📝 Add docstrings to feat/qwen3-vl-liger-integration

Docstrings generation was requested by @thad0ctor.

* https://github.com/axolotl-ai-cloud/axolotl/pull/3285#issuecomment-3593043121

The following files were modified:

* `src/axolotl/integrations/liger/plugin.py`

.github/FUNDING.yml

@@ -1,13 +1,13 @@
 # These are supported funding model platforms
 
-github: [winglian, OpenAccess-AI-Collective] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
-ko_fi: axolotl_ai # Replace with a single Ko-fi username
+ko_fi: # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
-custom: ['https://quickchart.io/qr?text=bitcoin%3Abc1qxlgwlqwfea5s2cxm42xqsfmwjct0rj8w8ea5np&size=480&centerImageUrl=https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2Fthumb%2F4%2F46%2FBitcoin.svg%2F64px-Bitcoin.svg.png'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

.github/workflows/base.yml

@@ -25,20 +25,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
@@ -67,6 +53,20 @@ 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"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            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"
+          - cuda: "130"
+            cuda_version: 13.0.0
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.1
+            torch_cuda_arch_list: "9.0+PTX"
+            dockerfile: "Dockerfile-base"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -90,7 +90,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl-base
             axolotlai/axolotl-base
       - name: Login to Docker Hub
         uses: docker/login-action@v2
@@ -122,13 +121,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
@@ -150,6 +142,20 @@ 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"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
+          - cuda: "130"
+            cuda_version: 13.0.0
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.1
+            torch_cuda_arch_list: "9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -15,11 +15,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -30,7 +25,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras: vllm
-            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -41,6 +35,17 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
+            is_latest: true
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.0
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -50,7 +55,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl
             axolotlai/axolotl
           tags: |
             type=ref,event=branch
@@ -88,11 +92,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -109,7 +108,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras: vllm
-            is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -120,6 +118,17 @@ jobs:
             python_version: "3.11"
             pytorch: 2.8.0
             axolotl_extras:
+            is_latest: true
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.0
+            axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.1
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -129,7 +138,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl-cloud
             axolotlai/axolotl-cloud
           tags: |
             type=ref,event=branch
@@ -162,11 +170,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -194,7 +197,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl-cloud-term
             axolotlai/axolotl-cloud-term
           tags: |
             type=ref,event=branch

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

@@ -26,13 +26,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -47,6 +40,13 @@ jobs:
             axolotl_extras: fbgemm-gpu
             num_gpus: 2
             nightly_build: "true"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.0
+            axolotl_extras: fbgemm-gpu
+            num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:

.github/workflows/nightlies.yml

@@ -12,16 +12,16 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -31,7 +31,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl
             axolotlai/axolotl
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}
@@ -65,16 +64,16 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -84,7 +83,6 @@ jobs:
         uses: docker/metadata-action@v5
         with:
           images: |
-            winglian/axolotl-cloud
             axolotlai/axolotl-cloud
           tags: |
             type=raw,value={{ branch }}-{{ date 'YYYYMMDD' }}

.github/workflows/precommit-autoupdate.yml

@@ -2,7 +2,7 @@ name: Pre-commit auto-update
 
 on:
   schedule:
-    - cron: '0 0 * * 0'  # Run weekly
+    - cron: '0 0 1 * *'  # Run monthly
   workflow_dispatch:  # Manual kickoff
 
 jobs:

.github/workflows/tests-nightly.yml

@@ -26,7 +26,7 @@ jobs:
       max-parallel: 2
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.0"]
+        pytorch_version: ["2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -102,14 +102,14 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.8.0
             num_gpus: 1
             axolotl_extras:
             nightly_build: "true"

.github/workflows/tests.yml

@@ -55,10 +55,14 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
+        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
     timeout-minutes: 20
 
     steps:
+      - name: cleanup node
+        run: |
+          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
+
       - name: Check out repository code
         uses: actions/checkout@v4
 
@@ -81,16 +85,20 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
           pip3 show torch
-          pip3 install --no-build-isolation -U -e .
+          pip3 install --no-cache-dir --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
       - name: Make sure PyTorch version wasn't clobbered
         run: |
           python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
@@ -118,10 +126,6 @@ jobs:
           flags: unittests,pytorch-${{ matrix.pytorch_version }}
           fail_ci_if_error: false
 
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
   pytest-sdist:
     name: PyTest from Source Dist
     runs-on: ubuntu-latest
@@ -130,10 +134,14 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
+        pytorch_version: ["2.7.1", "2.8.0", "2.9.0"]
     timeout-minutes: 20
 
     steps:
+      - name: cleanup node
+        run: |
+          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
+
       - name: Check out repository code
         uses: actions/checkout@v4
 
@@ -152,21 +160,25 @@ jobs:
       - name: upgrade pip
         run: |
           pip3 install --upgrade pip
-          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel
+          pip3 install --upgrade packaging==23.2 setuptools==75.8.0 setuptools_scm build wheel psutil
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
           pip3 show torch
           python -m build --no-isolation --sdist
-          pip3 install --no-build-isolation dist/axolotl*.tar.gz
+          pip3 install --no-cache-dir --no-build-isolation dist/axolotl*.tar.gz
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
 
+      - name: cleanup pip cache
+        run: |
+          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
+
       - name: Make sure PyTorch version wasn't clobbered
         run: |
           python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
@@ -184,10 +196,6 @@ jobs:
           pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/
 
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
-
   gate-skip-e2e:
     needs: [pre-commit, pytest, pytest-sdist]
     runs-on: ubuntu-latest
@@ -231,16 +239,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 128
+            cuda_version: 12.8.1
             python_version: "3.11"
-            pytorch: 2.7.1
-            num_gpus: 1
-            axolotl_extras:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.7.1
+            pytorch: 2.8.0
             num_gpus: 1
             axolotl_extras:
             dockerfile: "Dockerfile-uv.jinja"
@@ -289,15 +291,15 @@ jobs:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
             pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
+#          - cuda: 128
+#            cuda_version: 12.8.1
+#            python_version: "3.11"
+#            pytorch: 2.7.1
+#            num_gpus: 1
+#            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -305,6 +307,12 @@ jobs:
             num_gpus: 1
             gpu_type: "B200"
             axolotl_extras: fbgemm-gpu
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.9.0
+            num_gpus: 1
+            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.pre-commit-config.yaml

@@ -11,13 +11,13 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.12
+    rev: v0.14.3
     hooks:
     -   id: ruff
         args: [--fix]
     -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.1
+    rev: v1.18.2
     hooks:
     - id: mypy
       additional_dependencies:

README.md

@@ -29,6 +29,10 @@
 
 ## 🎉 Latest Updates
 
+- 2025/11: Axolotl now includes support for [Olmo3](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/olmo3).
+- 2025/10: New model support has been added in Axolotl for: [Qwen3 Next](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/qwen3-next), [Qwen2.5-vl, Qwen3-vl](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen2_5-vl), [Qwen3, Qwen3MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3), [Granite 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/granite4), [HunYuan](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/hunyuan), [Magistral 2509](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral#vision), [Apertus](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/apertus), and [Seed-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/seed-oss).
+- 2025/09: Axolotl now has text diffusion training. Read more [here](https://github.com/axolotl-ai-cloud/axolotl/tree/main/src/axolotl/integrations/diffusion).
+- 2025/08: QAT has been updated to include NVFP4 support. See [PR](https://github.com/axolotl-ai-cloud/axolotl/pull/3107).
 - 2025/07:
   - ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the [blog post](https://huggingface.co/blog/accelerate-nd-parallel) for more info.
   - Axolotl adds more models: [GPT-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss), [Gemma 3n](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n), [Liquid Foundation Model 2 (LFM2)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2), and [Arcee Foundation Models (AFM)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm).
@@ -36,12 +40,12 @@
   - [Voxtral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral), [Magistral 1.1](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral), and [Devstral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral) with mistral-common tokenizer support has been integrated in Axolotl!
   - TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!
 - 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!
-- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 
 <details>
 
 <summary>Expand older updates</summary>
 
+- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.
 - 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!
 - 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version!
 - 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!
@@ -73,7 +77,7 @@ Features:
 
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
 - Python 3.11
-- PyTorch ≥2.6.0
+- PyTorch ≥2.7.1
 
 ### Google Colab
 
@@ -154,6 +158,13 @@ That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/ge
 
 Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details.
 
+## 📈 Telemetry
+
+Axolotl has opt-out telemetry that helps us understand how the project is being used
+and prioritize improvements. We collect basic system information, model types, and
+error rates—never personal data or file paths. Telemetry is enabled by default. To
+disable it, set AXOLOTL_DO_NOT_TRACK=1. For more details, see our [telemetry documentation](https://docs.axolotl.ai/docs/telemetry.html).
+
 ## ❤️ Sponsors
 
 Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)

_quarto.yml

@@ -241,6 +241,7 @@ website:
             - docs/installation.qmd
             - docs/inference.qmd
             - docs/cli.qmd
+            - docs/telemetry.qmd
             - docs/config-reference.qmd
             - text: "API Reference"
               href: docs/api
@@ -267,6 +268,7 @@ website:
             - docs/dataset_loading.qmd
             - docs/qat.qmd
             - docs/quantize.qmd
+            - docs/optimizations.qmd
 
         - section: "Core Concepts"
           contents:

cicd/Dockerfile-uv.jinja

@@ -32,6 +32,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 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; \
     else \

cicd/Dockerfile.jinja

@@ -1,6 +1,6 @@
 FROM axolotlai/axolotl-base:{{ BASE_TAG }}
 
-ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
 ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
 ENV CUDA="{{ CUDA }}"
@@ -9,7 +9,7 @@ ENV GITHUB_REF="{{ GITHUB_REF }}"
 ENV GITHUB_SHA="{{ GITHUB_SHA }}"
 ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 ENV HF_HOME="{{ HF_HOME }}"
-ENV AXOLOTL_DATASET_PROCESSES="8"
+ENV AXOLOTL_DATASET_NUM_PROC="8"
 
 RUN apt-get update && \
     apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
@@ -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==23.2 setuptools==75.8.0
+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/single_gpu.py

@@ -65,8 +65,13 @@ def run_cmd(cmd: str, run_folder: str):
     import subprocess  # nosec
 
     sp_env = os.environ.copy()
-    sp_env["AXOLOTL_DATASET_PROCESSES"] = "8"
+    sp_env["AXOLOTL_DATASET_NUM_PROC"] = "8"
 
     # Propagate errors from subprocess.
-    if exit_code := subprocess.call(cmd.split(), cwd=run_folder, env=sp_env):  # nosec
-        exit(exit_code)
+    try:
+        exit_code = subprocess.call(cmd.split(), cwd=run_folder, env=sp_env)  # nosec
+        if exit_code:
+            print(f"Command '{cmd}' failed with exit code {exit_code}")
+            return exit_code
+    except Exception as e:  # pylint: disable=broad-except
+        print(f"Command '{cmd}' failed with exception {e}")

devtools/dev_chat_template.yml

@@ -13,7 +13,7 @@ datasets:
 val_set_size: 0
 output_dir: temp_debug/axolotl_outputs/model
 dataset_prepared_path: temp_debug/axolotl_outputs/data
-dataset_processes: 1
+dataset_num_proc: 1
 
 sequence_len: 4096
 sample_packing: false

docker/Dockerfile-base

@@ -35,18 +35,24 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 
-RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
+RUN python3 -m pip install --upgrade pip && pip3 install -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 && \
-    CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir causal_conv1d==1.5.2 && \
-    python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
     python3 -m pip cache purge
 
+RUN if [ "$CUDA" != "130" ] ; then \
+        CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@v1.5.4"; \
+        python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"; \
+        python3 -m pip cache purge; \
+    fi
+
 RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
-        FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \
+RUN if [ "$PYTORCH_VERSION" = "2.9.1" ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        pip3 install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
     fi

docker/Dockerfile-uv-base

@@ -30,7 +30,13 @@ RUN uv venv --no-project --relocatable axolotl-venv
 ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
 RUN uv pip install packaging setuptools wheel psutil \
-    && uv pip install torch==${PYTORCH_VERSION} \
+    && uv pip install torch==${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 [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$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

docs/dataset-formats/conversation.qmd

@@ -212,6 +212,21 @@ Instead of passing `tools` via the system prompt, an alternative method would be
 Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
 :::
 
+::: {.callout-warning}
+If you have tool arguments with same name but different dtypes (like `"time": string` and `"time": number`), please save `arguments: ` as JSON string to prevent `datasets` from having casting issues.
+
+```
+"arguments": "{\"...\": \"...\"}"
+```
+
+The same is applicable for tool parameters.
+
+```
+"parameters": "{\"...\": \"...\"}"
+```
+
+:::
+
 Example config for Llama4:
 ```yaml
 chat_template: llama4

docs/dataset-formats/index.qmd

@@ -61,7 +61,7 @@ While we recommend `.jsonl`, you can also use the other formats (`csv`, `parquet
 
 ### Pre-training without streaming
 
-On the rare case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
+In the case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
 
 One benefit of this is that the tokenization can be performed separately on a CPU-only machine, and then transferred to a GPU machine for training to save costs.
 

docs/debugging.qmd

@@ -29,7 +29,7 @@ While debugging it's helpful to simplify your test scenario as much as possible.
 1. **Make sure you are using the latest version of axolotl**:  This project changes often and bugs get fixed fast.  Check your git branch and make sure you have pulled the latest changes from `main`.
 1. **Eliminate concurrency**: Restrict the number of processes to 1 for both training and data preprocessing:
     - Set `CUDA_VISIBLE_DEVICES` to a single GPU, ex: `export CUDA_VISIBLE_DEVICES=0`.
-    - Set `dataset_processes: 1` in your axolotl config or run the training command with `--dataset_processes=1`.
+    - Set `dataset_num_proc: 1` in your axolotl config or run the training command with `--dataset_num_proc=1`.
 2. **Use a small dataset**: Construct or use a small dataset from HF Hub. When using a small dataset, you will often have to make sure `sample_packing: False` and `eval_sample_packing: False` to avoid errors.  If you are in a pinch and don't have time to construct a small dataset but want to use from the HF Hub, you can shard the data (this will still tokenize the entire dataset, but will only use a fraction of the data for training.  For example, to shard the dataset into 20 pieces, add the following to your axolotl config):
 
     ```yaml
@@ -101,7 +101,7 @@ For example, to mimic the command `cd devtools && CUDA_VISIBLE_DEVICES=0 acceler
                 "-m", "axolotl.cli.train", "dev_chat_template.yml",
                 // The flags below simplify debugging by overriding the axolotl config
                 // with the debugging tips above.  Modify as needed.
-                "--dataset_processes=1",      // limits data preprocessing to one process
+                "--dataset_num_proc=1",      // limits data preprocessing to one process
                 "--max_steps=1",              // limits training to just one step
                 "--batch_size=1",             // minimizes batch size
                 "--micro_batch_size=1",       // minimizes batch size

docs/faq.qmd

@@ -63,6 +63,14 @@ description: Frequently asked questions
 
 > A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717.
 
+**Q: Can we mix text and text+image datasets for VLM training?**
+
+> A: Yes, you can for newer VLM arch. The ones that would not work are LLaVA / Pixtral arch. If you notice one not working, please let us know!
+
+**Q: Why is `memory/max_*` different from `nvidia-smi`?**
+
+> A: We use `torch` APIs to retrieve this information. You can see https://docs.pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management for more information.
+
 ### Chat templates
 
 **Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
@@ -140,3 +148,7 @@ description: Frequently asked questions
 **Q: `ValueError("Backward pass should have cleared tracker of all tensors")`
 
 > A: This may happen due to edge cases in using the modern OffloadActivations context manager for CUDA streams. If you encounter this error, you may have success using the naive implementation with `offload_activations: legacy` in your YAML.
+
+**Q: `Error parsing tool_calls arguments as JSON.`
+
+> A: There is an error parsing string arguments to a dict. Please check your dataset and the error message for more details.

docs/fsdp_qlora.qmd

@@ -1,5 +1,5 @@
 ---
-title: "FDSP + QLoRA"
+title: "FSDP + QLoRA"
 description: Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
 format:
   html:
@@ -23,6 +23,12 @@ To enable `QLoRA` with `FSDP`, you need to perform the following steps:
 2. Enable FSDP in your axolotl config, as [described here](multi-gpu.qmd#sec-fsdp).
 3. Use one of the supported model types: `llama`, `mistral` or `mixtral`.
 
+## Enabling Swap for FSDP2
+
+If available memory is insufficient even after FSDP's CPU offloading, you can enable swap memory usage by setting `cpu_offload_pin_memory: false` alongside `offload_params: true` in FSDP config.
+
+This disables memory pinning, allowing FSDP to use disk swap space as fallback. Disabling memory pinning itself incurs performance overhead, and actually having to use swap adds more, but it may enable training larger models that would otherwise cause OOM errors on resource constrained systems.
+
 ## Example Config
 
 [examples/llama-2/qlora-fsdp.yml](../examples/llama-2/qlora-fsdp.yml) contains an example of how to enable QLoRA + FSDP in axolotl.

docs/lora_optims.qmd

@@ -5,10 +5,11 @@ description: "Custom autograd functions and Triton kernels in Axolotl for optimi
 
 Inspired by [Unsloth](https://github.com/unslothai/unsloth), we've implemented two
 optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU
-(in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function
-Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was
-to leverage operator fusion and tensor re-use in order to improve speed and reduce
-memory usage during the forward and backward passes of these calculations.
+(including the DDP, DeepSpeed, and FSDP2 settings) training. These include (1) SwiGLU
+and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom
+autograd functions. Our goal was to leverage operator fusion and tensor re-use in order
+to improve speed and reduce memory usage during the forward and backward passes of
+these calculations.
 
 We currently support several common model architectures, including (but not limited to):
 
@@ -131,6 +132,5 @@ computation path.
 ## Future Work
 
 - Support for additional model architectures
-- Support for the FSDP setting
 - Support for dropout and bias
 - Additional operator fusions

docs/lr_groups.qmd

@@ -27,3 +27,9 @@ learning_rate: 2e-5
 In this example, we have a default learning rate of 2e-5 across the entire model, but we have a separate learning rate
 of 1e-6 for all the self attention `o_proj` modules across all layers, and a learning are of 1e-5 to the 3rd layer's
 self attention `q_proj` module.
+
+::: {.callout-note}
+
+We currently only support varying `lr` for now. If you're interested in adding support for others (`weight_decay`), we welcome PRs. See https://github.com/axolotl-ai-cloud/axolotl/blob/613bcf90e58f3ab81d3827e7fc572319908db9fb/src/axolotl/core/trainers/mixins/optimizer.py#L17
+
+:::

docs/multi-gpu.qmd

@@ -4,7 +4,7 @@ format:
   html:
     toc: true
     toc-depth: 3
-    number-sections: true
+    # number-sections: true
     code-tools: true
 execute:
   enabled: false
@@ -14,12 +14,18 @@ This guide covers advanced training configurations for multi-GPU setups using Ax
 
 ## Overview {#sec-overview}
 
-Axolotl supports several methods for multi-GPU training:
+When training on multiple GPUs, Axolotl supports 3 sharding/parallelism strategies. Additionally, you can layer specific optimization features on top of that strategy.
 
-- DeepSpeed (recommended)
-- FSDP (Fully Sharded Data Parallel)
-- Sequence parallelism
-- FSDP + QLoRA
+You generally cannot combine these strategies; they are mutually exclusive.
+
+1.  **DeepSpeed**: Powerful optimization library, supports ZeRO stages 1-3.
+2.  **FSDP (Fully Sharded Data Parallel)**: PyTorch's native sharding implementation (Recommended).
+3.  **DDP (Distributed Data Parallel)**: PyTorch's native parallelism implementation (Default if neither of the above are selected).
+
+These features can often be combined with the strategies above:
+
+*   **Sequence Parallelism**: Splits long sequences across GPUs (Compatible with DDP, DeepSpeed, and FSDP).
+*   **FSDP + QLoRA**: Combines 4-bit quantization with FSDP (Specific to FSDP).
 
 ## DeepSpeed {#sec-deepspeed}
 
@@ -65,12 +71,18 @@ Start from Stage 1 -> Stage 2 -> Stage 3.
 
 ## Fully Sharded Data Parallel (FSDP) {#sec-fsdp}
 
+FSDP allows you to shard model parameters, gradients, and optimizer states across data parallel workers.
+
 ::: {.callout-note}
 
 FSDP2 is recommended for new users. FSDP1 is deprecated and will be removed in an upcoming release of Axolotl.
 
 :::
 
+### FSDP + QLoRA {#sec-fsdp-qlora}
+
+For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
+
 ### Migrating from FSDP1 to FSDP2 {#sec-migrate-fsdp1-fsdp2}
 
 To migrate your config from FSDP1 to FSDP2, you must use the `fsdp_version` top-level config field to specify the FSDP version, and
@@ -88,6 +100,7 @@ fsdp_sync_module_states | **REMOVED**
 fsdp_cpu_ram_efficient_loading | cpu_ram_efficient_loading
 fsdp_state_dict_type | state_dict_type
 fsdp_use_orig_params | **REMOVED**
+fsdp_activation_checkpointing | activation_checkpointing
 
 For more details, please see the migration guide in the [torchtitan repo](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md). In Axolotl,
 if you were using the following FSDP1 config:
@@ -144,10 +157,6 @@ single sequence causes OOM errors during model training.
 
 See our [dedicated guide](sequence_parallelism.qmd) for more information.
 
-### FSDP + QLoRA {#sec-fsdp-qlora}
-
-For combining FSDP with QLoRA, see our [dedicated guide](fsdp_qlora.qmd).
-
 ## Performance Optimization {#sec-performance}
 
 ### Liger Kernel Integration {#sec-liger}

docs/multimodal.qmd

@@ -56,10 +56,14 @@ image_resize_algorithm: bilinear
 
 Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
 
-::: {.callout-warning}
+::: {.callout-tip}
 Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
 :::
 
+::: {.callout-note}
+As of now, we do not truncate nor drop samples based on `sequence_len` as each arch has different ways to process non-text tokens. We are looking for help on this.
+:::
+
 ### Mllama {#sec-mllama}
 
 ```yaml
@@ -120,6 +124,8 @@ Please make sure to install audio lib via `pip3 install librosa==0.11.0 'mistral
 
 ```yaml
 base_model: mistralai/Voxtral-Mini-3B-2507
+
+processor_type: VoxtralProcessor
 ```
 
 ### Gemma-3 {#sec-gemma-3}
@@ -168,6 +174,14 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### Qwen3-VL {#sec-qwen3-vl}
+
+```yaml
+base_model: Qwen/Qwen3-VL-4B-Instruct
+
+chat_template: qwen2_vl  # same as qwen2-vl
+```
+
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}

docs/optimizations.qmd

@@ -0,0 +1,133 @@
+---
+title: Optimizations Guide
+description: A guide to the performance and memory optimizations available in Axolotl.
+---
+
+Axolotl includes numerous optimizations to speed up training, reduce memory usage, and handle large models.
+
+This guide provides a high-level overview and directs you to the detailed documentation for each feature.
+
+## Speed Optimizations
+
+These optimizations focus on increasing training throughput and reducing total training time.
+
+### Sample Packing
+
+Improves GPU utilization by combining multiple short sequences into a single packed sequence for training. This requires enabling one of the [attention](#attention-implementations) implementations below.
+
+- **Config:** `sample_packing: true`
+- **Learn more:** [Sample Packing](multipack.qmd)
+
+### Attention Implementations
+
+Using an optimized attention implementation is critical for training speed.
+
+- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `flash_attention: true`. **(Recommended)** The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check [AMD Support](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support).
+- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `flex_attention: true`.
+- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `sdp_attention: true`. PyTorch's native implementation.
+- **[Xformers](https://github.com/facebookresearch/xformers)**: `xformers_attention: true`. Works with FP16.
+
+*Note: You should only enable one attention backend.*
+
+### LoRA Optimizations
+
+Leverages optimized kernels to accelerate LoRA training and reduce memory usage.
+
+- **Learn more:** [LoRA Optimizations Documentation](lora_optims.qmd)
+
+## Memory Optimizations
+
+These techniques help you fit larger models or use bigger batch sizes on your existing hardware.
+
+### Parameter Efficient Finetuning (LoRA & QLoRA)
+
+Drastically reduces memory by training a small set of "adapter" parameters instead of the full model. This is the most common and effective memory-saving technique.
+
+- Examples: Find configs with `lora` or `qlora` in the [examples directory](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-3).
+- Config Reference: See `adapter`, `load_in_4bit`, and `load_in_8bit` in the [Configuration Reference](config-reference.qmd).
+
+### Gradient Checkpointing & Activation Offloading
+
+These techniques save VRAM by changing how activations are handled.
+
+- Gradient Checkpointing: re-computes activations during the backward pass, trading compute time for VRAM.
+- Activation Offloading: moves activations to CPU RAM or disk, trading I/O overhead for VRAM.
+- Learn more: [Gradient Checkpointing and Offloading Docs](gradient_checkpointing.qmd)
+
+### Cut Cross Entropy (CCE)
+
+Reduces VRAM usage by using an optimized cross-entropy loss calculation.
+
+- **Learn more:** [Custom Integrations - CCE](custom_integrations.qmd#cut-cross-entropy)
+
+### Liger Kernels
+
+Provides efficient Triton kernels to improve training speed and reduce memory usage.
+
+- **Learn more:** [Custom Integrations - Liger Kernels](custom_integrations.qmd#liger-kernels)
+
+## Long Context Models
+
+Techniques to train models on sequences longer than their original context window.
+
+### RoPE Scaling
+
+Extends a model's context window by interpolating its Rotary Position Embeddings.
+
+- **Config:** Pass the `rope_scaling` config under the `overrides_of_model_config: `. To learn how to set RoPE, check the respective model config.
+
+### Sequence Parallelism
+
+Splits long sequences across multiple GPUs, enabling training with sequence lengths that would not fit on a single device.
+
+- **Learn more:** [Sequence Parallelism Documentation](sequence_parallelism.qmd)
+
+### Artic Long Sequence Training (ALST)
+
+ALST is a recipe that combines several techniques to train long-context models efficiently. It typically involves:
+
+- TiledMLP to reduce memory usage in MLP layers.
+- Tiled Loss functions (like [CCE](#cut-cross-entropy-(cce) or [Liger](#liger-kernels)).
+- Activation Offloading to CPU.
+
+- Example: [ALST Example Configuration](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst)
+
+## Large Models (Distributed Training)
+
+To train models that don't fit on a single GPU, you'll need to use a distributed training strategy like FSDP or DeepSpeed. These frameworks shard the model weights, gradients, and optimizer states across multiple GPUs and nodes.
+
+- **Learn more:** [Multi-GPU Guide](multi-gpu.qmd)
+- **Learn more:** [Multi-Node Guide](multi-node.qmd)
+
+### N-D Parallelism (Beta)
+
+For advanced scaling, Axolotl allows you to compose different parallelism techniques (e.g., Data, Tensor, Sequence Parallelism). This is a powerful approach to train an extremely large model by overcoming multiple bottlenecks at once.
+
+- **Learn more:** [N-D Parallelism Guide](nd_parallelism.qmd)
+
+
+## Quantization
+
+Techniques to reduce the precision of model weights for memory savings.
+
+### 4-bit Training (QLoRA)
+
+The recommended approach for quantization-based training. It loads the base model in 4-bit using `bitsandbytes` and then trains QLoRA adapters. See [Adapter Finetuning](#adapter-finetuning-lora-qlora) for details.
+
+### FP8 Training
+
+Enables training with 8-bit floating point precision on supported hardware (e.g., NVIDIA Hopper series GPUs) for significant speed and memory gains.
+
+- **Example:** [Llama 3 FP8 FSDP Example](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-3/3b-fp8-fsdp2.yaml)
+
+### Quantization Aware Training (QAT)
+
+Simulates quantization effects during training, helping the model adapt and potentially improving the final accuracy of the quantized model.
+
+- **Learn more:** [QAT Documentation](qat.qmd)
+
+### GPTQ
+
+Allows you to finetune LoRA adapters on top of a model that has already been quantized using the GPTQ method.
+
+- **Example:** [GPTQ LoRA Example](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-2/gptq-lora.yml)

docs/qat.qmd

@@ -30,6 +30,7 @@ qat:
 ```
 
 We support the following quantization schemas:
+
 - `Int4WeightOnly` (requires the `fbgemm-gpu` extra when installing Axolotl)
 - `Int8DynamicActivationInt4Weight`
 - `Float8DynamicActivationFloat8Weight`

docs/rlhf.qmd

@@ -219,6 +219,21 @@ DPO supports the following types with the following dataset format:
 }
 ```
 
+#### chat_template.argilla_chat
+
+```json
+{
+    "chosen": [
+        {"role": "user", "content": "..."},
+        {"role": "assistant", "content": "..."}
+    ],
+    "rejected": [
+        {"role": "user", "content": "..."},
+        {"role": "assistant", "content": "..."}
+    ]
+}
+```
+
 #### chat_template.default
 
 ```yaml
@@ -582,6 +597,116 @@ To see other examples of custom reward functions, please see [TRL GRPO Docs](htt
 
 To see all configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/v0.9.2/src/axolotl/utils/schemas/trl.py).
 
+#### OpenEnv Rollout Functions
+
+GRPO supports custom rollout functions for OpenEnv-style environments, enabling interactive tasks like web browsing, code execution, or tool use. This allows you to implement custom generation logic that interacts with external environments.
+
+For example, to implement a simple math-solving environment with step-by-step verification:
+
+```python
+# math_env.py
+import re
+
+def math_solver_rollout(model, processing_class, prompts, generation_config=None):
+    """
+    Custom rollout function that generates step-by-step math solutions.
+
+    Args:
+        model: The language model
+        processing_class: The tokenizer/processing_class
+        prompts: List of prompt dicts (with 'messages' key for chat format)
+        generation_config: Optional generation configuration
+
+    Returns:
+        List of completion strings
+    """
+    completions = []
+
+    for prompt in prompts:
+        # Apply chat template to prompt
+        messages = prompt.get("messages", [])
+        formatted_prompt = processing_class.apply_chat_template(
+            messages, processing_class=False, add_generation_prompt=True
+        )
+
+        # Generate step-by-step solution
+        full_response = ""
+        for step in range(5):  # Max 5 reasoning steps
+            current_input = formatted_prompt + full_response + "\nNext step:"
+            inputs = processing_class(current_input, return_tensors="pt").to(model.device)
+
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=100,
+                generation_config=generation_config,
+            )
+            step_text = processing_class.decode(
+                outputs[0][inputs.input_ids.shape[1]:],
+                skip_special_tokens=True
+            )
+
+            # Check if solution is complete
+            if "FINAL ANSWER:" in step_text:
+                full_response += step_text
+                break
+            full_response += step_text + "\n"
+
+        completions.append(full_response)
+
+    return completions
+
+def math_reward(prompts, completions, answers, **kwargs):
+    """Reward function that checks mathematical correctness"""
+    rewards = []
+    for completion, correct_answer in zip(completions, answers):
+        # Extract predicted answer
+        match = re.search(r"FINAL ANSWER:\s*(.+)", completion)
+        predicted = match.group(1).strip() if match else ""
+
+        # Compare with correct answer
+        reward = 1.0 if predicted == str(correct_answer) else 0.0
+        rewards.append(reward)
+
+    return rewards
+
+def math_transform(cfg, *args, **kwargs):
+    """Transform dataset to GRPO format with answer field"""
+    def transform_fn(example, processing_class=None):
+        return {
+            "prompt": [{"role": "user", "content": example["question"]}],
+            "answer": str(example["answer"]),
+        }
+    return transform_fn, {"remove_columns": ["question"]}
+```
+
+```yaml
+rl: grpo
+
+trl:
+  beta: 0.001
+  max_completion_length: 512
+  num_generations: 4
+  rollout_func: "math_env.math_solver_rollout"  # Custom rollout function
+  reward_funcs: ["math_env.math_reward"]
+  reward_weights: [1.0]
+
+datasets:
+  - path: openai/gsm8k
+    name: main
+    type: math_env.math_transform
+```
+
+The `rollout_func` parameter accepts a fully qualified name (e.g., `module_name.function_name`) that points to a callable function in your local directory. The function receives:
+
+- `model`: The language model
+- `processing_class`: The tokenizer/processing class
+- `prompts`: List of prompt dictionaries
+- `generation_config` (optional): Generation configuration
+
+And should return a list of completion strings.
+
+For more OpenEnv examples, see [TRL OpenEnv Documentation](https://huggingface.co/docs/trl/main/en/openenv).
+
 #### GRPO with DAPO/Dr. GRPO loss
 
 The DAPO paper and subsequently Dr. GRPO paper proposed an alternative loss function for GRPO to remediate the penalty in longer responses.

docs/telemetry.qmd

@@ -0,0 +1,61 @@
+---
+title: Telemetry
+description: A description of the telemetry implementation in Axolotl.
+---
+
+# Telemetry in Axolotl
+
+Axolotl implements anonymous telemetry to help maintainers understand how the library
+is used and where users encounter issues. This data helps prioritize features, optimize
+performance, and fix bugs.
+
+## Data Collection
+
+We collect:
+
+- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
+version, etc.
+- Hardware info: CPU count, memory, GPU count and models
+- Runtime metrics: Training progress, memory usage, timing information
+- Usage patterns: Models (from a whitelist) and configurations used
+- Error tracking: Stack traces and error messages (sanitized to remove personal
+information)
+
+Personally identifiable information (PII) is not collected.
+
+## Implementation
+
+Telemetry is implemented using PostHog and consists of:
+
+- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
+telemetry system and provides methods for tracking events.
+- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
+sends sanitized stack traces.
+- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
+runtime metrics during training.
+- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
+runtime metrics telemetry.
+
+The telemetry system will block training startup for 10 seconds to ensure users are
+aware of data collection, unless telemetry is explicitly enabled or disabled.
+
+## Opt-Out Mechanism
+
+Telemetry is **enabled by default** on an opt-out basis. To disable it, set
+`AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1`.
+
+A warning message will be logged on start to clearly inform users about telemetry.
+We will remove this after some period.
+
+To hide the warning message about telemetry that is displayed on train, etc. startup,
+explicitly set: `AXOLOTL_DO_NOT_TRACK=0` (enable telemetry) or `AXOLOTL_DO_NOT_TRACK=1`
+(explicitly disable telemetry).
+
+## Privacy
+
+- All path-like config information is automatically redacted from telemetry data
+- Model information is only collected for whitelisted organizations
+    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
+- Each run generates a unique anonymous ID
+    - This allows us to link different telemetry events in a single same training run
+- Telemetry is only sent from the main process to avoid duplicate events

examples/LiquidAI/README.md

@@ -6,6 +6,8 @@ LFM2 features a new hybrid Liquid architecture with multiplicative gates, short-
 
 This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
 
+Thanks to the team at LiquidAI for giving us early access to prepare for these releases.
+
 ## Getting Started
 
 1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
@@ -31,6 +33,14 @@ This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
     axolotl train examples/LiquidAI/lfm2-vl-lora.yaml
     ```
 
+    **LFM2-MoE**
+    ```bash
+    pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
+
+    # LoRA SFT (1x48GB @ 16.2GiB)
+    axolotl train examples/LiquidAI/lfm2-8b-a1b-lora.yaml
+    ```
+
 ### TIPS
 
 - **Installation Error**: If you encounter `ImportError: ... undefined symbol ...` or `ModuleNotFoundError: No module named 'causal_conv1d_cuda'`, the `causal-conv1d` package may have been installed incorrectly. Try uninstalling it:
@@ -45,14 +55,13 @@ This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
 
 ## Optimization Guides
 
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [Optimizations Guide](https://docs.axolotl.ai/docs/optimizations.html)
 
 ## Related Resources
 
 - [LFM2 Blog](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models)
 - [LFM2-VL Blog](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models)
+- [LFM2-MoE Blog](https://www.liquid.ai/blog/lfm2-8b-a1b-an-efficient-on-device-mixture-of-experts)
 - [Axolotl Docs](https://docs.axolotl.ai)
 - [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
 - [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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

@@ -1,6 +1,7 @@
 base_model: LiquidAI/LFM2-350M
 
-chunked_cross_entropy: true
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
 
 eot_tokens:
   - "<|im_end|>"

examples/LiquidAI/lfm2-8b-a1b-lora.yaml

@@ -0,0 +1,59 @@
+base_model: LiquidAI/LFM2-8B-A1B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: true
+
+eot_tokens:
+  - "<|im_end|>"
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+
+adapter: lora
+lora_model_dir:
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 4
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 5e-5
+
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 2
+saves_per_epoch: 1
+
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -3,6 +3,9 @@ trust_remote_code: true
 model_type: AutoModelForImageTextToText
 processor_type: AutoProcessor
 
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true
 remove_unused_columns: false

examples/alst/README.md

@@ -7,3 +7,24 @@ techniques. It is a combination of:
 - Activation Offloading: Offload activations to CPU RAM to reduce memory usage
 
 For more information, you can check out the ALST paper [here](https://www.arxiv.org/abs/2506.13996).
+
+## Usage
+
+```yaml
+tiled_mlp: true
+
+# See Sequence Parallelism docs
+# https://docs.axolotl.ai/docs/sequence_parallelism.html
+context_parallel_size: int
+
+plugins:
+# See Cut Cross Entropy docs
+# https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+# or Liger Kernel docs
+# https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels
+  - axolotl.integrations.liger.LigerPlugin
+# ...
+
+```

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@c5aa3ef\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953\""
    ]
   },
   {

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

@@ -1,7 +1,7 @@
 base_model: google/gemma-3-1b-it
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
+
+model_type: Gemma3ForCausalLM
+
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 

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

@@ -1,7 +1,7 @@
 base_model: google/gemma-3-270m-it
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
+
+model_type: Gemma3ForCausalLM
+
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 

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

@@ -1,5 +1,8 @@
 base_model: google/gemma-3-4b-it
 
+# Need to set else transformers tries to load vision too
+model_type: Gemma3ForCausalLM
+
 load_in_4bit: true
 
 # gemma3 doesn't seem to play nice with ddp

examples/gpt-oss/README.md

@@ -2,6 +2,8 @@
 
 [GPT-OSS](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) are a family of open-weight MoE models trained by OpenAI, released in August 2025. There are two variants: 20B and 120B.
 
+In October 2025, OpenAI released safeguard models built upon GPT-OSS called [GPT-OSS-Safeguard](https://huggingface.co/collections/openai/gpt-oss-safeguard). They use the same architecture, so the same examples below can be re-used.
+
 This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
 
 ## Getting started
@@ -64,6 +66,16 @@ axolotl merge-sharded-fsdp-weights examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offlo
 mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
 ```
 
+### How to set reasoning_effort in template?
+
+The harmony template has a feature to set the `reasoning_effort` during prompt building. The default is `medium`. If you would like to adjust this, you can add the following to your config:
+
+```yaml
+chat_template_kwargs:
+  reasoning_effort: "high"  # low | medium | high
+```
+
+Currently, this applies globally. There is no method to apply per sample yet. If you are interested in adding this, please feel free to create an Issue to discuss.
 
 ### Inferencing your fine-tuned model
 

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

@@ -0,0 +1,67 @@
+base_model: openai/gpt-oss-safeguard-20b
+use_kernels: true
+model_quantization_config: Mxfp4Config
+model_quantization_config_kwargs:
+  dequantize: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+experimental_skip_move_to_device: true  # prevent OOM by not putting model to GPU before sharding
+
+datasets:
+  - path: HuggingFaceH4/Multilingual-Thinking
+    type: chat_template
+    field_thinking: thinking
+    template_thinking_key: thinking
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0
+output_dir: ./outputs/gpt-oss-safeguard-out/
+
+sequence_len: 4096
+sample_packing: true
+
+adapter: lora
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.0  # dropout not supported when using LoRA over expert parameters
+lora_target_linear: true
+
+# TODO: not supported for now, see peft#2710
+#lora_target_parameters:  # target the experts in the last two layers
+#  - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
+#  - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
+#  - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
+#  - "23._checkpoint_wrapped_module.mlp.experts.down_proj"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+
+optimizer: adamw_torch_8bit
+lr_scheduler: constant_with_warmup
+learning_rate: 2e-4
+
+bf16: true
+tf32: true
+
+flash_attention: true
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
+
+gradient_checkpointing: true
+activation_offloading: true
+
+logging_steps: 1
+saves_per_epoch: 1
+warmup_ratio: 0.1
+
+special_tokens:
+eot_tokens:
+  - "<|end|>"

examples/granite4/README.md

@@ -0,0 +1,65 @@
+# Finetune IBM's Granite 4.0 with Axolotl
+
+[Granite 4.0](https://huggingface.co/collections/ibm-granite/granite-40-language-models) are a family of open source models trained by IBM Research.
+
+This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Granite4 is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.7.1 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. Run the finetuning example:
+
+```bash
+axolotl train examples/granite4/granite-4.0-tiny-fft.yaml
+```
+
+This config uses about 40.8GiB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### TIPS
+
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+### Limitation
+
+Adapter finetuning does not work at the moment. It would error with
+
+```bash
+RuntimeError: mat1 and mat2 shapes cannot be multiplied (4096x3072 and 1x1179648)
+```
+
+In addition, if adapter training works, `lora_target_linear: true` will not work due to:
+```bash
+ValueError: Target module GraniteMoeHybridParallelExperts() is not supported.
+```
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Related Resources
+
+- [Granite Docs](https://www.ibm.com/granite/docs/models/granite)
+- [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/granite4/granite-4.0-tiny-fft.yaml

@@ -0,0 +1,45 @@
+base_model: ibm-granite/granite-4.0-tiny-preview
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/model-out
+
+sequence_len: 2048
+sample_packing: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-2/qlora-fsdp.yml

@@ -66,6 +66,7 @@ fsdp_config:
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
   fsdp_state_dict_type: FULL_STATE_DICT
+  # fsdp_cpu_offload_pin_memory: false  # uncomment to enable swap memory usage when RAM is insufficient
 special_tokens:
 
 # save_first_step: true  # uncomment this to validate checkpoint saving works with your config

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

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

examples/llama-3/opentelemetry-qlora.yml

@@ -0,0 +1,50 @@
+base_model: NousResearch/Llama-3.2-1B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_4bit: true
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+
+output_dir: ./outputs/opentelemetry-example
+
+adapter: qlora
+sequence_len: 512
+sample_packing: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+# OpenTelemetry Configuration
+use_otel_metrics: true
+otel_metrics_host: "localhost"
+otel_metrics_port: 8000
+
+# Disable WandB
+use_wandb: false
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: false
+
+warmup_ratio: 0.1
+evals_per_epoch: 2
+saves_per_epoch: 1
+weight_decay: 0.0
+
+special_tokens:
+  pad_token: "<|end_of_text|>"

examples/magistral/think/README.md

@@ -12,7 +12,7 @@ Before starting, ensure you have:
 Run the thinking model fine-tuning:
 
 ```bash
-axolotl train magistral-small-think-qlora.yaml
+axolotl train examples/magistral/think/magistral-small-think-qlora.yaml
 ```
 
 This config uses about 19.1 GiB VRAM.

examples/magistral/vision/README.md

@@ -21,7 +21,7 @@ Before starting, ensure you have:
 
 3. Run the fine-tuning:
    ```bash
-   axolotl train magistral-small-vision-24B-qlora.yml
+   axolotl train examples/magistral/vision/magistral-small-vision-24B-qlora.yml
    ```
 
 This config uses about 17GiB VRAM.

examples/mistral/mistral-small/README.md

@@ -0,0 +1,51 @@
+# Mistral Small 3.1/3.2 Fine-tuning
+
+This guide covers fine-tuning [Mistral Small 3.1](mistralai/Mistral-Small-3.1-24B-Instruct-2503) and [Mistral Small 3.2](mistralai/Mistral-Small-3.2-24B-Instruct-2506) with vision capabilities using Axolotl.
+
+## Prerequisites
+
+Before starting, ensure you have:
+- Installed Axolotl (see [Installation docs](https://docs.axolotl.ai/docs/installation.html))
+
+## Getting Started
+
+1. Install the required vision lib:
+    ```bash
+    pip install 'mistral-common[opencv]==1.8.5'
+    ```
+
+2. Download the example dataset image:
+   ```bash
+   wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+   ```
+
+3. Run the fine-tuning:
+   ```bash
+   axolotl train examples/mistral/mistral-small/mistral-small-3.1-24B-lora.yml
+   ```
+
+This config uses about 29.4 GiB VRAM.
+
+## Dataset Format
+
+The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+
+One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
+
+Example:
+```json
+{
+    "messages": [
+        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
+        {"role": "user", "content": [
+            { "type": "text", "text": "What's in this image?"},
+            {"type": "image", "path": "path/to/image.jpg" }
+        ]},
+        {"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
+    ],
+}
+```
+
+## Limitations
+
+- Sample Packing is not supported for multi-modality training currently.

examples/mistral/mistral-small/mistral-small-3.1-24B-lora.yml

@@ -39,7 +39,7 @@ wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1
-micro_batch_size: 1
+micro_batch_size: 2
 num_epochs: 1
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine

examples/olmo3/README.md

@@ -0,0 +1,46 @@
+# Finetune Allenai's Olmo 3 with Axolotl
+
+[Olmo 3](https://huggingface.co/collections/allenai/olmo-3) are a family of 7B and 32B models open source models trained by The Allen Institute for Artificial Intelligence.
+
+This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+
+    # Install Cut Cross Entropy
+    python scripts/cutcrossentropy_install.py | sh
+    ```
+
+2. Run the finetuning example:
+
+```bash
+axolotl train examples/olmo3/olmo3-7b-qlora.yaml
+```
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### TIPS
+
+- The example config can be re-used for Olmo and Olmo 2.
+- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [Olmo 3 Blog](https://allenai.org/blog/olmo3)
+- [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/olmo3/olmo3-7b-qlora.yaml

@@ -0,0 +1,64 @@
+base_model: allenai/Olmo-3-7B-Instruct-SFT
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/qwen3-next/README.md

@@ -38,7 +38,7 @@ pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
 axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
 ```
 
-This config uses about 41.7 GiB VRAM.
+This config uses about 45.62 GiB VRAM.
 
 Let us know how it goes. Happy finetuning! 🚀
 

examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml

@@ -27,6 +27,14 @@ lora_r: 16
 lora_alpha: 8
 lora_dropout: 0.05
 lora_target_modules:
+  - linear_attn.in_proj_ba
+  - linear_attn.in_proj_qkvz
+  - linear_attn.out_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+  - shared_expert.gate_proj
+  - shared_expert_gate
+  - mlp.gate
   - q_proj
   - v_proj
   - k_proj

examples/seed-oss/README.md

@@ -6,21 +6,17 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
 
 ## Getting started
 
-1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Seed-OSS is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
 
-    Here is an example of how to install from main for pip:
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 
-```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn]'
-
-# Install Cut Cross Entropy
-python scripts/cutcrossentropy_install.py | sh
-```
+    # Install Cut Cross Entropy
+    python scripts/cutcrossentropy_install.py | sh
+    ```
 
 2. Run the finetuning example:
 
@@ -41,9 +37,7 @@ Let us know how it goes. Happy finetuning! 🚀
 
 ## Optimization Guides
 
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
 ## Related Resources
 

examples/smolvlm2/README.md

@@ -37,9 +37,7 @@ This guide shows how to fine-tune SmolVLM2 models with Axolotl.
 
 ## Optimization Guides
 
-- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
-- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
-- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
 
 ## Related Resources
 

examples/voxtral/voxtral-mini-audio-qlora.yml

@@ -1,5 +1,5 @@
 base_model: mistralai/Voxtral-Mini-3B-2507
-processor_type: AutoProcessor
+processor_type: VoxtralProcessor
 
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name

requirements.txt

@@ -1,35 +1,34 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.47.0
+bitsandbytes==0.48.2
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-autoawq==0.2.7.post3
-liger-kernel==0.6.1
+liger-kernel==0.6.3
 # END section
 
 packaging==23.2
 
-huggingface_hub>=0.33.0
-peft>=0.17.0
-transformers==4.56.1
-tokenizers>=0.21.1
-accelerate==1.10.1
-datasets==4.0.0
+huggingface_hub>=0.36.0
+peft>=0.18.0
+tokenizers>=0.22.1
+transformers==4.57.1
+accelerate==1.11.0
+datasets==4.4.1
 deepspeed>=0.17.0
-trl==0.23.0
-hf_xet==1.1.5
-kernels==0.9.0
+trl==0.25.0
+hf_xet==1.2.0
+kernels>=0.9.0
 trackio
 
 optimum==1.16.2
 hf_transfer
 sentencepiece
-gradio==5.41.1
+gradio==5.49.1
 
 modal==1.0.2
-pydantic==2.10.6
+pydantic>=2.10.6
 addict
 fire
 PyYAML>=6.0
@@ -37,13 +36,12 @@ requests
 wandb
 einops
 colorama
-numba
-numpy>=1.24.4,<=2.0.1
+numba>=0.61.2
+numpy>=2.2.6
 
 # qlora things
 evaluate==0.4.1
 scipy
-scikit-learn==1.4.2
 nvidia-ml-py==12.560.30
 art
 tensorboard
@@ -51,7 +49,7 @@ python-dotenv==1.0.1
 
 # remote filesystems
 s3fs>=2024.5.0
-gcsfs>=2024.5.0
+gcsfs>=2025.3.0
 adlfs>=2024.5.0
 ocifs==1.3.2
 
@@ -65,9 +63,13 @@ immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
 torchao==0.13.0
+openenv-core==0.1.0
 schedulefree==1.4.1
 
-axolotl-contribs-lgpl==0.0.6
+axolotl-contribs-lgpl==0.0.7
 axolotl-contribs-mit==0.0.5
 
+# telemetry
+posthog==6.7.11
+
 mistral-common==1.8.5

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@c5aa3ef"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"'
 )

setup.py

@@ -26,7 +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]
-        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [
@@ -34,7 +33,6 @@ def parse_requirements(extras_require_map):
                 "triton",
                 "mamba-ssm",
                 "xformers",
-                "autoawq",
                 "liger-kernel",
             ]
             _install_requires = [
@@ -51,7 +49,7 @@ def parse_requirements(extras_require_map):
             try:
                 torch_version = version("torch")
             except PackageNotFoundError:
-                torch_version = "2.6.0"  # default to torch 2.6
+                torch_version = "2.8.0"  # default to torch 2.8.0
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -64,8 +62,15 @@ def parse_requirements(extras_require_map):
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 8):
-                pass
+            if (major, minor) >= (2, 9):
+                extras_require_map.pop("fbgemm-gpu")
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
+                extras_require_map["vllm"] = ["vllm==0.11.1"]
+                _install_requires.pop(_install_requires.index(xformers_version))
+            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"]
             elif (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
@@ -74,7 +79,7 @@ def parse_requirements(extras_require_map):
                     extras_require_map.pop("vllm")
                 else:
                     _install_requires.append("xformers==0.0.31")
-                    extras_require_map["vllm"] = ["vllm>=0.10.0"]
+                    extras_require_map["vllm"] = ["vllm==0.10.1"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers==0.0.29.post3")
@@ -87,7 +92,6 @@ def parse_requirements(extras_require_map):
                     _install_requires.append("xformers==0.0.28.post2")
                 else:
                     _install_requires.append("xformers>=0.0.28.post3")
-                _install_requires.pop(_install_requires.index(autoawq_version))
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 4):
                 extras_require_map.pop("vllm")
@@ -126,7 +130,7 @@ extras_require = {
         "ring-flash-attn>=0.1.7",
     ],
     "deepspeed": [
-        "deepspeed==0.17.5",
+        "deepspeed==0.18.2",
         "deepspeed-kernels",
     ],
     "mamba-ssm": [
@@ -161,7 +165,13 @@ extras_require = {
     "llmcompressor": [
         "llmcompressor==0.5.1",
     ],
-    "fbgemm-gpu": ["fbgemm-gpu-genai>=1.2.0"],
+    "fbgemm-gpu": ["fbgemm-gpu-genai==1.3.0"],
+    "opentelemetry": [
+        "opentelemetry-api",
+        "opentelemetry-sdk",
+        "opentelemetry-exporter-prometheus",
+        "prometheus-client",
+    ],
 }
 install_requires, dependency_links, extras_require_build = parse_requirements(
     extras_require

src/axolotl/cli/config.py

@@ -14,6 +14,8 @@ import yaml
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
+from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.comet_ import setup_comet_env_vars
 from axolotl.utils.config import (
     normalize_cfg_datasets,
@@ -31,6 +33,8 @@ LOG = get_logger(__name__)
 
 API_KEY_FIELDS = {"comet_api_key"}
 
+TELEMETRY_MANAGER = TelemetryManager.get_instance()
+
 
 def check_remote_config(config: Union[str, Path]) -> Union[str, Path]:
     """
@@ -164,6 +168,7 @@ def plugin_set_cfg(cfg: DictDefault):
         plugin_manager.cfg = cfg
 
 
+@send_errors
 def load_cfg(
     config: str | Path | DictDefault = Path("examples/"), **kwargs
 ) -> DictDefault:
@@ -197,6 +202,8 @@ def load_cfg(
             temp_file.close()
         cfg.axolotl_config_path = temp_file.name
 
+    TELEMETRY_MANAGER.send_event(event_type="config-loaded", properties=cfg)
+
     # If there are any options passed in the cli, if it is something that seems valid
     # from the yaml, then overwrite the value
     cfg_keys = cfg.keys()
@@ -240,6 +247,7 @@ def load_cfg(
     setup_comet_env_vars(cfg)
     plugin_set_cfg(cfg)
 
+    TELEMETRY_MANAGER.send_event(event_type="config-processed", properties=cfg)
     cfg_to_log = {
         k: "[REDACTED]" if k in API_KEY_FIELDS else v
         for k, v in cfg.items()

src/axolotl/cli/delinearize_llama4.py

@@ -85,9 +85,7 @@ def do_cli(model: Union[Path, str], output: Union[Path, str]) -> None:
     unpatch_llama4 = patch_llama4_linearized_modeling()
     from transformers import Llama4ForConditionalGeneration
 
-    model_ = Llama4ForConditionalGeneration.from_pretrained(
-        model, torch_dtype=torch.bfloat16
-    )
+    model_ = Llama4ForConditionalGeneration.from_pretrained(model, dtype=torch.bfloat16)
     processor = AutoProcessor.from_pretrained(model)
     processor.save_pretrained(output)
 

src/axolotl/cli/inference.py

@@ -19,7 +19,10 @@ from axolotl.cli.utils.diffusion import (
     launch_diffusion_gradio_ui,
 )
 from axolotl.integrations.base import PluginManager
-from axolotl.utils.chat_templates import get_chat_template_from_config
+from axolotl.telemetry.errors import send_errors
+from axolotl.utils.chat_templates import (
+    get_chat_template_from_config,
+)
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
@@ -43,6 +46,7 @@ def get_multi_line_input() -> str:
     return instruction
 
 
+@send_errors
 def do_inference(
     *,
     cfg: DictDefault,
@@ -160,6 +164,7 @@ def do_inference(
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
+@send_errors
 def do_inference_gradio(
     *,
     cfg: DictDefault,

src/axolotl/cli/merge_lora.py

@@ -7,12 +7,14 @@ import fire
 
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_merge_lora(*, cfg: DictDefault) -> None:
     """
     Calls `transformers`' `merge_and_unload` on the model given in the `axolotl` config

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -23,6 +23,7 @@ from safetensors.torch import save_file as safe_save_file
 from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
 from axolotl.cli.config import load_cfg
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.logging import get_logger
 from axolotl.utils.train import determine_last_checkpoint
 
@@ -118,6 +119,7 @@ def _distributed_checkpoint_to_merged_weights(
     return save_path_
 
 
+@send_errors
 def merge_fsdp_weights(
     checkpoint_dir: str,
     output_path: str,

src/axolotl/cli/preprocess.py

@@ -17,6 +17,7 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.trainer import disable_datasets_caching
@@ -24,6 +25,7 @@ from axolotl.utils.trainer import disable_datasets_caching
 LOG = get_logger(__name__)
 
 
+@send_errors
 def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     """
     Preprocesses dataset specified in axolotl config.

src/axolotl/cli/quantize.py

@@ -69,7 +69,7 @@ def do_quantize(
     config = AutoConfig.from_pretrained(model_path)
     torch_dtype = config.torch_dtype if hasattr(config, "torch_dtype") else None
     model = AutoModelForCausalLM.from_pretrained(
-        model_path, device_map="auto", torch_dtype=torch_dtype
+        model_path, device_map="auto", dtype=torch_dtype
     )
 
     LOG.info(

src/axolotl/cli/train.py

@@ -99,7 +99,7 @@ def ray_train_func(kwargs: dict):
     resolve_dtype(cfg)
 
     # ray serializing objects gets rid of frozen attribute - HF expects dict not DefaultDict
-    if cfg.deepspeed:
+    if cfg.deepspeed and hasattr(cfg.deepspeed, "to_dict"):
         cfg.deepspeed = cfg.deepspeed.to_dict()
 
     # initialize accelerator before model instantiation

src/axolotl/common/architectures.py

@@ -12,6 +12,9 @@ MOE_ARCH_BLOCK = {
     "mixtral": "MixtralSparseMoeBlock",
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
+    "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
     "deepseek_v2": "DeepseekV2MoE",
+    "deepseek_v3": "DeepseekV3MoE",
     "gpt_oss": "GptOssDecoderLayer",
+    "lfm2_moe": "Lfm2MoeSparseMoeBlock",
 }

src/axolotl/common/datasets.py

@@ -9,6 +9,7 @@ from datasets import Dataset
 import axolotl.monkeypatch.data.batch_dataset_fetcher  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.data import prepare_datasets, prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
@@ -34,6 +35,7 @@ def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:
     )
 
 
+@send_errors
 def load_datasets(
     *,
     cfg: DictDefault,
@@ -96,6 +98,7 @@ def load_datasets(
     )
 
 
+@send_errors
 def load_preference_datasets(
     *, cfg: DictDefault, cli_args: PreprocessCliArgs | TrainerCliArgs | None = None
 ) -> TrainDatasetMeta:

src/axolotl/core/builders/base.py

@@ -29,7 +29,13 @@ from transformers.trainer_pt_utils import AcceleratorConfig
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
-from axolotl.utils import is_comet_available, is_mlflow_available
+from axolotl.telemetry.callbacks import TelemetryCallback
+from axolotl.telemetry.manager import TelemetryManager
+from axolotl.utils import (
+    is_comet_available,
+    is_mlflow_available,
+    is_opentelemetry_available,
+)
 from axolotl.utils.callbacks import (
     GCCallback,
     SaveAxolotlConfigtoWandBCallback,
@@ -114,6 +120,13 @@ class TrainerBuilderBase(abc.ABC):
         if self.cfg.gc_steps:
             callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
 
+        if self.cfg.dynamic_checkpoint and self.cfg.dynamic_checkpoint.enabled:
+            from axolotl.utils.callbacks.dynamic_checkpoint import (
+                DynamicCheckpointCallback,
+            )
+
+            callbacks.append(DynamicCheckpointCallback(self.cfg))
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
@@ -134,6 +147,12 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
+        if self.cfg.use_otel_metrics and is_opentelemetry_available():
+            from axolotl.utils.callbacks.opentelemetry import (
+                OpenTelemetryMetricsCallback,
+            )
+
+            callbacks.append(OpenTelemetryMetricsCallback(self.cfg))
         if self.cfg.save_first_step:
             callbacks.append(SaveModelOnFirstStepCallback())
 
@@ -145,6 +164,10 @@ class TrainerBuilderBase(abc.ABC):
                 )
             )
 
+        telemetry_manager = TelemetryManager.get_instance()
+        if telemetry_manager.enabled:
+            callbacks.append(TelemetryCallback())
+
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -186,9 +209,9 @@ class TrainerBuilderBase(abc.ABC):
     ):
         warmup_steps = 0
         warmup_ratio = 0.0
-        if self.cfg.warmup_steps:
+        if self.cfg.warmup_steps is not None:
             warmup_steps = self.cfg.warmup_steps
-        elif self.cfg.warmup_ratio:
+        elif self.cfg.warmup_ratio is not None:
             if total_num_steps:
                 warmup_steps = max(int(self.cfg.warmup_ratio * total_num_steps), 0)
             else:
@@ -491,6 +514,7 @@ class TrainerBuilderBase(abc.ABC):
             "dion_momentum",
             "dion_rank_fraction",
             "dion_rank_multiple_of",
+            "dataset_num_proc",
         ]:
             if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
                 training_args_kwargs[arg] = getattr(self.cfg, arg)
@@ -514,9 +538,6 @@ class TrainerBuilderBase(abc.ABC):
         training_args_kwargs["max_steps"] = self.cfg.max_steps or total_num_steps or -1
         training_args_kwargs["num_train_epochs"] = self.cfg.num_epochs
 
-        if self.cfg.dataset_processes:
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         # max_length is not used in CausalTrainer
         if self.cfg.reward_model or self.cfg.rl:
             training_args_kwargs["max_length"] = self.cfg.sequence_len

src/axolotl/core/builders/causal.py

@@ -12,7 +12,7 @@ from transformers import (
     EarlyStoppingCallback,
     Trainer,
 )
-from trl.trainer.utils import RewardDataCollatorWithPadding
+from trl.trainer.reward_trainer import DataCollatorForPreference
 
 from axolotl.core.builders.base import TrainerBuilderBase
 from axolotl.core.trainers import (
@@ -28,7 +28,6 @@ from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     LossWatchDogCallback,
-    SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     colab_inference_post_train_callback,
@@ -63,12 +62,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.relora:
             callbacks.append(ReLoRACallback(self.cfg))
 
-        if (
-            hasattr(self.model, "use_bettertransformer")
-            and self.model.use_bettertransformer is True
-        ):
-            callbacks.append(SaveBetterTransformerModelCallback())
-
         # TODO: check if can move to base class
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))
@@ -460,7 +453,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
                 DataCollatorWithFlattening,
-                RewardDataCollatorWithPadding,
+                DataCollatorForPreference,
             ]
         ]
         collator_args = [self.tokenizer]
@@ -477,7 +470,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if kwargs and isinstance(kwargs, dict):
                 kwargs.update(collator_cls_and_kwargs[1])
         elif self.cfg.reward_model:
-            collator = RewardDataCollatorWithPadding
+            collator = DataCollatorForPreference
+            tokenizer = collator_args.pop(0)
+            kwargs["pad_token_id"] = tokenizer.pad_token_id
+            kwargs.pop("padding")
         elif use_batch_sampler_collator:
             # Use V2BatchSamplerDataCollatorForSeq2Seq for flex attention,
             # supported multipack models, or non-flash-attention llama

src/axolotl/core/trainers/base.py

@@ -43,7 +43,7 @@ from axolotl.core.trainers.utils import (
 from axolotl.utils import get_not_null
 from axolotl.utils.bench import get_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_main_process
+from axolotl.utils.distributed import is_distributed, is_main_process
 from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -225,17 +225,6 @@ class AxolotlTrainer(
 
         data_collator = self.data_collator if is_training else self.eval_data_collator
 
-        if dataset.column_names and "length" in dataset.column_names:
-            dataset = dataset.remove_columns(["length"])
-        if (
-            dataset.column_names
-            and "position_ids" in dataset.column_names
-            and "attention_mask" in dataset.column_names
-            and self.args.sample_packing
-            and self.args.sample_packing_drop_attention_mask
-        ):
-            dataset = dataset.remove_columns(["attention_mask"])
-
         if isinstance(dataset, datasets.Dataset):
             if is_training:
                 if not self.args.sample_packing or self.args.pretraining:
@@ -294,6 +283,18 @@ class AxolotlTrainer(
         ):
             self.accelerator.even_batches = False
 
+        if dataset.column_names and "length" in dataset.column_names:
+            dataset = dataset.remove_columns(["length"])
+
+        if (
+            dataset.column_names
+            and "position_ids" in dataset.column_names
+            and "attention_mask" in dataset.column_names
+            and self.args.sample_packing
+            and self.args.sample_packing_drop_attention_mask
+        ):
+            dataset = dataset.remove_columns(["attention_mask"])
+
         dataloader = DataLoader(dataset, **dataloader_params)
 
         # Accelerator.free_memory() will destroy the references, so
@@ -349,6 +350,11 @@ class AxolotlTrainer(
         # track number of tokens for tokens per second calculation
         if self.args.include_tkps:
             inputs_key = "labels" if "labels" in inputs else "input_ids"
+            num_tokens = (inputs[inputs_key] != -100).sum()
+            if is_distributed():
+                torch.distributed.all_reduce(
+                    num_tokens, op=torch.distributed.ReduceOp.SUM
+                )
             if hasattr(self.state, "num_tokens"):
                 self.state.num_tokens = (
                     self.state.num_tokens + (inputs[inputs_key] != -100).sum().cpu()
@@ -356,6 +362,11 @@ class AxolotlTrainer(
             else:
                 self.state.num_tokens = (inputs[inputs_key] != -100).sum().cpu()
 
+            if hasattr(self.state, "total_tokens"):
+                self.state.total_tokens += num_tokens
+            else:
+                self.state.total_tokens = num_tokens
+
         if self.args.orpo_alpha:
             return self.orpo_compute_loss(
                 model,
@@ -560,13 +571,6 @@ class AxolotlTrainer(
 
         super().create_accelerator_and_postprocess()
 
-        if self.is_fsdp_enabled:
-            if (
-                "limit_all_gathers" in self.args.fsdp_config
-                and self.args.fsdp_config["limit_all_gathers"]
-            ):
-                self.accelerator.state.fsdp_plugin.limit_all_gathers = True
-
     def additional_accelerator_args(
         self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
     ) -> dict[str, Any]:
@@ -627,6 +631,7 @@ class AxolotlTrainer(
             logs["tokens_per_second_per_gpu"] = round(
                 self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
             )
+            logs["total_tokens"] = int(self.state.total_tokens.item())
 
         del self._stored_metrics[train_eval]
 

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

@@ -36,6 +36,4 @@ class DPOStrategy:
             training_args_kwargs["dpo_norm_loss"] = cfg.dpo_norm_loss
         if cfg.dpo_use_logits_to_keep is not None:
             training_args_kwargs["use_logits_to_keep"] = cfg.dpo_use_logits_to_keep
-        if cfg.dpo_disable_output_fp32 is not None:
-            training_args_kwargs["disable_output_fp32"] = cfg.dpo_disable_output_fp32
         return training_args_kwargs

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

@@ -16,4 +16,3 @@ class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
     """
 
     dpo_norm_loss: bool | None = False
-    disable_output_fp32: bool | None = False

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

@@ -52,6 +52,7 @@ class GRPOStrategy:
             if trl.vllm_mode:
                 grpo_args_kwargs["vllm_mode"] = trl.vllm_mode
             if trl.vllm_mode == "colocate":
+                grpo_args_kwargs["vllm_enable_sleep_mode"] = trl.vllm_enable_sleep_mode  # type: ignore[attr-defined]
                 grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
                     vllm_cfg.gpu_memory_utilization
                 )
@@ -125,6 +126,9 @@ class GRPOStrategy:
         if trl.use_liger_loss is not None:
             grpo_args_kwargs["use_liger_loss"] = trl.use_liger_loss
 
+        if trl.rollout_func:
+            grpo_args_kwargs["rollout_func"] = cls.get_rollout_func(trl.rollout_func)
+
         return grpo_args_kwargs
 
     @classmethod
@@ -200,3 +204,32 @@ class GRPOStrategy:
                 raise ValueError(
                     f"Reward function {reward_func_fqn} not found."
                 ) from exc
+
+    @classmethod
+    def get_rollout_func(cls, rollout_func_fqn: str):
+        """
+        Returns the rollout function from the given fully qualified name.
+
+        Args:
+            rollout_func_fqn (str): Fully qualified name of the rollout function
+                                    (e.g. my_module.my_rollout_func)
+
+        Returns:
+            Callable rollout function
+        """
+        try:
+            rollout_func_module_name = rollout_func_fqn.split(".")[-1]
+            rollout_func_module = importlib.import_module(
+                ".".join(rollout_func_fqn.split(".")[:-1])
+            )
+            rollout_func = getattr(rollout_func_module, rollout_func_module_name)
+
+            if not callable(rollout_func):
+                raise ValueError(
+                    f"Rollout function {rollout_func_fqn} must be callable"
+                )
+
+            return rollout_func
+
+        except ModuleNotFoundError as exc:
+            raise ValueError(f"Rollout function {rollout_func_fqn} not found.") from exc

src/axolotl/evaluate.py

@@ -10,6 +10,7 @@ import torch
 from datasets import Dataset
 from transformers.trainer import Trainer
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.train import (
     TrainDatasetMeta,
     setup_model_and_tokenizer,
@@ -63,6 +64,7 @@ def evaluate_dataset(
     return metrics
 
 
+@send_errors
 def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, float]:
     """
     Evaluate a model on training and validation datasets.

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@c5aa3ef"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"
 ```
 
 ## Usage
@@ -31,6 +31,7 @@ plugins:
 
 ## Supported Models
 
+- apertus
 - arcee
 - cohere
 - cohere2
@@ -44,18 +45,29 @@ plugins:
 - glm
 - glm4
 - glm4_moe
+- glm4v
+- glm4v_moe
 - gpt_oss
 - granite
 - granitemoe
+- granitemoeshared
+- granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
+- lfm2
+- lfm2_moe
+- lfm2_vl
 - llama
 - llama4
 - llama4_text
+- llava
 - mistral
 - mistral3
 - mixtral
 - mllama
+- olmo
+- olmo2
+- olmo3
 - phi
 - phi3
 - phi4_multimodal
@@ -65,6 +77,8 @@ plugins:
 - qwen2_5_vl
 - qwen3
 - qwen3_moe
+- qwen3_vl
+- qwen3_vl_moe
 - qwen3_next
 - smollm3
 - seed_oss

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@c5aa3ef"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@5eff953"`'
 )
 
 

src/axolotl/integrations/diffusion/generation.py

@@ -7,7 +7,7 @@ import torch
 
 from axolotl.utils.logging import get_logger
 
-from .utils import create_bidirectional_attention_mask
+from .utils import create_bidirectional_attention_mask, shift_logits_to_input_positions
 
 LOG = get_logger(__name__)
 
@@ -360,7 +360,7 @@ def _diffusion_step(
 
     # Forward pass
     outputs = model(input_ids=sequence, attention_mask=attention_mask)
-    logits = outputs.logits
+    logits = shift_logits_to_input_positions(outputs.logits)
 
     # Only sample at currently masked positions
     if current_mask.any():

src/axolotl/integrations/diffusion/trainer.py

@@ -11,7 +11,7 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 from .callbacks import DiffusionGenerationCallback
-from .utils import create_bidirectional_attention_mask
+from .utils import create_bidirectional_attention_mask, shift_logits_to_input_positions
 
 LOG = get_logger(__name__)
 
@@ -207,7 +207,7 @@ class DiffusionTrainer(AxolotlTrainer):
             input_ids=noisy_batch.long(),
             attention_mask=bidirectional_mask,
         )
-        logits = outputs.logits
+        logits = shift_logits_to_input_positions(outputs.logits)
 
         if masked_indices.sum() > 0:
             valid_indices = torch.where(masked_indices)

src/axolotl/integrations/diffusion/utils.py

@@ -157,3 +157,10 @@ def create_bidirectional_attention_mask(
 
     # Add head dimension: [batch_size, 1, seq_len, seq_len]
     return bidirectional_mask.unsqueeze(1)
+
+
+def shift_logits_to_input_positions(logits: torch.Tensor) -> torch.Tensor:
+    """Align next-token logits with their input token positions for diffusion."""
+    if logits.size(1) <= 1:
+        return logits
+    return torch.cat([logits[:, :1], logits[:, :-1]], dim=1)

src/axolotl/integrations/kd/kernels/models.py

@@ -72,9 +72,9 @@ def kldiv_forward_llama_like(
 
     # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
     # TODO, we can optimize this further by filtering hidden_states on sequence dimension using labels != -100
-    # self.loss_function should be LigerFusedLinearKLTopKLogprobLoss
+    # self._loss_function should be LigerFusedLinearKLTopKLogprobLoss
 
-    loss = self.loss_function(
+    loss = self._loss_function(
         self.lm_head.weight,
         hidden_states,
         target_token_ids,

src/axolotl/integrations/kd/trainer.py

@@ -29,7 +29,8 @@ class AxolotlKDTrainer(AxolotlTrainer):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.model_accepts_loss_kwargs = True
-        self.model._loss_function = LigerFusedLinearKLTopKLogprobLoss(
+
+        loss_fn = LigerFusedLinearKLTopKLogprobLoss(
             self.args.kd_ce_alpha,  # hard label loss
             self.args.kd_alpha,  # kd loss
             self.args.kd_temperature,
@@ -37,6 +38,14 @@ class AxolotlKDTrainer(AxolotlTrainer):
             compute_ce_loss=bool(self.args.kd_ce_alpha),
             normalize_topk=self.args.kd_normalize_topk,
         )
+        target = self.model
+
+        # Unwrap PEFT wrapper
+        if hasattr(target, "get_base_model"):
+            target = target.get_base_model()
+
+        # Set on the actual model instance
+        target._loss_function = loss_fn
 
     def _set_signature_columns_if_needed(self):
         super()._set_signature_columns_if_needed()

src/axolotl/integrations/liger/README.md

@@ -18,6 +18,9 @@ liger_rms_norm: true
 liger_glu_activation: true
 liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
+
+# FLCE-specific
+liger_use_token_scaling: true
 ```
 
 ## Supported Models

src/axolotl/integrations/liger/args.py

@@ -16,7 +16,7 @@
 Module for handling LIGER input arguments.
 """
 
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, Field, model_validator
 
 from axolotl.utils.logging import get_logger
 
@@ -35,6 +35,15 @@ class LigerArgs(BaseModel):
     liger_glu_activation: bool | None = None
     liger_cross_entropy: bool | None = None
     liger_fused_linear_cross_entropy: bool | None = None
+    liger_use_token_scaling: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": (
+                "Enables use_token_scaling in fused_linear_cross_entropy. "
+                "When True, each token's loss is multiplied by its predicted probability (detached from gradients)."
+            )
+        },
+    )
 
     @model_validator(mode="before")
     @classmethod
@@ -75,6 +84,18 @@ class LigerArgs(BaseModel):
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_liger_use_token_scaling_flce(cls, data):
+        if data.get("liger_use_token_scaling") and not data.get(
+            "liger_fused_linear_cross_entropy"
+        ):
+            raise ValueError(
+                "`liger_use_token_scaling: true` requires `liger_fused_linear_cross_entropy` enabled."
+            )
+
+        return data
+
     @model_validator(mode="after")
     def check_tensor_parallel_size_liger_fused_linear_cross_entropy(self):
         # TODO @SalmanMohammadi this is a larger fix - investigate

src/axolotl/integrations/liger/plugin.py

@@ -23,6 +23,29 @@ class LigerPlugin(BasePlugin):
         return "axolotl.integrations.liger.LigerArgs"
 
     def pre_model_load(self, cfg):
+        """
+        Apply LIGER runtime patches and integrations according to the provided configuration.
+        
+        This hook inspects `cfg` and conditionally applies LIGER kernel patches, replacements, and model-specific integrations (rotary embeddings, normalization, GLU variants, and cross-entropy implementations) for the model type indicated by `cfg.model_config_type`. Behavior is driven entirely by the various `cfg.liger_*` flags; the method logs actions and warnings when support is experimental or unavailable.
+        
+        Parameters:
+            cfg: Configuration object containing LIGER-related flags and model identification. Expected attributes include:
+                - model_config_type (str): Target model config type to determine which patches to apply.
+                - base_model (str): Base model identifier used when probing model modules (used for some model types).
+                - trust_remote_code (bool|None): Passed when loading remote model code (used for some model types).
+                - torch_compile (bool): If true, disable torch.compile optimizations for certain LIGER kernels.
+                - liger_cross_entropy (bool)
+                - liger_fused_linear_cross_entropy (bool)
+                - liger_use_token_scaling (bool)
+                - liger_rope (bool)
+                - liger_rms_norm (bool)
+                - liger_layer_norm (bool)
+                - liger_glu_activation (str|bool): Name or flag for GLU/SwiGLU activation selection.
+                (Other LIGER flags referenced by the code may also be consulted.)
+        
+        Raises:
+            ValueError: If both `cfg.liger_cross_entropy` and `cfg.liger_fused_linear_cross_entropy` are enabled.
+        """
         if cfg.torch_compile:
             # torch compile will unnecessarily attempt to optimize the triton kernel unless explicitly disabled
             import liger_kernel.ops.fused_linear_cross_entropy
@@ -48,6 +71,33 @@ class LigerPlugin(BasePlugin):
                 "Cannot have both `liger_cross_entropy` and `liger_fused_linear_cross_entropy` set."
             )
 
+        if cfg.liger_use_token_scaling:
+            # Patch FLCE to set token_scaling=True for function and class API
+            from liger_kernel.transformers import functional
+            from liger_kernel.transformers.fused_linear_cross_entropy import (
+                LigerFusedLinearCrossEntropyLoss,
+            )
+
+            old_liger_fused_linear_cross_entropy = (
+                functional.liger_fused_linear_cross_entropy
+            )
+
+            def patched_liger_fused_linear_cross_entropy(*args, **kwargs):
+                kwargs["use_token_scaling"] = True
+                return old_liger_fused_linear_cross_entropy(*args, **kwargs)
+
+            functional.liger_fused_linear_cross_entropy = (
+                patched_liger_fused_linear_cross_entropy
+            )
+
+            old_init = LigerFusedLinearCrossEntropyLoss.__init__
+
+            def patched_init(self, *args, **kwargs):
+                kwargs["use_token_scaling"] = True
+                return old_init(self, *args, **kwargs)
+
+            LigerFusedLinearCrossEntropyLoss.__init__ = patched_init
+
         if cfg.model_config_type in MODEL_TYPE_TO_APPLY_LIGER_FN:
             apply_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[cfg.model_config_type]
             liger_fn_sig = inspect.signature(apply_liger_fn)
@@ -141,6 +191,22 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
+        elif cfg.model_config_type == "qwen3_vl":
+            """
+            Apply Liger kernels for Qwen3 Vision-Language models.
+            
+            Note: The parameter 'swiglu' is used instead of 'glu_activation' to match
+            the Liger kernel API for vision-language models.
+            """
+            from liger_kernel.transformers import apply_liger_kernel_to_qwen3_vl
+
+            apply_liger_kernel_to_qwen3_vl(
+                rope=cfg.liger_rope,
+                cross_entropy=cfg.liger_cross_entropy,
+                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
+                rms_norm=cfg.liger_rms_norm,
+                swiglu=cfg.liger_glu_activation,  # Note: qwen3_vl uses swiglu parameter name
+            )
         elif cfg.model_config_type == "qwen3_moe":
             from axolotl.integrations.liger.models.qwen3_moe import (
                 apply_liger_kernel_to_qwen3_moe,
@@ -179,4 +245,4 @@ class LigerPlugin(BasePlugin):
         else:
             LOG.warning(
                 f"Unsupported model config type: {cfg.model_config_type}. Liger not applied."
-            )
+            )

src/axolotl/loaders/adapter.py

@@ -20,6 +20,7 @@ from peft import (
 from transformers import PreTrainedModel
 
 from axolotl.loaders.utils import get_linear_embedding_layers
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
@@ -101,6 +102,8 @@ def load_lora(
         lora_config_kwargs["layer_replication"] = cfg.peft_layer_replication
     if cfg.peft_trainable_token_indices:
         lora_config_kwargs["trainable_token_indices"] = cfg.peft_trainable_token_indices
+    if cfg.peft_ensure_weight_tying is not None:
+        lora_config_kwargs["ensure_weight_tying"] = cfg.peft_ensure_weight_tying
 
     # Determine the correct PEFT task type
     model_cls = type(model).__name__
@@ -172,6 +175,7 @@ def load_lora(
     return model, lora_config
 
 
+@send_errors
 def load_adapter(
     model: PreTrainedModel,
     cfg: DictDefault,

src/axolotl/loaders/model.py

@@ -49,6 +49,7 @@ from axolotl.loaders.utils import (
     load_model_config,
 )
 from axolotl.models.mamba import fix_mamba_attn_for_loss
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import (
@@ -158,6 +159,7 @@ class ModelLoader:
         """Property that determines if FSDP with QLoRA is enabled."""
         return self.is_fsdp_enabled and self.cfg.adapter == "qlora"
 
+    @send_errors
     def load(self) -> tuple[PreTrainedModel | PeftModelForCausalLM, PeftConfig | None]:
         """Load and prepare the model with all configurations and patches.
 
@@ -515,9 +517,6 @@ class ModelLoader:
             if self.cfg.model_quantization_config_kwargs:
                 mxfp4_kwargs = self.cfg.model_quantization_config_kwargs
             self.model_kwargs["quantization_config"] = Mxfp4Config(**mxfp4_kwargs)
-        else:
-            self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
-            self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
 
         if self.cfg.gptq:
             if not hasattr(self.model_config, "quantization_config"):
@@ -552,9 +551,7 @@ class ModelLoader:
                 self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                     **self.model_config.quantization_config
                 )
-        elif self.cfg.adapter == "qlora" and self.model_kwargs.get(
-            "load_in_4bit", False
-        ):
+        elif self.cfg.adapter == "qlora" and self.cfg.load_in_4bit:
             bnb_config = {
                 "load_in_4bit": True,
                 "llm_int8_threshold": 6.0,
@@ -580,9 +577,7 @@ class ModelLoader:
             self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                 **bnb_config,
             )
-        elif self.cfg.adapter == "lora" and self.model_kwargs.get(
-            "load_in_8bit", False
-        ):
+        elif self.cfg.adapter == "lora" and self.cfg.load_in_8bit:
             bnb_config = {
                 "load_in_8bit": True,
             }
@@ -596,11 +591,6 @@ class ModelLoader:
                 **bnb_config,
             )
 
-        # no longer needed per https://github.com/huggingface/transformers/pull/26610
-        if "quantization_config" in self.model_kwargs or self.cfg.gptq:
-            self.model_kwargs.pop("load_in_8bit", None)
-            self.model_kwargs.pop("load_in_4bit", None)
-
     def _set_attention_config(self):
         """Sample packing uses custom FA2 patch"""
         if self.cfg.attn_implementation:

src/axolotl/loaders/patch_manager.py

@@ -54,7 +54,6 @@ class PatchManager:
         # self._apply_flex_attention_patches()
         self._apply_flash_attention_patches()
         self._apply_chunked_cross_entropy_patch()
-        self._apply_dpo_disable_output_fp32_patch()
         self._apply_fsdp_patches()
         self._apply_adapter_patches()
         self._apply_model_specific_patches()
@@ -85,6 +84,13 @@ class PatchManager:
         patch_evaluation_loop()
         patch_maybe_log_save_evaluate()
 
+        if self.cfg.context_parallel_size > 1:
+            from axolotl.monkeypatch.transformers.trainer_context_parallel import (
+                patch_prepare_context_parallel_inputs,
+            )
+
+            patch_prepare_context_parallel_inputs()
+
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
         self._apply_llama_flash_attn_patches(model)
@@ -108,16 +114,6 @@ class PatchManager:
             else:
                 patch_chunked_ce_loss_fn()
 
-    def _apply_dpo_disable_output_fp32_patch(self):
-        from axolotl.utils.schemas.enums import RLType
-
-        if self.cfg.rl in {RLType.DPO, RLType.IPO} and self.cfg.dpo_disable_output_fp32:
-            from axolotl.monkeypatch.trainer.dpo_chunked import (
-                patch_dpo_disable_output_fp32,
-            )
-
-            patch_dpo_disable_output_fp32()
-
     def _apply_fsdp_patches(self):
         """Apply patches for FSDP configurations."""
         if self.cfg.context_parallel_size > 1 or (
@@ -461,7 +457,7 @@ class PatchManager:
             and self.cfg.flash_attention
             and not self.inference
         ):
-            # TODO(MengqingCao): split these patches seperately
+            # TODO(MengqingCao): split these patches separately
             from axolotl.monkeypatch.llama_attn_hijack_flash import (
                 is_xformers_swiglu_available,
                 replace_llama_mlp_with_swiglu,

src/axolotl/loaders/processor.py

@@ -1,27 +1,47 @@
 """Processor loading functionality for multi-modal models"""
 
-from typing import Any
-
 import transformers
 from transformers import (
     AutoProcessor,
     PreTrainedTokenizerBase,
 )
 
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
 
+@send_errors
 def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
-    processor_kwargs: dict[str, Any] = {}  # Do we actually need this?
-
     processor_cls = AutoProcessor
     if cfg.processor_type:
         processor_cls = getattr(transformers, cfg.processor_type)
 
     if cfg.tokenizer_use_mistral_common:
+
+        def _patch_mistralcommontokenizer():
+            """
+            Transformers v5 stops reading the sub-processor.
+
+            We need to patch this, so both processors use this.
+            """
+            import transformers.tokenization_mistral_common as tokenization_mistral_common
+
+            from axolotl.utils.mistral import HFMistralTokenizer
+
+            tokenization_mistral_common.MistralCommonTokenizer = HFMistralTokenizer
+
+        _patch_mistralcommontokenizer()
+
+        from transformers import VoxtralProcessor
+
+        if processor_cls == VoxtralProcessor:
+            return VoxtralProcessor.from_pretrained(
+                cfg.processor_config,
+            )
+
         from axolotl.utils.mistral import Mistral3Processor
 
         return Mistral3Processor(
@@ -32,7 +52,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
         cfg.processor_config,
         trust_remote_code=cfg.trust_remote_code or False,
         tokenizer=tokenizer,
-        **processor_kwargs,
     )
 
     # Attempt to load image size from processor if available

src/axolotl/loaders/tokenizer.py

@@ -13,6 +13,7 @@ from transformers import (
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import get_linear_embedding_layers, load_model_config
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
+from axolotl.telemetry.errors import send_errors
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import (
@@ -119,6 +120,7 @@ def modify_tokenizer_files(
     return tokenizer_dir
 
 
+@send_errors
 def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
     """Load and configure the tokenizer based on the provided config."""
 

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -4,6 +4,7 @@ monkeypatch for accelerate fsdp2 fix when modifying ordereddict during interatio
 
 import copy
 import functools
+import os
 import sys
 
 import torch
@@ -277,6 +278,11 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
 
     mesh = getattr(accelerator.state, "device_mesh", None)
 
+    # Disable memory pinning if requested
+    offload_to_cpu = isinstance(fsdp2_plugin.cpu_offload, CPUOffloadPolicy)
+    if offload_to_cpu and os.environ.get("FSDP_CPU_OFFLOAD_PIN_MEMORY", "") == "false":
+        fsdp2_plugin.cpu_offload.pin_memory = False
+
     fsdp2_kwargs = {
         "reshard_after_forward": fsdp2_plugin.reshard_after_forward,
         "offload_policy": fsdp2_plugin.cpu_offload,
@@ -341,7 +347,6 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
         )
 
     if fsdp2_plugin.cpu_ram_efficient_loading:
-        offload_to_cpu = isinstance(fsdp2_plugin.cpu_offload, CPUOffloadPolicy)
         fsdp2_load_full_state_dict(
             accelerator, model, original_sd, offload_to_cpu=offload_to_cpu
         )

src/axolotl/monkeypatch/lora_kernels.py

@@ -134,6 +134,11 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
 
         return Qwen2Attention
 
+    if model_type == "qwen3_vl":
+        from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLTextAttention
+
+        return Qwen3VLTextAttention
+
     if model_type == "mllama":
         from transformers.models.mllama.modeling_mllama import MllamaTextSelfAttention
 

src/axolotl/monkeypatch/multipack.py

@@ -40,11 +40,18 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "smollm3",
     "granite",
     "granitemoe",
+    "granitemoeshared",
+    "granitemoehybrid",
     "hunyuan_v1_dense",
     "hunyuan_v1_moe",
     "gpt_oss",
     "arcee",
     "seed_oss",
+    "lfm2",
+    "lfm2_moe",
+    "olmo",
+    "olmo2",
+    "olmo3",
 ]
 
 

src/axolotl/monkeypatch/trainer/dpo_chunked.py

@@ -1,90 +0,0 @@
-"""Monkeypatch helpers to reduce fp32 materialization during DPO training."""
-
-from __future__ import annotations
-
-from contextlib import contextmanager
-from types import MethodType
-from typing import Iterable
-
-import torch
-from trl import DPOTrainer
-
-_PATCHED = False
-
-
-def _iter_patch_targets(model) -> Iterable[torch.nn.Module]:
-    current = model
-    seen: set[int] = set()
-    while current is not None and id(current) not in seen:
-        seen.add(id(current))
-        yield current
-        current = getattr(current, "module", None)
-
-
-def _resolve_unwrapped_forward(module):
-    forward = getattr(module, "forward", None)
-    if forward is None:
-        return None
-
-    if hasattr(forward, "__wrapped__"):
-        unwrapped = forward.__wrapped__
-        return MethodType(unwrapped, module)
-
-    original = getattr(module, "_original_forward", None)
-    if original is None:
-        return None
-
-    func = original.__func__ if hasattr(original, "__func__") else original
-    return MethodType(func, module)
-
-
-@contextmanager
-def _temporarily_disable_output_fp32(model):
-    patched = []
-    for target in _iter_patch_targets(model):
-        replacement = _resolve_unwrapped_forward(target)
-        if replacement is None:
-            continue
-        patched.append((target, target.forward, replacement))
-
-    try:
-        for module, _, replacement in patched:
-            module.forward = replacement
-        yield
-    finally:
-        for module, original_forward, _ in reversed(patched):
-            module.forward = original_forward
-
-
-def _cast_fp32_outputs(output: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-    if not isinstance(output, dict):
-        return output
-
-    for key, value in output.items():
-        if torch.is_tensor(value) and value.dtype in (torch.float16, torch.bfloat16):
-            output[key] = value.float()
-    return output
-
-
-def patch_dpo_disable_output_fp32():
-    """Patch TRL's DPOTrainer to skip Accelerate's convert_to_fp32 wrapper when requested."""
-    global _PATCHED
-    if _PATCHED:
-        return
-
-    original_concatenated_forward = DPOTrainer.concatenated_forward
-
-    def patched_concatenated_forward(self, model, batch, is_ref_model: bool = False):
-        if not getattr(self.args, "disable_output_fp32", False):
-            return original_concatenated_forward(
-                self, model, batch, is_ref_model=is_ref_model
-            )
-
-        with _temporarily_disable_output_fp32(model):
-            result = original_concatenated_forward(
-                self, model, batch, is_ref_model=is_ref_model
-            )
-        return _cast_fp32_outputs(result)
-
-    DPOTrainer.concatenated_forward = patched_concatenated_forward
-    _PATCHED = True

src/axolotl/monkeypatch/transformers/trainer_context_parallel.py

@@ -0,0 +1,66 @@
+"""Monkey patch to allow context parallelism with FlashAttention in HF Trainer."""
+
+from __future__ import annotations
+
+import importlib
+import inspect
+
+from transformers import Trainer
+
+from axolotl.monkeypatch.utils import detab_code
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+GUARD_PATTERN = 'if model.config._attn_implementation != "sdpa":'
+PATCHED_GUARD = 'if (attn_impl := (getattr(model.config, "_attn_implementation", None) or getattr(model.model.config, "_attn_implementation", None))) and attn_impl not in ("sdpa", "flash_attention_2"):'
+
+
+def patch_prepare_context_parallel_inputs() -> None:
+    """Relax the SDPA-only guard when running context parallelism with FlashAttention."""
+    if getattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched", False):
+        LOG.debug("Trainer._prepare_context_parallel_inputs already patched")
+        return
+
+    try:
+        original_source = inspect.getsource(Trainer._prepare_context_parallel_inputs)
+    except OSError as exc:  # pragma: no cover - occurs when source is unavailable
+        LOG.warning("Unable to patch Trainer._prepare_context_parallel_inputs: %s", exc)
+        return
+
+    if GUARD_PATTERN not in original_source:
+        LOG.warning(
+            "Expected guard not found in Trainer._prepare_context_parallel_inputs; \n"
+            "skipping FlashAttention context parallelism patch"
+        )
+        return
+
+    patched_source = original_source.replace(GUARD_PATTERN, PATCHED_GUARD)
+    patched_source, _ = detab_code(patched_source)
+    patched_source = patched_source.replace(
+        "def _prepare_context_parallel_inputs(",
+        "def axolotl_prepare_context_parallel_inputs(",
+        1,
+    )
+
+    module_name = Trainer.__module__
+    module = importlib.import_module(module_name)
+
+    # import symbols referenced in the method so exec can succeed
+    items_to_import = []
+    for item in dir(module):
+        if item in patched_source:
+            items_to_import.append(item)
+
+    exec(f"from {module_name} import ({', '.join(items_to_import)})", globals())
+    exec(patched_source, globals())
+
+    Trainer._original_prepare_context_parallel_inputs = (
+        Trainer._prepare_context_parallel_inputs
+    )
+    Trainer._prepare_context_parallel_inputs = axolotl_prepare_context_parallel_inputs
+    Trainer._axolotl_prepare_context_parallel_inputs_source = patched_source
+    Trainer._axolotl_prepare_context_parallel_inputs_patched = True
+    LOG.debug(
+        "Patched Trainer._prepare_context_parallel_inputs for FlashAttention + CP"
+    )

src/axolotl/processing_strategies.py

@@ -6,8 +6,10 @@ from typing import Optional
 from PIL import Image, ImageOps
 from PIL.Image import Resampling
 from torch import Tensor, zeros_like
-from transformers import ProcessorMixin, SmolVLMProcessor, VoxtralProcessor
+from transformers import ProcessorMixin
 from transformers.image_utils import load_image
+from transformers.models.smolvlm import SmolVLMProcessor
+from transformers.models.voxtral import VoxtralProcessor
 
 from axolotl.utils.dict import remove_none_values
 from axolotl.utils.logging import get_logger

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -71,10 +71,10 @@ class BTChatTemplateStrategy(ChatTemplateStrategy):
             ]
 
         return {
-            "input_ids_chosen": chosen_tokenized["input_ids"],
+            "chosen_input_ids": chosen_tokenized["input_ids"],
             "attention_mask_chosen": chosen_tokenized["attention_mask"],
             "labels_chosen": 1.0,
-            "input_ids_rejected": rejected_tokenized["input_ids"],
+            "rejected_input_ids": rejected_tokenized["input_ids"],
             "attention_mask_rejected": rejected_tokenized["attention_mask"],
             "labels_rejected": 0.0,
         }

src/axolotl/prompt_strategies/chat_template.py

@@ -2,6 +2,7 @@
 HF Chat Templates prompt strategy
 """
 
+import json
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Set, Union
 
@@ -794,6 +795,22 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 if val is not None:
                     transformed_message[key] = val
 
+        if "tool_calls" in transformed_message and transformed_message["tool_calls"]:
+            for tool_call in transformed_message["tool_calls"]:
+                if "function" in tool_call and "arguments" in tool_call["function"]:
+                    args = tool_call["function"]["arguments"]
+                    if isinstance(args, str):
+                        try:
+                            tool_call["function"]["arguments"] = json.loads(args)
+                        except json.JSONDecodeError as e:
+                            LOG.error(
+                                f"Error parsing tool_calls arguments as JSON. "
+                                f"Function: {tool_call.get('function', {}).get('name', 'unknown')}, "
+                                f"Arguments string: {args!r}, "
+                                f"Error: {e}"
+                            )
+                            raise
+
         return transformed_message
 
     def _get_images(self, prompt):
@@ -806,6 +823,23 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             return None
 
         if isinstance(tools, list):
+            # Process each tool to handle JSON string parameters
+            for tool in tools:
+                if isinstance(tool, dict) and "function" in tool:
+                    function = tool["function"]
+                    if "parameters" in function:
+                        params = function["parameters"]
+                        if isinstance(params, str):
+                            try:
+                                function["parameters"] = json.loads(params)
+                            except json.JSONDecodeError as e:
+                                LOG.error(
+                                    f"Error parsing tool parameters as JSON. "
+                                    f"Function: {function.get('name', 'unknown')}, "
+                                    f"Parameters string: {params!r}, "
+                                    f"Error: {e}"
+                                )
+                                raise
             return tools
 
         raise ValueError(