=is_chat,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
         )
         # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
         if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:

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

@@ -126,10 +126,8 @@ class GRPOStrategy:
         if trl.use_liger_loss is not None:
             grpo_args_kwargs["use_liger_loss"] = trl.use_liger_loss
 
-        if trl.multi_objective_aggregation is not None:
-            grpo_args_kwargs["multi_objective_aggregation"] = (
-                trl.multi_objective_aggregation
-            )
+        if trl.rollout_func:
+            grpo_args_kwargs["rollout_func"] = cls.get_rollout_func(trl.rollout_func)
 
         return grpo_args_kwargs
 
@@ -151,8 +149,6 @@ class GRPOStrategy:
             trainer_kwargs["reward_processing_classes"] = (
                 cfg.trl.reward_processing_classes
             )
-        if cfg.trl and cfg.trl.rollout_func:
-            trainer_kwargs["rollout_func"] = cls.get_rollout_func(cfg.trl.rollout_func)
 
         return trainer_kwargs
 
@@ -163,12 +159,7 @@ class GRPOStrategy:
 
     @classmethod
     def get_blocklist_args_kwargs(cls) -> list[str]:
-        return [
-            "dataset_num_proc",
-            "max_length",
-            "include_tokens_per_second",
-            "max_prompt_length",
-        ]
+        return ["dataset_num_proc", "max_length", "include_tokens_per_second"]
 
     @classmethod
     def get_reward_func(cls, reward_func_fqn: str) -> RewardFunc:

src/axolotl/core/trainers/mixins/optimizer.py

@@ -104,7 +104,7 @@ class OptimizerMixin(Trainer):
 
         return optimizer_grouped_parameters
 
-    def create_optimizer(self, model=None):
+    def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
             and self.args.embedding_lr_scale is None
@@ -112,9 +112,9 @@ class OptimizerMixin(Trainer):
             and self.args.lr_groups is None
             and self.optimizer_cls_and_kwargs is None
         ):
-            return super().create_optimizer(model=model)
+            return super().create_optimizer()
 
-        opt_model = self.model if model is None else model
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
 
         if (
             not self.optimizer

src/axolotl/core/trainers/trl.py

@@ -1,10 +1,12 @@
 """Module for TRL RL trainers"""
 
-from trl import RewardTrainer
-from trl.experimental.cpo import CPOTrainer
-from trl.experimental.kto import KTOTrainer
-from trl.experimental.orpo import ORPOTrainer
-from trl.experimental.prm import PRMTrainer
+from trl import (
+    CPOTrainer,
+    KTOTrainer,
+    ORPOTrainer,
+    PRMTrainer,
+    RewardTrainer,
+)
 
 from axolotl.core.trainers.mixins import DistributedParallelMixin, RngLoaderMixin
 from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, OptimizerMixin

src/axolotl/core/training_args.py

@@ -8,11 +8,7 @@ from dataclasses import dataclass, field
 from typing import Optional, Type
 
 from transformers import TrainingArguments
-from trl import RewardConfig
-from trl.experimental.cpo import CPOConfig
-from trl.experimental.kto import KTOConfig
-from trl.experimental.orpo import ORPOConfig
-from trl.experimental.prm import PRMConfig
+from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
 from axolotl.integrations.config import merge_training_args
 

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@0d4ce4b"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"
 ```
 
 ## Usage
@@ -36,7 +36,6 @@ plugins:
 - cohere
 - cohere2
 - deepseek_v3
-- exaone4
 - gemma
 - gemma2
 - gemma3
@@ -46,16 +45,13 @@ plugins:
 - glm
 - glm4
 - glm4_moe
-- glm4_moe_lite
-- glm46v
 - glm4v
 - glm4v_moe
-- glm_image
 - gpt_oss
 - granite
 - granitemoe
-- granitemoehybrid
 - granitemoeshared
+- granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
 - internvl
@@ -80,17 +76,16 @@ plugins:
 - phi3
 - phi4_multimodal
 - qwen2
-- qwen2_moe
 - qwen2_vl
+- qwen2_moe
 - qwen2_5_vl
 - qwen3
 - qwen3_moe
-- qwen3_next
 - qwen3_vl
 - qwen3_vl_moe
-- seed_oss
+- qwen3_next
 - smollm3
-- step3p5
+- seed_oss
 - voxtral
 
 ## Citation

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@0d4ce4b"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@318b7e2"`'
 )
 
 
@@ -104,7 +104,7 @@ class CutCrossEntropyPlugin(BasePlugin):
 
     def patch_llama_like(
         self,
-        model_type_to_patch: str,
+        model_type: str,
     ) -> None:
         """
         Generic patch for model architectures with causal lm similar to llama
@@ -112,10 +112,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
         def patch_generic(
-            maybe_model,
-            patch_options,
-            remote_model_id: str | None,
-            model_type: str,
+            maybe_model, patch_options, model_type: str, remote_model_id: str | None
         ):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
@@ -139,13 +136,11 @@ class CutCrossEntropyPlugin(BasePlugin):
                     f"Error: {str(e)}"
                 ) from e
 
-        if model_type_to_patch not in PATCH_FNS:
+        if model_type not in PATCH_FNS:
             LOG.warning_once(
-                "Setting up generic cce patch for model type: %s", model_type_to_patch
+                "Setting up generic cce patch for model type: %s", model_type
             )
             LOG.warning_once(
-                f"Generic Cut Cross Entropy + {model_type_to_patch} support is experimental and may not work as expected."
-            )
-            PATCH_FNS[model_type_to_patch] = partial(
-                patch_generic, model_type=model_type_to_patch
+                f"Generic Cut Cross Entropy + {model_type} support is experimental and may not work as expected."
             )
+            PATCH_FNS[model_type] = partial(patch_generic, model_type=model_type)

src/axolotl/integrations/kernels/README.md

@@ -1,44 +0,0 @@
-# Kernels Integration
-
-MoE (Mixture of Experts) kernels speed up training for MoE layers and reduce VRAM costs. In transformers v5, `batched_mm` and `grouped_mm` were integrated as built-in options via the `experts_implementation` config kwarg:
-
-```python
-class ExpertsInterface(GeneralInterface):
-    _global_mapping = {
-        "batched_mm": batched_mm_experts_forward,
-        "grouped_mm": grouped_mm_experts_forward,
-    }
-```
-
-In our custom integration, we add support for **ScatterMoE**, which is even more efficient and faster than `grouped_mm`.
-
-## Usage
-
-Add the following to your axolotl YAML config:
-
-```yaml
-plugins:
-  - axolotl.integrations.kernels.KernelsPlugin
-
-use_kernels: true
-use_scattermoe: true
-```
-
-**Important:** Setting `experts_implementation` is incompatible with `use_scattermoe`.
-
-## How It Works
-
-The `KernelsPlugin` runs before model loading and:
-
-1. Registers the ScatterMoE kernel from the [`axolotl-ai-co/scattermoe`](https://huggingface.co/axolotl-ai-co/scattermoe) Hub repo.
-2. Patches the model's `SparseMoeBlock` forward method with the optimized ScatterMoE implementation.
-
-This works for any MoE model in transformers that uses a `SparseMoeBlock` class (Mixtral, Qwen2-MoE, OLMoE, etc.).
-
-## Limitations
-
-ScatterMoE uses a softmax -> topk routing, so results may be different for some model arch as baseline (GPT-OSS, GLM_MOE_DSA).
-
-## Note on MegaBlocks
-
-We tested [MegaBlocks](https://huggingface.co/kernels-community/megablocks) but were unable to ensure numerical accuracy, so we did not integrate it. It was also incompatible with many newer model architectures in transformers.

src/axolotl/integrations/kernels/__init__.py

@@ -1,7 +0,0 @@
-from .args import KernelsArgs
-from .plugin import KernelsPlugin
-
-__all__ = [
-    "KernelsArgs",
-    "KernelsPlugin",
-]

src/axolotl/integrations/kernels/args.py

@@ -1,35 +0,0 @@
-from pydantic import BaseModel, model_validator
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class KernelsArgs(BaseModel):
-    use_scattermoe: bool | None = True
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_use_kernels(cls, data):
-        if data.get("use_kernels") is not True:
-            LOG.warning(
-                "`use_kernels` must be set to True to use this. Automatically setting it to True."
-            )
-            data["use_kernels"] = True
-
-        return data
-
-    @model_validator(mode="before")
-    @classmethod
-    def check_experts_implementation(cls, data):
-        experts_implementation = data.get("experts_implementation")
-        if experts_implementation is None:
-            # transformers may default to batched_mm when unset
-            data["experts_implementation"] = "eager"
-        elif experts_implementation != "eager":
-            LOG.warning(
-                "`experts_implementation` must be set to 'eager' to use this. Automatically setting it to 'eager'."
-            )
-            data["experts_implementation"] = "eager"
-
-        return data

src/axolotl/integrations/kernels/plugin.py

@@ -1,61 +0,0 @@
-from kernels import (
-    LayerRepository,
-    Mode,
-    register_kernel_mapping,
-    replace_kernel_forward_from_hub,
-)
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.callbacks.models import get_causal_lm_model_cls_prefix
-
-
-class KernelsPlugin(BasePlugin):
-    def get_input_args(self):
-        return "axolotl.integrations.kernels.KernelsArgs"
-
-    def pre_model_load(self, cfg):
-        if cfg.use_scattermoe:
-            self._register_kernels()
-            self._kernelize_model(cfg.model_config_type)
-
-    def _register_kernels(self):
-        register_kernel_mapping(
-            {
-                "HFScatterMoEParallelExperts": {
-                    "cuda": {
-                        Mode.TRAINING: LayerRepository(
-                            repo_id="axolotl-ai-co/scattermoe",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                        Mode.INFERENCE: LayerRepository(
-                            repo_id="axolotl-ai-co/scattermoe",
-                            layer_name="HFScatterMoEGatedMLP",
-                        ),
-                    },
-                }
-            }
-        )
-
-    def _kernelize_model(self, model_type: str):
-        if model_type == "olmoe":
-            from transformers.models.olmoe.modeling_olmoe import OlmoeSparseMoeBlock
-
-            replace_kernel_forward_from_hub(
-                OlmoeSparseMoeBlock, "HFScatterMoEParallelExperts"
-            )
-        else:
-            try:
-                model_moe_cls = get_model_moe_block(model_type)
-                replace_kernel_forward_from_hub(
-                    model_moe_cls, "HFScatterMoEParallelExperts"
-                )
-            except Exception as err:
-                raise ValueError(f"Unsupported model type: {model_type}") from err
-
-
-def get_model_moe_block(model_type: str):
-    module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-    model_cls_prefix, _ = get_causal_lm_model_cls_prefix(model_type)
-    module = __import__(module_path, fromlist=[f"{model_cls_prefix}SparseMoeBlock"])
-    model_cls = getattr(module, f"{model_cls_prefix}SparseMoeBlock")
-    return model_cls

src/axolotl/integrations/llm_compressor/utils.py

@@ -12,6 +12,7 @@ def save_compressed_model(
     model: PreTrainedModel,
     output_dir: Union[str, bytes],
     trainer: Trainer,
+    safe_serialization: bool = False,
     save_compressed: bool = False,
 ) -> None:
     """
@@ -21,6 +22,7 @@ def save_compressed_model(
         model (PreTrainedModel): The model to be saved.
         output_dir (str or bytes): Path where the model files will be written.
         trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
         save_compressed (bool): Write compressed tensors if True.
     """
     trainer.accelerator.wait_for_everyone()
@@ -32,6 +34,7 @@ def save_compressed_model(
     modify_save_pretrained(model)
     model.save_pretrained(
         output_dir,
+        safe_serialization=safe_serialization,
         save_compressed=save_compressed,
         skip_sparsity_compression_stats=not save_compressed,
     )

src/axolotl/integrations/lm_eval/README.md

@@ -6,12 +6,6 @@ See https://github.com/EleutherAI/lm-evaluation-harness
 
 ## Usage
 
-There are two ways to use the LM Eval integration:
-
-### 1. Post-Training Evaluation
-
-When training with the plugin enabled, evaluation runs automatically after training completes:
-
 ```yaml
 plugins:
   - axolotl.integrations.lm_eval.LMEvalPlugin
@@ -22,50 +16,9 @@ lm_eval_tasks:
   - arc_easy
 
 lm_eval_batch_size: # Batch size for evaluation
-
-# Directory to save evaluation results.
-# The final model is loaded from this directory
-# unless specified otherwise (see below)
-output_dir:
+output_dir: # Directory to save evaluation results
 ```
 
-Run training as usual:
-```bash
-axolotl train config.yml
-```
-
-### 2. Standalone CLI Evaluation
-
-Evaluate any model directly without training:
-
-```yaml
-lm_eval_model: meta-llama/Llama-2-7b-hf
-
-plugins:
-  - axolotl.integrations.lm_eval.LMEvalPlugin
-
-lm_eval_tasks:
-  - gsm8k
-  - hellaswag
-  - arc_easy
-
-lm_eval_batch_size: 8
-output_dir: ./outputs
-```
-
-Run evaluation:
-```bash
-axolotl lm-eval config.yml
-```
-
-## Model Selection Priority
-
-The model to evaluate is selected in the following priority order:
-
-1. **`lm_eval_model`** - Explicit model path or HuggingFace repo (highest priority)
-2. **`hub_model_id`** - Trained model pushed to HuggingFace Hub
-3. **`output_dir`** - Local checkpoint directory containing trained model weights
-
 ## Citation
 
 ```bib

src/axolotl/integrations/lm_eval/__init__.py

@@ -5,7 +5,7 @@ Module for the Plugin for LM Eval Harness
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin
-from axolotl.integrations.lm_eval.cli import build_lm_eval_command, get_model_path
+from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
 from .args import LMEvalArgs as LMEvalArgs
 
@@ -29,7 +29,7 @@ class LMEvalPlugin(BasePlugin):
                 wandb_project=cfg.wandb_project,
                 wandb_entity=cfg.wandb_entity,
                 wandb_name=cfg.wandb_name,
-                model=get_model_path(cfg),
+                model=cfg.lm_eval_model or cfg.hub_model_id,
             ):
                 subprocess.run(  # nosec
                     lm_eval_args,

src/axolotl/integrations/lm_eval/cli.py

@@ -13,21 +13,6 @@ import yaml
 from axolotl.utils.dict import DictDefault
 
 
-def get_model_path(cfg: DictDefault) -> str | None:
-    """
-    Determine which model path to use for evaluation.
-
-    Priority order (highest to lowest):
-    1. lm_eval_model - Explicit model path override
-    2. hub_model_id - Model pushed to HuggingFace Hub
-    3. None - Falls back to output_dir in build_lm_eval_command
-
-    Returns:
-        Model path string or None to use output_dir fallback
-    """
-    return cfg.lm_eval_model or cfg.hub_model_id or None
-
-
 def build_lm_eval_command(
     tasks: list[str],
     bfloat16=True,
@@ -123,7 +108,7 @@ def lm_eval(config: str, cloud: Optional[str] = None):
             wandb_project=cfg.wandb_project,
             wandb_entity=cfg.wandb_entity,
             wandb_name=cfg.wandb_name,
-            model=get_model_path(cfg),
+            model=cfg.lm_eval_model or cfg.hub_model_id,
             revision=cfg.revision,
             apply_chat_template=cfg.apply_chat_template,
             fewshot_as_multiturn=cfg.fewshot_as_multiturn,

src/axolotl/integrations/swanlab/__init__.py

@@ -1,6 +0,0 @@
-"""SwanLab integration plugin for Axolotl"""
-
-from axolotl.integrations.swanlab.args import SwanLabConfig
-from axolotl.integrations.swanlab.plugins import SwanLabPlugin
-
-__all__ = ["SwanLabConfig", "SwanLabPlugin"]

src/axolotl/integrations/swanlab/args.py

@@ -1,140 +0,0 @@
-"""SwanLab configuration arguments"""
-
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-
-class SwanLabConfig(BaseModel):
-    """SwanLab configuration subset"""
-
-    use_swanlab: bool | None = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Enable SwanLab experiment tracking and visualization"
-        },
-    )
-    swanlab_project: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Your SwanLab project name"},
-    )
-    swanlab_experiment_name: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Set the name of your SwanLab experiment"},
-    )
-    swanlab_description: str | None = Field(
-        default=None,
-        json_schema_extra={"description": "Description for your SwanLab experiment"},
-    )
-    swanlab_mode: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": '"cloud" to sync to SwanLab cloud, "local" for local only, "offline" to save metadata locally, "disabled" to turn off SwanLab'
-        },
-    )
-    swanlab_workspace: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "SwanLab workspace name (organization or username)"
-        },
-    )
-    swanlab_api_key: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "SwanLab API key for authentication. Can also be set via SWANLAB_API_KEY environment variable"
-        },
-    )
-    swanlab_log_model: bool | None = Field(
-        default=False,
-        json_schema_extra={
-            "description": "Whether to log model checkpoints to SwanLab (feature coming soon)"
-        },
-    )
-    swanlab_web_host: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Web address for SwanLab cloud environment (for private deployment)"
-        },
-    )
-    swanlab_api_host: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "API address for SwanLab cloud environment (for private deployment)"
-        },
-    )
-    swanlab_lark_webhook_url: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Lark (Feishu) webhook URL for sending training notifications to team chat"
-        },
-    )
-    swanlab_lark_secret: str | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Secret for Lark webhook HMAC signature authentication (optional)"
-        },
-    )
-    swanlab_log_completions: bool | None = Field(
-        default=True,
-        json_schema_extra={
-            "description": "Enable logging RLHF completions to SwanLab for qualitative analysis (DPO/KTO/ORPO/GRPO)"
-        },
-    )
-    swanlab_completion_log_interval: int | None = Field(
-        default=100,
-        json_schema_extra={
-            "description": "Number of training steps between completion table logging to SwanLab"
-        },
-    )
-    swanlab_completion_max_buffer: int | None = Field(
-        default=128,
-        json_schema_extra={
-            "description": "Maximum number of completions to buffer before logging (prevents memory leaks)"
-        },
-    )
-
-    @field_validator("swanlab_mode")
-    @classmethod
-    def validate_swanlab_mode(cls, v):
-        """Validate swanlab_mode is one of the allowed values."""
-        if v is None:
-            return v
-
-        valid_modes = ["cloud", "local", "offline", "disabled"]
-        if v not in valid_modes:
-            raise ValueError(
-                f"Invalid swanlab_mode: '{v}'.\n\n"
-                f"Valid options: {', '.join(valid_modes)}\n\n"
-                f"Examples:\n"
-                f"  swanlab_mode: cloud     # Sync to SwanLab cloud\n"
-                f"  swanlab_mode: local     # Local only, no cloud sync\n"
-                f"  swanlab_mode: offline   # Save metadata locally\n"
-                f"  swanlab_mode: disabled  # Turn off SwanLab\n"
-            )
-        return v
-
-    @field_validator("swanlab_project")
-    @classmethod
-    def validate_swanlab_project(cls, v):
-        """Validate swanlab_project is non-empty when provided."""
-        if v is not None and isinstance(v, str) and len(v.strip()) == 0:
-            raise ValueError(
-                "swanlab_project cannot be an empty string.\n\n"
-                "Either:\n"
-                "  1. Provide a valid project name: swanlab_project: my-project\n"
-                "  2. Remove the swanlab_project field entirely\n"
-            )
-        return v
-
-    @model_validator(mode="after")
-    def validate_swanlab_enabled_requires_project(self):
-        """Validate that if use_swanlab is True, swanlab_project must be set."""
-        if self.use_swanlab is True and not self.swanlab_project:
-            raise ValueError(
-                "SwanLab enabled (use_swanlab: true) but 'swanlab_project' is not set.\n\n"
-                "Solutions:\n"
-                "  1. Add 'swanlab_project: your-project-name' to your config\n"
-                "  2. Set 'use_swanlab: false' to disable SwanLab\n\n"
-                "Example:\n"
-                "  use_swanlab: true\n"
-                "  swanlab_project: my-llm-training\n"
-            )
-        return self

src/axolotl/integrations/swanlab/callbacks.py

@@ -1,179 +0,0 @@
-"""SwanLab callbacks for Axolotl trainers.
-
-This module provides HuggingFace Trainer callbacks for logging
-RLHF completions to SwanLab.
-"""
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments,
-)
-
-from axolotl.integrations.swanlab.completion_logger import CompletionLogger
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class SwanLabRLHFCompletionCallback(TrainerCallback):
-    """Callback for logging RLHF completions to SwanLab.
-
-    This callback periodically logs model completions (prompts, chosen/rejected
-    responses, rewards) to SwanLab during RLHF training for qualitative analysis.
-
-    Supports DPO, KTO, ORPO, and GRPO trainers.
-
-    Example usage:
-        >>> callback = SwanLabRLHFCompletionCallback(
-        ...     log_interval=100,  # Log every 100 steps
-        ...     max_completions=128,  # Keep last 128 completions
-        ... )
-        >>> trainer.add_callback(callback)
-
-    Attributes:
-        logger: CompletionLogger instance
-        log_interval: Number of steps between SwanLab logging
-        trainer_type: Auto-detected trainer type (dpo/kto/orpo/grpo)
-    """
-
-    def __init__(
-        self,
-        log_interval: int = 100,
-        max_completions: int = 128,
-        table_name: str = "rlhf_completions",
-    ):
-        """Initialize SwanLab RLHF completion callback.
-
-        Args:
-            log_interval: Log to SwanLab every N steps. Default: 100
-            max_completions: Maximum completions to buffer. Default: 128
-            table_name: SwanLab table name. Default: "rlhf_completions"
-        """
-        super().__init__()
-        self.logger = CompletionLogger(maxlen=max_completions)
-        self.log_interval = log_interval
-        self.table_name = table_name
-        self.trainer_type: str | None = None  # Auto-detected
-        self._last_logged_step = 0
-
-    def on_init_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Detect trainer type on initialization."""
-        trainer = kwargs.get("trainer")
-        if trainer is not None:
-            trainer_name = trainer.__class__.__name__
-            if "DPO" in trainer_name:
-                self.trainer_type = "dpo"
-            elif "KTO" in trainer_name:
-                self.trainer_type = "kto"
-            elif "ORPO" in trainer_name:
-                self.trainer_type = "orpo"
-            elif "GRPO" in trainer_name:
-                self.trainer_type = "grpo"
-            else:
-                self.trainer_type = "unknown"
-
-            LOG.info(
-                f"SwanLab RLHF completion logging enabled for {trainer_name} "
-                f"(type: {self.trainer_type})"
-            )
-
-    def on_log(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        logs: dict | None = None,
-        **kwargs,
-    ):
-        """Capture completions from logs and buffer them.
-
-        Different trainers log completions in different formats:
-        - DPO: logs['dpo/chosen'], logs['dpo/rejected'], logs['dpo/reward_diff']
-        - KTO: logs['kto/completion'], logs['kto/label'], logs['kto/reward']
-        - ORPO: logs['orpo/chosen'], logs['orpo/rejected']
-        - GRPO: logs['grpo/completion'], logs['grpo/reward']
-
-        Note: This is a placeholder implementation. Actual log keys depend
-        on the TRL trainer implementation. You may need to patch the trainers
-        to expose completion data in logs.
-        """
-        if logs is None or self.trainer_type is None:
-            return
-
-        step = state.global_step
-
-        # DPO completions
-        if self.trainer_type == "dpo":
-            if all(key in logs for key in ["dpo/prompt", "dpo/chosen", "dpo/rejected"]):
-                self.logger.add_dpo_completion(
-                    step=step,
-                    prompt=logs.get("dpo/prompt", ""),
-                    chosen=logs.get("dpo/chosen", ""),
-                    rejected=logs.get("dpo/rejected", ""),
-                    reward_diff=logs.get("dpo/reward_diff"),
-                )
-
-        # KTO completions
-        elif self.trainer_type == "kto":
-            if all(key in logs for key in ["kto/prompt", "kto/completion"]):
-                self.logger.add_kto_completion(
-                    step=step,
-                    prompt=logs.get("kto/prompt", ""),
-                    completion=logs.get("kto/completion", ""),
-                    label=logs.get("kto/label", False),
-                    reward=logs.get("kto/reward"),
-                )
-
-        # ORPO completions
-        elif self.trainer_type == "orpo":
-            if all(
-                key in logs for key in ["orpo/prompt", "orpo/chosen", "orpo/rejected"]
-            ):
-                self.logger.add_orpo_completion(
-                    step=step,
-                    prompt=logs.get("orpo/prompt", ""),
-                    chosen=logs.get("orpo/chosen", ""),
-                    rejected=logs.get("orpo/rejected", ""),
-                    log_odds_ratio=logs.get("orpo/log_odds_ratio"),
-                )
-
-        # GRPO completions
-        elif self.trainer_type == "grpo":
-            if all(key in logs for key in ["grpo/prompt", "grpo/completion"]):
-                self.logger.add_grpo_completion(
-                    step=step,
-                    prompt=logs.get("grpo/prompt", ""),
-                    completion=logs.get("grpo/completion", ""),
-                    reward=logs.get("grpo/reward"),
-                    advantage=logs.get("grpo/advantage"),
-                )
-
-        # Periodically log to SwanLab
-        if step - self._last_logged_step >= self.log_interval:
-            if len(self.logger) > 0:
-                self.logger.log_to_swanlab(table_name=self.table_name)
-                self.logger.clear()
-                self._last_logged_step = step
-
-    def on_train_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Log remaining completions at end of training."""
-        if len(self.logger) > 0:
-            LOG.info(
-                f"Training complete, logging final {len(self.logger)} completions to SwanLab"
-            )
-            self.logger.log_to_swanlab(table_name=self.table_name)
-            self._last_logged_step = state.global_step

src/axolotl/integrations/swanlab/completion_logger.py

@@ -1,228 +0,0 @@
-"""SwanLab completion logger for RLHF/DPO/KTO/ORPO/GRPO training.
-
-This module provides utilities for logging model completions during
-preference training to SwanLab for qualitative analysis.
-"""
-
-from collections import deque
-from collections.abc import Mapping
-from typing import Any
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-class CompletionLogger:
-    """Memory-bounded logger for RLHF completions.
-
-    Stores prompts, completions, and rewards in fixed-size deques to prevent
-    memory leaks during long training runs. Logs completion tables to SwanLab
-    for qualitative analysis of model outputs.
-
-    Example usage:
-        >>> logger = CompletionLogger(maxlen=128)
-        >>> logger.add_dpo_completion(
-        ...     step=0,
-        ...     prompt="What is AI?",
-        ...     chosen="Artificial Intelligence is...",
-        ...     rejected="AI means...",
-        ...     reward_diff=0.5
-        ... )
-        >>> logger.log_to_swanlab()
-
-    Attributes:
-        maxlen: Maximum number of completions to store (older ones are dropped)
-        data: Deque storing completion dictionaries
-    """
-
-    def __init__(self, maxlen: int = 128):
-        """Initialize completion logger with bounded buffer.
-
-        Args:
-            maxlen: Maximum number of completions to store. When the buffer
-                is full, oldest completions are automatically discarded.
-                Default: 128 (sufficient for most RLHF runs without memory issues)
-        """
-        self.maxlen = maxlen
-        self.data: deque[Mapping[str, Any]] = deque(maxlen=maxlen)
-
-    def add_dpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        chosen: str,
-        rejected: str,
-        reward_diff: float | None = None,
-    ) -> None:
-        """Add a DPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            chosen: Chosen (preferred) completion
-            rejected: Rejected (non-preferred) completion
-            reward_diff: Reward difference (chosen - rejected), if available
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "chosen": chosen,
-            "rejected": rejected,
-        }
-        if reward_diff is not None:
-            entry["reward_diff"] = reward_diff
-
-        self.data.append(entry)
-
-    def add_kto_completion(
-        self,
-        step: int,
-        prompt: str,
-        completion: str,
-        label: bool,
-        reward: float | None = None,
-    ) -> None:
-        """Add a KTO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            completion: Model-generated completion
-            label: True if desirable, False if undesirable
-            reward: Reward score, if available
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "completion": completion,
-            "label": "desirable" if label else "undesirable",
-        }
-        if reward is not None:
-            entry["reward"] = reward
-
-        self.data.append(entry)
-
-    def add_orpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        chosen: str,
-        rejected: str,
-        log_odds_ratio: float | None = None,
-    ) -> None:
-        """Add an ORPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            chosen: Chosen (preferred) completion
-            rejected: Rejected (non-preferred) completion
-            log_odds_ratio: Log odds ratio between chosen and rejected
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "chosen": chosen,
-            "rejected": rejected,
-        }
-        if log_odds_ratio is not None:
-            entry["log_odds_ratio"] = log_odds_ratio
-
-        self.data.append(entry)
-
-    def add_grpo_completion(
-        self,
-        step: int,
-        prompt: str,
-        completion: str,
-        reward: float | None = None,
-        advantage: float | None = None,
-    ) -> None:
-        """Add a GRPO completion to the buffer.
-
-        Args:
-            step: Training step number
-            prompt: Input prompt
-            completion: Model-generated completion
-            reward: Reward score from reward model
-            advantage: Advantage estimate (reward - baseline)
-        """
-        entry = {
-            "step": step,
-            "prompt": prompt,
-            "completion": completion,
-        }
-        if reward is not None:
-            entry["reward"] = reward
-        if advantage is not None:
-            entry["advantage"] = advantage
-
-        self.data.append(entry)
-
-    def log_to_swanlab(self, table_name: str = "completions") -> bool:
-        """Log buffered completions to SwanLab as a table.
-
-        Creates a SwanLab echarts Table with all buffered completions.
-        Only logs if SwanLab is initialized and data is available.
-
-        Args:
-            table_name: Name of the table in SwanLab dashboard.
-                Default: "completions"
-
-        Returns:
-            True if logging succeeded, False otherwise
-        """
-        if not self.data:
-            LOG.debug("No completions to log to SwanLab")
-            return False
-
-        try:
-            import swanlab
-
-            if swanlab.get_run() is None:
-                LOG.debug("SwanLab not initialized, skipping completion logging")
-                return False
-
-            # Convert deque to list of dicts
-            completions = list(self.data)
-
-            # Extract headers from first entry (all entries should have same structure)
-            headers = list(completions[0].keys())
-
-            # Build rows: each completion becomes one row
-            rows = []
-            for completion in completions:
-                row = [completion.get(header, "") for header in headers]
-                rows.append(row)
-
-            # Log to SwanLab as echarts Table
-            swanlab.log({table_name: swanlab.echarts.Table().add(headers, rows)})
-
-            LOG.info(f"Logged {len(rows)} completions to SwanLab table '{table_name}'")
-            return True
-
-        except ImportError:
-            LOG.warning(
-                "SwanLab not installed, cannot log completions. "
-                "Install with: pip install swanlab"
-            )
-            return False
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception("Failed to log completions to SwanLab: %s", err)
-            return False
-
-    def clear(self) -> None:
-        """Clear all buffered completions."""
-        self.data.clear()
-
-    def __len__(self) -> int:
-        """Return number of buffered completions."""
-        return len(self.data)
-
-    def __repr__(self) -> str:
-        """String representation showing buffer status."""
-        return (
-            f"CompletionLogger(maxlen={self.maxlen}, "
-            f"buffered={len(self.data)}/{self.maxlen})"
-        )

src/axolotl/integrations/swanlab/plugins.py

@@ -1,554 +0,0 @@
-"""SwanLab Plugin for Axolotl"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.logging import get_logger
-
-if TYPE_CHECKING:
-    from transformers import TrainerCallback
-
-    from axolotl.utils.dict import DictDefault
-
-LOG = get_logger(__name__)
-
-
-class SwanLabPlugin(BasePlugin):
-    """
-    SwanLab integration plugin for Axolotl.
-
-    Provides experiment tracking, visualization, and logging capabilities
-    using SwanLab (https://swanlab.cn).
-
-    Usage in config.yaml:
-        plugins:
-          - axolotl.integrations.swanlab.SwanLabPlugin
-
-        use_swanlab: true
-        swanlab_project: my-project
-        swanlab_experiment_name: my-experiment
-        swanlab_mode: cloud  # or 'local', 'offline', 'disabled'
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.swanlab_initialized = False
-        LOG.info("SwanLab plugin initialized")
-
-    def get_input_args(self) -> str:
-        """Returns the configuration model for SwanLab integration."""
-        return "axolotl.integrations.swanlab.SwanLabConfig"
-
-    def register(self, cfg: dict):
-        """Register SwanLab plugin with configuration and conflict detection."""
-        LOG.info("Registering SwanLab plugin")
-
-        # === Conflict Detection: Required Fields ===
-
-        # Check if SwanLab is enabled
-        if cfg.get("use_swanlab"):
-            # 1. Validate project name is set
-            if not cfg.get("swanlab_project"):
-                raise ValueError(
-                    "SwanLab enabled but 'swanlab_project' is not set.\n\n"
-                    "Solutions:\n"
-                    "  1. Add 'swanlab_project: your-project-name' to your config\n"
-                    "  2. Set 'use_swanlab: false' to disable SwanLab\n\n"
-                    "See: src/axolotl/integrations/swanlab/README.md for examples"
-                )
-
-            # 2. Validate swanlab_mode value
-            valid_modes = ["cloud", "local", "offline", "disabled"]
-            mode = cfg.get("swanlab_mode")
-            if mode and mode not in valid_modes:
-                raise ValueError(
-                    f"Invalid swanlab_mode: '{mode}'.\n\n"
-                    f"Valid options: {', '.join(valid_modes)}\n\n"
-                    f"Example:\n"
-                    f"  swanlab_mode: cloud  # Sync to SwanLab cloud\n"
-                    f"  swanlab_mode: local  # Local only, no cloud sync\n"
-                )
-
-            # 3. Check API key for cloud mode
-            import os
-
-            mode = cfg.get("swanlab_mode", "cloud")  # Default is cloud
-            if mode == "cloud":
-                api_key = cfg.get("swanlab_api_key") or os.environ.get(
-                    "SWANLAB_API_KEY"
-                )
-                if not api_key:
-                    LOG.warning(
-                        "SwanLab cloud mode enabled but no API key found.\n"
-                        "SwanLab may fail to initialize during training.\n\n"
-                        "Solutions:\n"
-                        "  1. Set SWANLAB_API_KEY environment variable:\n"
-                        "     export SWANLAB_API_KEY=your-api-key\n"
-                        "  2. Add 'swanlab_api_key: your-api-key' to config (less secure)\n"
-                        "  3. Run 'swanlab login' before training\n"
-                        "  4. Use 'swanlab_mode: local' for offline tracking\n"
-                    )
-
-        # === Conflict Detection: Multi-Logger Performance Warning ===
-
-        # Detect all active logging tools
-        active_loggers = []
-        if cfg.get("use_wandb"):
-            active_loggers.append("WandB")
-        if cfg.get("use_mlflow"):
-            active_loggers.append("MLflow")
-        if cfg.get("comet_api_key") or cfg.get("comet_project_name"):
-            active_loggers.append("Comet")
-        if cfg.get("use_swanlab"):
-            active_loggers.append("SwanLab")
-
-        if len(active_loggers) > 1:
-            LOG.warning(
-                f"\n{'=' * 70}\n"
-                f"Multiple logging tools enabled: {', '.join(active_loggers)}\n"
-                f"{'=' * 70}\n"
-                f"This may cause:\n"
-                f"  - Performance overhead (~1-2% per logger, cumulative)\n"
-                f"  - Increased memory usage\n"
-                f"  - Longer training time per step\n"
-                f"  - Potential config/callback conflicts\n\n"
-                f"Recommendations:\n"
-                f"  - Choose ONE primary logging tool for production training\n"
-                f"  - Use multiple loggers only for:\n"
-                f"    * Migration period (transitioning between tools)\n"
-                f"    * Short comparison runs\n"
-                f"    * Debugging specific tool issues\n"
-                f"  - Monitor system resources (CPU, memory) during training\n"
-                f"{'=' * 70}\n"
-            )
-
-            if len(active_loggers) >= 3:
-                LOG.error(
-                    f"\n{'!' * 70}\n"
-                    f"WARNING: {len(active_loggers)} logging tools enabled simultaneously!\n"
-                    f"{'!' * 70}\n"
-                    f"This is likely unintentional and WILL significantly impact performance.\n"
-                    f"Expected overhead: ~{len(active_loggers) * 1.5:.1f}% per training step.\n\n"
-                    f"STRONGLY RECOMMEND:\n"
-                    f"  - Disable all but ONE logging tool\n"
-                    f"  - Use config inheritance to manage multiple configs\n"
-                    f"{'!' * 70}\n"
-                )
-
-        # === Auto-Enable Logic ===
-
-        # Enable SwanLab if project is specified
-        if cfg.get("swanlab_project") and not cfg.get("use_swanlab"):
-            cfg["use_swanlab"] = True
-            LOG.info("Automatically enabled use_swanlab because swanlab_project is set")
-
-    def pre_model_load(self, cfg: DictDefault):
-        """Initialize SwanLab before model loading with runtime checks."""
-        if not cfg.use_swanlab:
-            return
-
-        # === Runtime Check: Import Availability ===
-        try:
-            import swanlab
-        except ImportError as err:
-            raise ImportError(
-                "SwanLab is not installed.\n\n"
-                "Install with:\n"
-                "  pip install swanlab\n\n"
-                "Or add to requirements:\n"
-                "  swanlab>=0.3.0\n\n"
-                f"Original error: {err}"
-            ) from err
-
-        # Log SwanLab version
-        try:
-            swanlab_version = swanlab.__version__
-            LOG.info(f"SwanLab version: {swanlab_version}")
-        except AttributeError:
-            LOG.warning("Could not determine SwanLab version")
-
-        # === Runtime Check: Distributed Training Setup ===
-        from axolotl.utils.distributed import get_world_size, is_main_process
-
-        world_size = get_world_size()
-        if world_size > 1:
-            mode = getattr(cfg, "swanlab_mode", "cloud")
-            LOG.info(
-                f"\n{'=' * 70}\n"
-                f"Distributed training detected (world_size={world_size})\n"
-                f"SwanLab mode: {mode}\n"
-                f"{'=' * 70}\n"
-                f"Behavior:\n"
-                f"  - Only rank 0 will initialize SwanLab\n"
-                f"  - Other ranks will skip SwanLab to avoid conflicts\n"
-            )
-
-            if mode == "cloud":
-                LOG.info(
-                    f"  - Only rank 0 will upload to SwanLab cloud\n"
-                    f"  - Other ranks run without SwanLab overhead\n"
-                    f"{'=' * 70}\n"
-                )
-
-        # Only initialize SwanLab on the main process (rank 0)
-        # to avoid creating multiple runs in distributed training
-        if not is_main_process():
-            LOG.debug("Skipping SwanLab initialization on non-main process")
-            return
-
-        # Initialize SwanLab run (passing all params directly to init)
-        try:
-            init_kwargs = self._get_swanlab_init_kwargs(cfg)
-            swanlab.init(**init_kwargs)
-            self.swanlab_initialized = True
-            LOG.info(f"SwanLab initialized with project: {cfg.swanlab_project}")
-
-            # Register Lark notification callback (if configured)
-            self._register_lark_callback(cfg)
-
-            # Log configuration (with error handling)
-            try:
-                config_dict = self._prepare_config_for_logging(cfg)
-                swanlab.config.update(config_dict)
-                LOG.debug("Successfully logged config to SwanLab")
-            except Exception as config_err:  # pylint: disable=broad-except
-                LOG.warning(
-                    f"Failed to log config to SwanLab: {config_err}. Continuing anyway."
-                )
-
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception("Failed to initialize SwanLab: %s", err)
-            self.swanlab_initialized = False
-
-    def add_callbacks_pre_trainer(self, cfg: DictDefault, model):
-        """Add SwanLab callbacks before trainer creation."""
-        callbacks: list[TrainerCallback] = []
-
-        if not cfg.use_swanlab:
-            return callbacks
-
-        if not self.swanlab_initialized:
-            LOG.warning("SwanLab not initialized, skipping callback registration")
-            return callbacks
-
-        try:
-            from axolotl.utils.callbacks.swanlab import (
-                CustomSwanLabCallback,
-                SaveAxolotlConfigtoSwanLabCallback,
-            )
-
-            # Add our custom lightweight SwanLabCallback
-            # (avoids omegaconf/antlr4 version conflicts)
-            swanlab_callback = CustomSwanLabCallback()
-            callbacks.append(swanlab_callback)
-            LOG.info("Added CustomSwanLabCallback for metrics logging")
-
-            # Add Axolotl config logging callback
-            if cfg.axolotl_config_path:
-                config_callback = SaveAxolotlConfigtoSwanLabCallback(
-                    cfg.axolotl_config_path
-                )
-                callbacks.append(config_callback)
-                LOG.info("Added SaveAxolotlConfigtoSwanLabCallback")
-
-        except ImportError as err:
-            LOG.exception("Failed to import SwanLab callbacks: %s", err)
-
-        return callbacks
-
-    def post_trainer_create(self, cfg: DictDefault, trainer):
-        """Post-trainer creation hook."""
-        if cfg.use_swanlab and self.swanlab_initialized:
-            try:
-                import swanlab
-
-                # Log additional trainer information (with safe conversion)
-                trainer_config = {
-                    "total_steps": int(trainer.state.max_steps)
-                    if trainer.state.max_steps
-                    else None,
-                    "num_train_epochs": float(trainer.args.num_train_epochs)
-                    if trainer.args.num_train_epochs
-                    else None,
-                    "train_batch_size": int(trainer.args.train_batch_size)
-                    if hasattr(trainer.args, "train_batch_size")
-                    else None,
-                    "gradient_accumulation_steps": int(
-                        trainer.args.gradient_accumulation_steps
-                    )
-                    if trainer.args.gradient_accumulation_steps
-                    else None,
-                }
-                # Remove None values
-                trainer_config = {
-                    k: v for k, v in trainer_config.items() if v is not None
-                }
-
-                if trainer_config:
-                    swanlab.config.update(trainer_config)
-                    LOG.info("Logged trainer configuration to SwanLab")
-            except Exception as err:  # pylint: disable=broad-except
-                LOG.debug(f"Failed to log trainer config to SwanLab: {err}")
-
-            # Register RLHF completion logging callback if enabled
-            self._register_completion_callback(cfg, trainer)
-
-    def _get_swanlab_init_kwargs(self, cfg: DictDefault) -> dict:
-        """Prepare kwargs for swanlab.init().
-
-        Passes all configuration parameters directly to swanlab.init()
-        instead of using environment variables as an intermediate layer.
-
-        Returns:
-            dict: Keyword arguments for swanlab.init()
-        """
-        init_kwargs = {}
-
-        # Project name (required)
-        if cfg.swanlab_project:
-            init_kwargs["project"] = cfg.swanlab_project
-
-        # Experiment name
-        if cfg.swanlab_experiment_name:
-            init_kwargs["experiment_name"] = cfg.swanlab_experiment_name
-
-        # Description
-        if cfg.swanlab_description:
-            init_kwargs["description"] = cfg.swanlab_description
-
-        # Workspace (organization)
-        if cfg.swanlab_workspace:
-            init_kwargs["workspace"] = cfg.swanlab_workspace
-
-        # Mode: cloud, local, offline, disabled
-        if cfg.swanlab_mode:
-            init_kwargs["mode"] = cfg.swanlab_mode
-
-        # API key (pass directly instead of via env var)
-        if cfg.swanlab_api_key:
-            init_kwargs["api_key"] = cfg.swanlab_api_key
-
-        # Private deployment hosts (pass directly instead of via env var)
-        if cfg.swanlab_web_host:
-            init_kwargs["web_host"] = cfg.swanlab_web_host
-
-        if cfg.swanlab_api_host:
-            init_kwargs["api_host"] = cfg.swanlab_api_host
-
-        # Log model checkpoints (coming soon in SwanLab)
-        if cfg.swanlab_log_model:
-            init_kwargs["log_model"] = cfg.swanlab_log_model
-
-        # Custom branding - adds Axolotl identifier to SwanLab UI
-        # This helps identify runs from Axolotl vs other frameworks
-        init_kwargs["config"] = {"UPPERFRAME": "🦎 Axolotl"}
-
-        return init_kwargs
-
-    def _prepare_config_for_logging(self, cfg: DictDefault) -> dict:
-        """Prepare configuration dict for logging to SwanLab."""
-
-        def safe_convert(value):
-            """Convert value to JSON-serializable type."""
-            if value is None:
-                return None
-            if isinstance(value, (int, float, bool)):
-                return value
-            if isinstance(value, str):
-                return value
-            # Convert everything else to string
-            return str(value)
-
-        try:
-            # Extract important training parameters with safe conversion
-            config_dict = {
-                "base_model": safe_convert(getattr(cfg, "base_model", "")),
-                "model_type": safe_convert(getattr(cfg, "model_type", "")),
-                "sequence_len": safe_convert(getattr(cfg, "sequence_len", None)),
-                "micro_batch_size": safe_convert(
-                    getattr(cfg, "micro_batch_size", None)
-                ),
-                "gradient_accumulation_steps": safe_convert(
-                    getattr(cfg, "gradient_accumulation_steps", None)
-                ),
-                "num_epochs": safe_convert(getattr(cfg, "num_epochs", None)),
-                "max_steps": safe_convert(getattr(cfg, "max_steps", None)),
-                "learning_rate": safe_convert(getattr(cfg, "learning_rate", None)),
-                "lr_scheduler": safe_convert(getattr(cfg, "lr_scheduler", "")),
-                "optimizer": safe_convert(getattr(cfg, "optimizer", "")),
-                "warmup_ratio": safe_convert(getattr(cfg, "warmup_ratio", None)),
-                "weight_decay": safe_convert(getattr(cfg, "weight_decay", None)),
-                "seed": safe_convert(getattr(cfg, "seed", None)),
-                "bf16": safe_convert(getattr(cfg, "bf16", None)),
-                "tf32": safe_convert(getattr(cfg, "tf32", None)),
-                "flash_attention": safe_convert(getattr(cfg, "flash_attention", None)),
-                "sample_packing": safe_convert(getattr(cfg, "sample_packing", None)),
-            }
-
-            # Add FSDP/parallel config - only boolean flags
-            if hasattr(cfg, "fsdp_config") and cfg.fsdp_config:
-                config_dict["fsdp_enabled"] = True
-                config_dict["fsdp_version"] = safe_convert(
-                    getattr(cfg, "fsdp_version", None)
-                )
-
-            if hasattr(cfg, "deepspeed") and cfg.deepspeed:
-                config_dict["deepspeed_enabled"] = True
-
-            # Add context parallel info
-            if hasattr(cfg, "context_parallel_size"):
-                config_dict["context_parallel_size"] = safe_convert(
-                    getattr(cfg, "context_parallel_size", None)
-                )
-            if hasattr(cfg, "tensor_parallel_size"):
-                config_dict["tensor_parallel_size"] = safe_convert(
-                    getattr(cfg, "tensor_parallel_size", None)
-                )
-            if hasattr(cfg, "dp_shard_size"):
-                config_dict["dp_shard_size"] = safe_convert(
-                    getattr(cfg, "dp_shard_size", None)
-                )
-
-            # Remove None values and empty strings
-            config_dict = {
-                k: v
-                for k, v in config_dict.items()
-                if v is not None and v != "" and v != "None"
-            }
-
-            return config_dict
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.warning(f"Failed to prepare config for logging: {err}")
-            # Return minimal config
-            try:
-                lr = getattr(cfg, "learning_rate", None)
-                lr_value = float(lr) if lr is not None else None
-            except (TypeError, ValueError):
-                lr_value = None
-            return {
-                "base_model": str(getattr(cfg, "base_model", "unknown")),
-                "learning_rate": lr_value,
-            }
-
-    def _register_lark_callback(self, cfg: DictDefault):
-        """Register Lark (Feishu) notification callback if configured.
-
-        Lark notifications enable sending training updates to team chat channels,
-        useful for production monitoring and team collaboration.
-
-        Args:
-            cfg: Configuration object with Lark webhook settings
-        """
-        # Check if Lark webhook URL is configured
-        lark_webhook_url = getattr(cfg, "swanlab_lark_webhook_url", None)
-        if not lark_webhook_url:
-            return  # Lark not configured, skip
-
-        try:
-            import swanlab
-            from swanlab.plugin.notification import LarkCallback
-
-            # Get optional secret for HMAC signature authentication
-            lark_secret = getattr(cfg, "swanlab_lark_secret", None)
-
-            # Create Lark callback with webhook URL and optional secret
-            lark_callback = LarkCallback(
-                webhook_url=lark_webhook_url,
-                secret=lark_secret,
-            )
-
-            # Register callback with SwanLab
-            swanlab.register_callbacks([lark_callback])
-
-            if lark_secret:
-                LOG.info(
-                    "Registered Lark notification callback with HMAC authentication"
-                )
-            else:
-                LOG.info("Registered Lark notification callback (no HMAC secret)")
-                LOG.warning(
-                    "Lark webhook has no secret configured. "
-                    "For production use, set 'swanlab_lark_secret' to enable HMAC signature verification."
-                )
-
-        except ImportError as err:
-            LOG.warning(
-                f"Failed to import SwanLab Lark plugin: {err}\n\n"
-                "Lark notifications require SwanLab >= 0.3.0 with plugin support.\n"
-                "Install with: pip install 'swanlab>=0.3.0'\n\n"
-                "Continuing without Lark notifications..."
-            )
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception(
-                "Failed to register Lark callback: %s\n\n"
-                "Check your Lark webhook URL and secret configuration.\n"
-                "Continuing without Lark notifications...",
-                err,
-            )
-
-    def _register_completion_callback(self, cfg: DictDefault, trainer):
-        """Register RLHF completion logging callback if enabled and applicable.
-
-        This callback logs model completions (prompts, chosen/rejected responses,
-        rewards) to SwanLab during RLHF training for qualitative analysis.
-
-        Args:
-            cfg: Configuration object with completion logging settings
-            trainer: The trainer instance to add callback to
-        """
-        # Check if completion logging is enabled
-        log_completions = getattr(cfg, "swanlab_log_completions", True)
-        if not log_completions:
-            LOG.debug("SwanLab completion logging disabled by config")
-            return
-
-        # Check if trainer is an RLHF trainer
-        trainer_name = trainer.__class__.__name__
-        rlhf_trainers = ["DPO", "KTO", "ORPO", "GRPO", "CPO"]
-        is_rlhf_trainer = any(name in trainer_name for name in rlhf_trainers)
-
-        if not is_rlhf_trainer:
-            LOG.debug(
-                f"Trainer {trainer_name} is not an RLHF trainer, "
-                "skipping completion logging callback"
-            )
-            return
-
-        try:
-            from axolotl.integrations.swanlab.callbacks import (
-                SwanLabRLHFCompletionCallback,
-            )
-
-            # Get configuration parameters
-            log_interval = getattr(cfg, "swanlab_completion_log_interval", 100)
-            max_buffer = getattr(cfg, "swanlab_completion_max_buffer", 128)
-
-            # Create and register callback
-            completion_callback = SwanLabRLHFCompletionCallback(
-                log_interval=log_interval,
-                max_completions=max_buffer,
-                table_name="rlhf_completions",
-            )
-
-            trainer.add_callback(completion_callback)
-
-            LOG.info(
-                f"Registered SwanLab RLHF completion logging callback for {trainer_name} "
-                f"(log_interval={log_interval}, max_buffer={max_buffer})"
-            )
-
-        except ImportError as err:
-            LOG.warning(
-                f"Failed to import SwanLab completion callback: {err}\n\n"
-                "This is a bug - the callback should be available.\n"
-                "Please report this issue.\n\n"
-                "Continuing without completion logging..."
-            )
-        except Exception as err:  # pylint: disable=broad-except
-            LOG.exception(
-                "Failed to register SwanLab completion callback: %s\n\n"
-                "Continuing without completion logging...",
-                err,
-            )

src/axolotl/integrations/swanlab/profiling.py

@@ -1,203 +0,0 @@
-"""SwanLab profiling utilities for Axolotl trainers.
-
-This module provides decorators and context managers for profiling
-trainer methods and logging execution times to SwanLab.
-"""
-
-import time
-from contextlib import contextmanager
-from functools import wraps
-from typing import Any, Callable
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-
-@contextmanager
-def swanlab_profiling_context(trainer: Any, func_name: str):
-    """Context manager for profiling trainer methods.
-
-    Measures execution time and logs to SwanLab if enabled.
-
-    Example usage:
-        >>> with swanlab_profiling_context(self, "training_step"):
-        ...     result = do_expensive_computation()
-
-    Args:
-        trainer: Trainer instance (must have cfg attribute with use_swanlab flag)
-        func_name: Name of the function being profiled
-
-    Yields:
-        None
-    """
-    start_time = time.perf_counter()
-    try:
-        yield
-    finally:
-        duration = time.perf_counter() - start_time
-
-        # Check if SwanLab is enabled and initialized
-        use_swanlab = getattr(getattr(trainer, "cfg", None), "use_swanlab", False)
-        if use_swanlab:
-            try:
-                import swanlab
-
-                if swanlab.get_run() is not None:
-                    # Log profiling metric
-                    trainer_class = trainer.__class__.__name__
-                    metric_name = f"profiling/Time taken: {trainer_class}.{func_name}"
-
-                    swanlab.log({metric_name: duration})
-
-            except ImportError:
-                # SwanLab not installed, silently skip
-                pass
-            except Exception as err:  # pylint: disable=broad-except
-                # Log error but don't fail training
-                LOG.debug(f"Failed to log profiling metric for {func_name}: {err}")
-
-
-def swanlab_profile(func: Callable) -> Callable:
-    """Decorator to profile and log function execution time to SwanLab.
-
-    Automatically measures execution time of trainer methods and logs
-    to SwanLab as profiling metrics.
-
-    Example usage:
-        >>> class MyTrainer:
-        ...     @swanlab_profile
-        ...     def training_step(self, model, inputs):
-        ...         return super().training_step(model, inputs)
-
-    Args:
-        func: Function to profile (must be a method of a trainer instance)
-
-    Returns:
-        Wrapped function with profiling
-    """
-
-    @wraps(func)
-    def wrapper(self, *args, **kwargs):
-        with swanlab_profiling_context(self, func.__name__):
-            return func(self, *args, **kwargs)
-
-    return wrapper
-
-
-class ProfilingConfig:
-    """Configuration for SwanLab profiling.
-
-    This class provides a centralized way to control profiling behavior.
-
-    Attributes:
-        enabled: Whether profiling is enabled globally
-        min_duration_ms: Minimum duration (in ms) to log (filters out very fast ops)
-        log_interval: Log every N function calls (to reduce overhead)
-    """
-
-    def __init__(
-        self,
-        enabled: bool = True,
-        min_duration_ms: float = 0.1,
-        log_interval: int = 1,
-    ):
-        """Initialize profiling configuration.
-
-        Args:
-            enabled: Enable profiling. Default: True
-            min_duration_ms: Minimum duration to log (ms). Default: 0.1
-            log_interval: Log every N calls. Default: 1 (log all)
-        """
-        self.enabled = enabled
-        self.min_duration_ms = min_duration_ms
-        self.log_interval = log_interval
-        self._call_counts: dict[str, int] = {}
-
-    def should_log(self, func_name: str, duration_seconds: float) -> bool:
-        """Check if a profiling measurement should be logged.
-
-        Args:
-            func_name: Name of the profiled function
-            duration_seconds: Execution duration in seconds
-
-        Returns:
-            True if should log, False otherwise
-        """
-        if not self.enabled:
-            return False
-
-        # Check minimum duration threshold
-        duration_ms = duration_seconds * 1000
-        if duration_ms < self.min_duration_ms:
-            return False
-
-        # Check log interval
-        self._call_counts.setdefault(func_name, 0)
-        self._call_counts[func_name] += 1
-
-        # Always log on first call OR at intervals
-        count = self._call_counts[func_name]
-        if count == 1 or count % self.log_interval == 0:
-            return True
-
-        return False
-
-
-# Global profiling config (can be modified by users)
-DEFAULT_PROFILING_CONFIG = ProfilingConfig()
-
-
-@contextmanager
-def swanlab_profiling_context_advanced(
-    trainer: Any,
-    func_name: str,
-    config: ProfilingConfig | None = None,
-):
-    """Advanced profiling context with configurable behavior.
-
-    Similar to swanlab_profiling_context but with additional configuration
-    options for filtering and throttling profiling logs.
-
-    Example usage:
-        >>> config = ProfilingConfig(min_duration_ms=1.0, log_interval=10)
-        >>> with swanlab_profiling_context_advanced(self, "forward", config):
-        ...     output = model(inputs)
-
-    Args:
-        trainer: Trainer instance
-        func_name: Function name
-        config: Profiling configuration. If None, uses DEFAULT_PROFILING_CONFIG
-
-    Yields:
-        None
-    """
-    if config is None:
-        config = DEFAULT_PROFILING_CONFIG
-
-    start_time = time.perf_counter()
-    try:
-        yield
-    finally:
-        duration = time.perf_counter() - start_time
-
-        # Check if should log based on config
-        if config.should_log(func_name, duration):
-            # Check if SwanLab is enabled
-            use_swanlab = getattr(getattr(trainer, "cfg", None), "use_swanlab", False)
-            if use_swanlab:
-                try:
-                    import swanlab
-
-                    if swanlab.get_run() is not None:
-                        trainer_class = trainer.__class__.__name__
-                        metric_name = (
-                            f"profiling/Time taken: {trainer_class}.{func_name}"
-                        )
-
-                        swanlab.log({metric_name: duration})
-
-                except ImportError:
-                    pass
-                except Exception as err:  # pylint: disable=broad-except
-                    LOG.debug(f"Failed to log profiling metric for {func_name}: {err}")

src/axolotl/loaders/model.py

@@ -26,6 +26,7 @@ from torch.distributed import DeviceMesh
 from transformers import (
     AutoModelForCausalLM,
     AutoModelForImageTextToText,
+    AutoModelForVision2Seq,
     AwqConfig,
     BitsAndBytesConfig,
     GPTQConfig,
@@ -225,7 +226,6 @@ class ModelLoader:
         ):
             self.model = self.model.merge_and_unload()
 
-        self._configure_experts_implementation()
         self._apply_activation_checkpointing()
         self._resize_token_embeddings()
         self._adjust_model_config()
@@ -233,10 +233,6 @@ class ModelLoader:
         self._configure_qat()
         log_gpu_memory_usage(LOG, "Memory usage after model load", 0)
 
-    def _configure_experts_implementation(self):
-        if self.cfg.experts_implementation is not None:
-            self.model.set_experts_implementation(self.cfg.experts_implementation)
-
     def _apply_activation_checkpointing(self):
         if self.cfg.activation_offloading is True:
             from axolotl.core.trainers.mixins.activation_checkpointing import (
@@ -338,12 +334,7 @@ class ModelLoader:
             # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so
             # we need to convert them back to fp16/bf16 for flash-attn compatibility.
             (
-                (
-                    needs_fa2_dtype
-                    or self.cfg.flash_attention
-                    or self.cfg.flex_attention
-                    or self.cfg.sage_attention
-                )
+                (needs_fa2_dtype or self.cfg.flash_attention or self.cfg.flex_attention)
                 and not self.is_qlora_and_fsdp_enabled
             )
             or (
@@ -443,7 +434,7 @@ class ModelLoader:
         """
         if self.cfg.is_multimodal:
             self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
-                self.model_config.model_type, AutoModelForImageTextToText
+                self.model_config.model_type, AutoModelForVision2Seq
             )
             if isinstance(self.auto_model_loader, str):
                 self.auto_model_loader = AutoModelForImageTextToText
@@ -485,7 +476,6 @@ class ModelLoader:
             max_memory = None
 
         self.model_kwargs["torch_dtype"] = self.cfg.torch_dtype
-        self.model_kwargs["dtype"] = self.cfg.torch_dtype
 
         is_ds_zero3 = is_deepspeed_zero3_enabled()
 
@@ -617,10 +607,6 @@ class ModelLoader:
         elif self.cfg.sdp_attention:
             self.model_kwargs["attn_implementation"] = "sdpa"
             self.model_config._attn_implementation = "sdpa"
-        elif self.cfg.sage_attention:
-            # sets FA2 attention to re-use same internal handling like masking
-            self.model_kwargs["attn_implementation"] = "flash_attention_2"
-            self.model_config._attn_implementation = "flash_attention_2"
         elif self.cfg.eager_attention:
             self.model_kwargs["attn_implementation"] = "eager"
             self.model_config._attn_implementation = "eager"
@@ -684,7 +670,7 @@ class ModelLoader:
         Uses the selected loader when provided; otherwise falls back to the auto loader.
         """
         loader = model_loader_class or self.auto_model_loader
-        if loader in [AutoModelForCausalLM, AutoModelForImageTextToText]:
+        if loader in [AutoModelForCausalLM, AutoModelForVision2Seq]:
             model = loader.from_config(
                 config=self.model_config,
                 trust_remote_code=self.cfg.trust_remote_code or False,
@@ -802,7 +788,6 @@ class ModelLoader:
                 # Use auto model loader (handles gptq and default cases)
                 model_loader_class = self.auto_model_loader
 
-            self.model_kwargs["dtype"] = self.model_kwargs["torch_dtype"]
             if self.cfg.reinit_weights:
                 self.model = self._load_model_from_config(model_loader_class)
             else:

src/axolotl/loaders/patch_manager.py

@@ -10,7 +10,6 @@ from functools import cached_property
 import addict
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
-from transformers.modeling_flash_attention_utils import is_flash_attn_available
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import (
@@ -97,7 +96,6 @@ class PatchManager:
         # self._apply_flex_attention_patches()
         self._apply_flash_attention_patches()
         self._apply_chunked_cross_entropy_patch()
-        self._apply_sageattn_patches()
         self._apply_fsdp_patches()
         self._apply_adapter_patches()
         self._apply_model_specific_patches()
@@ -140,7 +138,6 @@ class PatchManager:
         self._apply_llama_flash_attn_patches(model)
         self._apply_unsloth_patches(model)
         self._apply_lora_kernel_patch(model)
-        self._apply_scaling_softmax_patch(model)
 
     def _apply_flash_attention_patches(self):
         """Apply patches related to Flash Attention."""
@@ -203,13 +200,6 @@ class PatchManager:
             flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
             patch_flex_wrapper(**flex_attn_compile_kwargs)
 
-    def _apply_sageattn_patches(self):
-        """Apply patches for SageAttention."""
-        if self.cfg.sage_attention:
-            from axolotl.monkeypatch.attention.sage_attn import patch_sageattn
-
-            patch_sageattn()
-
     def _apply_model_specific_patches(self):
         """Apply patches specific to model architectures."""
         if (
@@ -229,6 +219,13 @@ class PatchManager:
 
             patch_qwen3_next_modeling_packing()
 
+        if self.cfg.model_config_type == "mistral3" and self.cfg.processor_type:
+            from axolotl.monkeypatch.models.mistral3.mistral_common_tokenizer import (
+                apply_mistral_tokenizer_image_patch,
+            )
+
+            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,
@@ -501,7 +498,6 @@ class PatchManager:
             and not self.cfg.trust_remote_code
             and not self.cfg.gptq
             and self.cfg.flash_attention
-            and is_flash_attn_available()
             and not self.inference
         ):
             # TODO(MengqingCao): split these patches separately
@@ -564,16 +560,3 @@ class PatchManager:
             )
 
             patch_apertus_xielu_activation()
-
-    def _apply_scaling_softmax_patch(self, model: PreTrainedModel):
-        """Apply Scaling Softmax (SSMax) patch.  Ref: https://arxiv.org/abs/2501.19399"""
-        if self.cfg.scaling_softmax:
-            from axolotl.monkeypatch.scaled_softmax_attn import (
-                patch_scaled_softmax_attention,
-            )
-
-            patch_scaled_softmax_attention(
-                scaling_factor_init=self.cfg.scaling_softmax_factor or 0.43,
-                bias=self.cfg.scaling_softmax_bias or 0.0,
-                model=model,
-            )

src/axolotl/loaders/processor.py

@@ -31,7 +31,7 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
 
             from axolotl.utils.mistral import HFMistralTokenizer
 
-            tokenization_mistral_common.MistralCommonBackend = HFMistralTokenizer
+            tokenization_mistral_common.MistralCommonTokenizer = HFMistralTokenizer
 
         _patch_mistralcommontokenizer()
 

src/axolotl/loaders/utils.py

@@ -5,7 +5,6 @@ from typing import Type
 
 import addict
 import torch
-import transformers
 from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
 
 from axolotl.utils.dict import DictDefault
@@ -154,9 +153,6 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     This function determines the appropriate model config source, loads it, applies any
     necessary overrides, and validates it for compatibility with the `axolotl` config.
 
-    If `cfg.cls_model_config` is set, a custom config class from transformers will be
-    used instead of `AutoConfig` (e.g., 'LlamaConfig', 'MistralConfig').
-
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
 
@@ -178,13 +174,8 @@ def load_model_config(cfg: DictDefault) -> PretrainedConfig | addict.Dict:
     if cfg.num_labels:
         # num_labels is used to initialize classifier models
         config_kwargs["num_labels"] = cfg.num_labels
-
-    config_cls = AutoConfig
-    if cfg.cls_model_config:
-        config_cls = getattr(transformers, cfg.cls_model_config)
-
     try:
-        model_config = config_cls.from_pretrained(
+        model_config = AutoConfig.from_pretrained(
             model_config_name,
             trust_remote_code=trust_remote_code,
             **config_kwargs,

src/axolotl/models/mamba/modeling_mamba.py

@@ -111,6 +111,7 @@ class MambaLMHeadModel(nn.Module, GenerationMixin):
         self,
         save_directory: Union[str, os.PathLike],
         state_dict: Optional[dict] = None,
+        safe_serialization: Optional[bool] = None,
     ):
         if state_dict is None:
             state_dict = self.state_dict()

src/axolotl/monkeypatch/attention/sage_attn.py

@@ -1,211 +0,0 @@
-"""
-Monkeypatch for SageAttention for use with transformers.
-
-https://github.com/thu-ml/SageAttention/
-"""
-
-import torch
-from transformers.integrations.sdpa_attention import repeat_kv
-
-from axolotl.utils.logging import get_logger
-
-LOG = get_logger(__name__)
-
-sageattn = None  # pylint: disable=invalid-name
-sageattn_varlen = None  # pylint: disable=invalid-name
-
-
-def _is_sageattn_available():
-    """Determine if SageAttention is available"""
-    try:
-        import sageattention  # noqa: F401 # pylint: disable=unused-import
-
-        return True
-    except ImportError:
-        return False
-
-
-if _is_sageattn_available():
-    # import sageattn here if available
-    from sageattention import sageattn, sageattn_varlen
-
-
-def _check_sageattn_imported():
-    """Check if SageAttention is imported. Raises an ImportError if not."""
-    if sageattn is None:
-        raise ImportError(
-            "SageAttention is not installed. Please install it from source: "
-            "`pip install git+https://github.com/thu-ml/SageAttention.git@1718ddc06dbc694bcf3c6b49ac28c1921aa2d8bd`"
-        )
-
-
-def sage_attention_forward(
-    module: torch.nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: torch.Tensor | None = None,
-    dropout: float = 0.0,
-    scaling: float | None = None,
-    is_causal: bool | None = None,
-    **kwargs,
-) -> tuple[torch.Tensor, None]:
-    """
-    Forward pass for SageAttention compatible with transformers attention interfaces.
-
-    https://github.com/thu-ml/SageAttention/
-    """
-
-    _check_sageattn_imported()
-
-    if kwargs.get("output_attentions", False) or kwargs.get("head_mask") is not None:
-        raise NotImplementedError(
-            "SageAttention does not support `output_attentions=True` or `head_mask`."
-        )
-
-    # The base sageattn API does not support dropout.
-    if dropout > 0.0:
-        raise NotImplementedError("SageAttention does not support dropout.")
-
-    # Handle Grouped-Query Attention (GQA) and Multi-Query Attention (MQA)
-    if hasattr(module, "num_key_value_groups"):
-        key = repeat_kv(key, module.num_key_value_groups)
-        value = repeat_kv(value, module.num_key_value_groups)
-
-    # Calculate is_causal following transformers
-    assert is_causal is not False, "is_causal must be True or None"
-    is_causal = True
-
-    position_ids = kwargs.get("position_ids", None)
-    query_length = query.shape[2]
-
-    cu_seqlens_q = kwargs.get("cu_seqlens_q", None)
-    cu_seqlens_k = kwargs.get("cu_seqlens_k", None)
-    max_length_q = kwargs.get("max_length_q", None)
-    max_length_k = kwargs.get("max_length_k", None)
-
-    # Sample packing uses position_ids, so we check for it first
-    if position_ids is not None and (
-        max_length_q is not None
-        or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all())
-    ):
-        # transpose inputs to NHD layout for use with FA2 utils
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        batch_size = query.size(0)
-
-        from transformers.modeling_flash_attention_utils import (
-            prepare_fa2_from_position_ids,
-        )
-
-        if cu_seqlens_q is None or cu_seqlens_k is None:
-            query, key, value, indices_q, cu_seq_lens, max_seq_lens = (
-                prepare_fa2_from_position_ids(query, key, value, position_ids)
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_length_q, max_length_k = max_seq_lens
-
-        else:
-            query = query.reshape(-1, query.size(-2), query.size(-1))
-            key = key.reshape(-1, key.size(-2), key.size(-1))
-            value = value.reshape(-1, value.size(-2), value.size(-1))
-
-        attn_output_unpad = sageattn_varlen(
-            q=query,
-            k=key,
-            v=value,
-            cu_seqlens_q=cu_seqlens_q,
-            cu_seqlens_k=cu_seqlens_k,
-            max_seqlen_q=max_length_q,
-            max_seqlen_k=max_length_k,
-            is_causal=is_causal,
-            sm_scale=scaling,
-            smooth_k=False,  # reduces loss 0 / nan grad norms
-            tensor_layout="NHD",
-        )
-
-        attn_output = attn_output_unpad.view(
-            batch_size, -1, attn_output_unpad.size(-2), attn_output_unpad.size(-1)
-        )
-
-    elif attention_mask is not None:
-        # NOTE: When used without `pad_to_sequence_len`, the loss becomes unstable after a few steps.
-
-        assert attention_mask.ndim == 2, "Attention mask must be 2D"
-
-        from transformers.modeling_flash_attention_utils import (
-            _upad_input,
-        )
-
-        # transpose inputs to NHD layout for use with FA2 utils
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        batch_size = query.shape[0]
-
-        query, key, value, indices_q, cu_seq_lens, max_seq_lens = _upad_input(
-            query, key, value, attention_mask, query_length
-        )
-        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-        max_seqlen_q, max_seqlen_k = max_seq_lens
-
-        attn_output_unpad = sageattn_varlen(
-            q=query,
-            k=key,
-            v=value,
-            cu_seqlens_q=cu_seqlens_q,
-            cu_seqlens_k=cu_seqlens_k,
-            max_seqlen_q=max_seqlen_q,
-            max_seqlen_k=max_seqlen_k,
-            is_causal=is_causal,
-            sm_scale=scaling,
-            tensor_layout="NHD",
-        )
-
-        from flash_attn.bert_padding import pad_input
-
-        attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-    else:
-        # Use standard sageattn
-        # The input layout for transformers models is (batch_size, num_heads, seq_len, head_dim),
-        # which corresponds to SageAttention's "HND" layout.
-        attn_output = sageattn(
-            q=query,
-            k=key,
-            v=value,
-            tensor_layout="HND",
-            is_causal=is_causal,
-            sm_scale=scaling,
-        )
-
-        # SageAttention with "HND" returns (batch, heads, seq_len, head_dim)
-        # Transformers expects (batch, seq_len, heads, head_dim) for the output
-        # So we need to transpose dimensions 1 and 2
-        attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, None
-
-
-def patch_sageattn():
-    """Patch SageAttention for use with transformers."""
-
-    _check_sageattn_imported()
-
-    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-    # Replace flash attention with sage attention
-    ALL_ATTENTION_FUNCTIONS.register("flash_attention_2", sage_attention_forward)
-
-    # Note: New method after transformers refactor to use ALL_MASK_ATTENTION_FUNCTIONS
-    # Register sage_attention with the global attention interface
-    # ALL_ATTENTION_FUNCTIONS.register("sage_attention", sage_attention_forward)
-
-    # from transformers.masking_utils import ALL_MASK_ATTENTION_FUNCTIONS, flash_attention_mask
-
-    # ALL_MASK_ATTENTION_FUNCTIONS.register("sage_attention", flash_attention_mask)
-
-    LOG.info("SageAttention patched successfully")

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -59,12 +59,7 @@ class CPU_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
         hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
         hidden_states.requires_grad = True
         with torch.enable_grad():
-            output = ctx.forward_function(hidden_states, *ctx.args)
-            # Newer HF models (e.g. Qwen3MoE) using GradientCheckpointingLayer
-            # return a plain tensor, not a tuple.  Older models return tuples
-            # like (hidden_states, present_kv, ...).  Unwrap if needed.
-            if isinstance(output, (tuple, list)):
-                (output,) = output
+            (output,) = ctx.forward_function(hidden_states, *ctx.args)
         torch.autograd.backward(output, dY)
         return (
             None,