Update SETUP_MIAAI.md: add bare Ubuntu rebuild section (driver, packages, Ollama)

* Add optional Axolotl MoRA/ReMoRA integration

Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>

* Isolate MoRA adapter behavior in plugin

Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>

* Constrain MoRA variants to supported enum values

* Keep MoRA validation out of core config

---------

Co-authored-by: Swarm <swarm@localhost>
Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>

.github/CONTRIBUTING.md

@@ -31,7 +31,11 @@ PRs are **greatly welcome**!
 
 Please run below to setup env
 ```bash
-pip3 install -r requirements-dev.txt -r requirements-tests.txt
+# Install axolotl + dev and test dependencies
+export UV_TORCH_BACKEND=cu128  # or cu130
+uv venv --no-project --relocatable
+source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 pre-commit install
 
 # test

.github/workflows/base.yml

@@ -30,14 +30,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -168,14 +160,6 @@ jobs:
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
             platforms: "linux/amd64,linux/arm64"
-          - cuda: "128"
-            cuda_version: 12.8.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.9.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
-            platforms: "linux/amd64,linux/arm64"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
       types: [opened, synchronize, reopened, ready_for_review]
       paths:
        - '**.py'
-       - 'requirements.txt'
+       - 'pyproject.toml'
        - '.github/workflows/*.yml'
        - "*.[q]md"
        - "examples/**/*.y[a]?ml"

.github/workflows/main.yml

@@ -18,12 +18,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"
@@ -180,12 +174,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.9.0
-            axolotl_extras:
-            platforms: "linux/amd64,linux/arm64"
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"

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

@@ -3,17 +3,15 @@ name: docker-multigpu-tests-biweekly
 on:
   pull_request:
     paths:
-      - 'tests/e2e/multigpu/**.py'
+      - "tests/e2e/multigpu/**.py"
-      - 'requirements.txt'
+      - "pyproject.toml"
-      - 'setup.py'
+      - ".github/workflows/multi-gpu-e2e.yml"
-      - 'pyproject.toml'
+      - "scripts/cutcrossentropy_install.py"
-      - '.github/workflows/multi-gpu-e2e.yml'
+      - "src/axolotl/core/trainers/mixins/sequence_parallel.py"
-      - 'scripts/cutcrossentropy_install.py'
+      - "src/axolotl/utils/distributed.py"
-      - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
-      - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
   schedule:
-    - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
+    - cron: "0 0 * * 1,4" # Runs at 00:00 UTC every monday & thursday
 
 # Cancel jobs on the same ref if a new one is triggered
 concurrency:
@@ -33,19 +31,19 @@ jobs:
       fail-fast: false
       matrix:
         include:
-#          - cuda: 129
+          #          - cuda: 129
-#            cuda_version: 12.9.1
+          #            cuda_version: 12.9.1
-#            python_version: "3.12"
+          #            python_version: "3.12"
-#            pytorch: 2.9.1
+          #            pytorch: 2.9.1
-#            axolotl_extras: "fbgemm-gpu"
+          #            axolotl_extras: "fbgemm-gpu"
-#            num_gpus: 2
+          #            num_gpus: 2
-#            dockerfile: "Dockerfile-uv.jinja"
+          #            dockerfile: "Dockerfile-uv.jinja"
           - cuda: 130
             cuda_version: 13.0.0
             python_version: "3.11"
             pytorch: 2.9.1
             axolotl_extras:
-#            axolotl_extras: fbgemm-gpu
+            #            axolotl_extras: fbgemm-gpu
             num_gpus: 2
           - cuda: 128
             cuda_version: 12.8.1
@@ -53,7 +51,6 @@ jobs:
             pytorch: 2.10.0
             axolotl_extras: "fbgemm-gpu"
             num_gpus: 2
-            dockerfile: "Dockerfile-uv.jinja"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -75,7 +72,7 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
+          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile-uv.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/pypi.yml

@@ -8,6 +8,9 @@ on:
 
 permissions: {}
 
+env:
+  UV_SYSTEM_PYTHON: "1"
+
 jobs:
   setup_release:
     name: Create Release
@@ -41,11 +44,15 @@ jobs:
         with:
           python-version: "3.11"
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+
       - name: Install dependencies
         run: |
-          pip3 install wheel packaging==26.0
+          uv pip install wheel packaging
-          pip3 install --no-build-isolation -e .
+          uv pip install --no-build-isolation -e .
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+          uv pip install black mypy pre-commit types-requests quartodoc jupyter blobfile tiktoken \
+            codecov codecov-cli pytest pytest-cov pytest-retry pytest-sugar pytest-xdist tbparse
 
       - name: Extract tag name
         id: tag

.github/workflows/tests-nightly.yml

@@ -2,15 +2,18 @@ name: Tests Nightly against upstream main
 on:
   workflow_dispatch:
   schedule:
-    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+    - cron: "0 0 * * *" # Runs at 00:00 UTC every day
   pull_request:
     types: [opened, synchronize, reopened, ready_for_review]
     paths:
-      - '.github/workflows/tests-nightly.yml'
+      - ".github/workflows/tests-nightly.yml"
 
 permissions:
   contents: read
 
+env:
+  UV_SYSTEM_PYTHON: "1"
+
 jobs:
   pre-commit:
     name: pre-commit
@@ -20,7 +23,7 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-          cache: 'pip' # caching pip dependencies
+          cache: "pip" # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
@@ -43,7 +46,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python_version: ["3.12"]  # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
+        python_version: ["3.12"] # TODO include py3.14 once https://github.com/mistralai/mistral-common/pull/194 is merged
         pytorch_version: ["2.9.1", "2.10.0"]
     timeout-minutes: 20
 
@@ -61,36 +64,34 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
+      - name: Install uv
-        run: |
+        uses: astral-sh/setup-uv@v7
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==78.1.1 wheel
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install torch==${{ matrix.pytorch_version }} torchvision
-
+          uv pip freeze | grep -E "^(torch|torchvision)==" > /tmp/torch-pin.txt
-      - name: Update requirements.txt
-        run: |
-          sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt
-          sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt
-          sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt
-          sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt
-          sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt
 
       - name: Install dependencies
         run: |
-          pip3 show torch
+          uv pip install --no-build-isolation -e . --override /tmp/torch-pin.txt
-          pip3 install --no-build-isolation -U -e .
+          python scripts/cutcrossentropy_install.py --uv | sh
-          python scripts/unsloth_install.py | sh
+          uv pip install black mypy pre-commit types-requests quartodoc jupyter blobfile tiktoken \
-          python scripts/cutcrossentropy_install.py | sh
+            codecov codecov-cli pytest pytest-cov pytest-retry pytest-sugar pytest-xdist tbparse
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+
+      - name: Override with nightly HF packages
+        run: |
+          uv pip install --no-deps \
+            "transformers @ git+https://github.com/huggingface/transformers.git@main" \
+            "peft @ git+https://github.com/huggingface/peft.git@main" \
+            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
+            "trl @ git+https://github.com/huggingface/trl.git@main" \
+            "datasets @ git+https://github.com/huggingface/datasets.git@main"
 
       - name: Make sure PyTorch version wasn't clobbered
         run: |
-          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__"
+          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__, f'Expected torch ${{ matrix.pytorch_version }} but got {torch.__version__}'"
 
       - name: Ensure axolotl CLI was installed
         run: |
@@ -102,9 +103,6 @@ jobs:
           pytest -v --durations=10 tests/patched/
           pytest -v --durations=10 tests/cli/
 
-      - name: cleanup pip cache
-        run: |
-          find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests:
     if: github.repository_owner == 'axolotl-ai-cloud'
@@ -136,7 +134,6 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
-            dockerfile: "Dockerfile-uv.jinja"
             nightly_build: "true"
     steps:
       - name: Checkout
@@ -157,7 +154,7 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
+          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile-uv.jinja'}}" >> $GITHUB_ENV
           echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         env:

.github/workflows/tests.yml

@@ -6,21 +6,19 @@ on:
     branches:
       - "main"
     paths:
-      - '**.py'
+      - "**.py"
-      - 'requirements.txt'
+      - "pyproject.toml"
-      - '.github/workflows/*.yml'
+      - ".github/workflows/*.yml"
-      - 'requirements-tests.txt'
+      - "cicd/cicd.sh"
-      - 'cicd/cicd.sh'
+      - "cicd/Dockerfile-uv.jinja"
-      - 'cicd/Dockerfile.jinja'
   pull_request:
-      types: [opened, synchronize, reopened, ready_for_review]
+    types: [opened, synchronize, reopened, ready_for_review]
-      paths:
+    paths:
-       - '**.py'
+      - "**.py"
-       - 'requirements.txt'
+      - "pyproject.toml"
-       - '.github/workflows/*.yml'
+      - ".github/workflows/*.yml"
-       - 'requirements-tests.txt'
+      - "cicd/cicd.sh"
-       - 'cicd/cicd.sh'
+      - "cicd/Dockerfile-uv.jinja"
-       - 'cicd/Dockerfile.jinja'
   workflow_dispatch:
 
 # Cancel jobs on the same ref if a new one is triggered
@@ -33,6 +31,7 @@ permissions:
 
 env:
   TRANSFORMERS_IS_CI: "yes"
+  UV_SYSTEM_PYTHON: "1"
 
 jobs:
   pre-commit:
@@ -44,7 +43,7 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-          cache: 'pip' # caching pip dependencies
+          cache: "pip" # caching pip dependencies
       - uses: pre-commit/action@v3.0.1
         env:
           SKIP: no-commit-to-branch
@@ -73,7 +72,7 @@ jobs:
         exclude:
           - python_version: "3.14"
             pytorch_version: "2.9.1"
-    timeout-minutes: 20
+    timeout-minutes: 25
 
     steps:
       - name: cleanup node
@@ -94,32 +93,25 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
+      - name: Install uv
-        run: |
+        uses: astral-sh/setup-uv@v7
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 wheel
 
       - name: Install PyTorch
         run: |
-          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install torch==${{ matrix.pytorch_version }} torchvision
+          uv pip freeze | grep -E "^(torch|torchvision)==" > /tmp/torch-pin.txt
 
       - name: Install dependencies
         run: |
-          pip3 show torch
+          uv pip install --no-build-isolation -e . --override /tmp/torch-pin.txt
-          pip3 install --no-cache-dir --no-build-isolation -U -e .
+          python scripts/cutcrossentropy_install.py --uv | sh
-          python scripts/unsloth_install.py | sh
+          uv pip install black mypy pre-commit types-requests quartodoc jupyter blobfile tiktoken \
-          python scripts/cutcrossentropy_install.py | sh
+            codecov codecov-cli pytest pytest-cov pytest-retry pytest-sugar pytest-xdist tbparse
-          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 '${{ matrix.pytorch_version }}' in torch.__version__"
+          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__, f'Expected torch ${{ matrix.pytorch_version }} but got {torch.__version__}'"
 
       - name: Ensure axolotl CLI was installed
         run: |
@@ -188,33 +180,27 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python_version }}
-          cache: 'pip' # caching pip dependencies
 
-      - name: upgrade pip
+      - name: Install uv
-        run: |
+        uses: astral-sh/setup-uv@v7
-          pip3 install --upgrade pip
-          pip3 install --upgrade packaging==26.0 setuptools==75.8.0 setuptools_scm build wheel psutil
 
       - name: Install PyTorch
         run: |
-          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
+          uv pip install torch==${{ matrix.pytorch_version }} torchvision
+          uv pip freeze | grep -E "^(torch|torchvision)==" > /tmp/torch-pin.txt
 
       - name: Install dependencies
         run: |
-          pip3 show torch
+          uv pip install packaging setuptools_scm build wheel psutil
           python -m build --no-isolation --sdist
-          pip3 install --no-cache-dir --no-build-isolation dist/axolotl*.tar.gz
+          uv pip install --no-build-isolation dist/axolotl*.tar.gz --override /tmp/torch-pin.txt
-          python scripts/unsloth_install.py | sh
+          python scripts/cutcrossentropy_install.py --uv | sh
-          python scripts/cutcrossentropy_install.py | sh
+          uv pip install black mypy pre-commit types-requests quartodoc jupyter blobfile tiktoken \
-          pip3 install -r requirements-dev.txt -r requirements-tests.txt
+            codecov codecov-cli pytest pytest-cov pytest-retry pytest-sugar pytest-xdist tbparse
-
-      - 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 '${{ matrix.pytorch_version }}' in torch.__version__"
+          python -c "import torch; assert '${{ matrix.pytorch_version }}' in torch.__version__, f'Expected torch ${{ matrix.pytorch_version }} but got {torch.__version__}'"
 
       - name: Ensure axolotl CLI was installed
         run: |
@@ -291,7 +277,6 @@ jobs:
             pytorch: 2.9.1
             num_gpus: 1
             axolotl_extras:
-            dockerfile: "Dockerfile-uv.jinja"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -312,7 +297,7 @@ jobs:
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
+          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile-uv.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
@@ -374,7 +359,7 @@ jobs:
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
           echo "GPU_TYPE=${{ matrix.gpu_type || 'L40S'}}" >> $GITHUB_ENV
-          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
+          echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile-uv.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

AGENTS.md

@@ -26,7 +26,7 @@ axolotl config-schema                  # Dump config JSON schema
 | Method | Config Key | When to Use |
 |--------|-----------|-------------|
 | SFT | *(default)* | Input-output pairs, instruction tuning |
-| DPO/IPO | `rl: dpo` / `rl: ipo` | Paired preference data (chosen vs rejected) |
+| DPO/IPO | `rl: dpo` / `rl: dpo, dpo_loss_type: ["ipo"]` | Paired preference data (chosen vs rejected) |
 | KTO | `rl: kto` | Unpaired binary preference labels |
 | ORPO | `rl: orpo` | Single-stage alignment, no ref model |
 | GRPO | `rl: grpo` | RL with verifiable reward functions (math, code) |

MANIFEST.in

@@ -1,7 +1,6 @@
-include requirements.txt
 include README.md
 include LICENSE
-include src/setuptools_axolotl_dynamic_dependencies.py
+include VERSION
 include src/axolotl/utils/chat_templates/templates/*.jinja
 include AGENTS.md
 recursive-include docs/agents *.md

README.md

@@ -29,6 +29,9 @@
 
 ## 🎉 Latest Updates
 
+- 2026/04:
+  - New model support has been added in Axolotl for [Mistral Medium 3.5](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/mistral-medium-3_5) and [Gemma 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma4).
+  - Axolotl is now [uv-first](https://github.com/axolotl-ai-cloud/axolotl/pull/3545) and has [SonicMoE fused LoRA](https://github.com/axolotl-ai-cloud/axolotl/pull/3519) support.
 - 2026/03:
   - New model support has been added in Axolotl for [Mistral Small 4](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/mistral4), [Qwen3.5, Qwen3.5 MoE](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5), [GLM-4.7-Flash](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash), [GLM-4.6V](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v), and [GLM-4.5-Air](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45).
   - [MoE expert quantization](https://docs.axolotl.ai/docs/expert_quantization.html) support (via `quantize_moe_experts: true`) greatly reduces VRAM when training MoE models (FSDP2 compat).
@@ -95,14 +98,11 @@ Features:
 
 ### Installation
 
-#### Using uv (recommended)
-
 ```bash
-# install uv if you don't already have it installed
+# install uv if you don't already have it installed (restart shell after)
 curl -LsSf https://astral.sh/uv/install.sh | sh
-source $HOME/.local/bin/env
 
-# CUDA 12.8.1 tends to have better package compatibility
+# change depending on system
 export UV_TORCH_BACKEND=cu128
 
 # create a new virtual environment
@@ -112,23 +112,6 @@ source .venv/bin/activate
 uv pip install torch==2.10.0 torchvision
 uv pip install --no-build-isolation axolotl[deepspeed]
 
-# recommended - install cut-cross-entropy
-uv pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@main"
-
-# (optional) - prefetch flash-attn2 and causal-conv1d kernels
-uv run --python 3.12 python -c "from kernels import get_kernel; get_kernel('kernels-community/flash-attn2'); get_kernel('kernels-community/causal-conv1d')"
-
-# Download example axolotl configs, deepspeed configs
-axolotl fetch examples
-axolotl fetch deepspeed_configs  # OPTIONAL
-```
-
-#### Using pip
-
-```bash
-pip3 install -U packaging==26.0 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
-
 # Download example axolotl configs, deepspeed configs
 axolotl fetch examples
 axolotl fetch deepspeed_configs  # OPTIONAL
@@ -138,7 +121,7 @@ axolotl fetch deepspeed_configs  # OPTIONAL
 
 Installing with Docker can be less error prone than installing in your own environment.
 ```bash
-docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+docker run --gpus '"all"' --ipc=host --rm -it axolotlai/axolotl:main-latest
 ```
 
 Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).

SETUP_MIAAI.md

@@ -0,0 +1,273 @@
+# Axolotl Setup — miaai (RTX 5080, CUDA 13.2)
+
+## System Info
+- GPU: NVIDIA RTX 5080 (16GB VRAM, sm_120 / Blackwell)
+- Driver: 580.126.09 — max CUDA 13.0 shown by nvidia-smi, but nvcc from conda is 13.2
+- OS: Ubuntu 25.10 (Python 3.13 system — do NOT use system Python for ML)
+- Axolotl repo: `/home/tocmo0nlord/axolotl` (branch: `activeblue/main`)
+- Conda env: `axolotl` at `/opt/miniconda3/envs/axolotl`
+
+---
+
+## Starting from Bare Ubuntu 25.10
+
+If rebuilding from scratch, complete these steps first before anything else.
+
+### A. System packages
+```bash
+sudo apt update && sudo apt upgrade -y
+sudo apt install -y \
+  build-essential cmake git curl wget \
+  python3-dev libssl-dev zlib1g-dev \
+  ca-certificates gnupg lsb-release
+```
+
+### B. NVIDIA driver (580.xx)
+Ubuntu 25.10 is too new for NVIDIA's apt repo. Install via ubuntu-drivers:
+```bash
+sudo ubuntu-drivers autoinstall
+sudo reboot
+```
+
+After reboot, verify:
+```bash
+nvidia-smi
+# Must show: NVIDIA GeForce RTX 5080, Driver Version: 580.x
+```
+
+If ubuntu-drivers installs the wrong version, force the right one:
+```bash
+sudo apt install -y nvidia-driver-580
+sudo reboot
+```
+
+### C. Install Ollama
+```bash
+curl -fsSL https://ollama.com/install.sh | sh
+
+# Verify it's running
+systemctl status ollama
+```
+
+### D. HuggingFace CLI
+```bash
+pip3 install huggingface_hub
+huggingface-cli login
+# Paste your HF token — required for gated models like meta-llama
+```
+
+Once steps A–D are done, continue with the One-time Setup below.
+
+---
+
+## Pre-Training Checklist (every session)
+
+```bash
+# 1. Stop Ollama — if it receives a request mid-training it will compete for VRAM
+sudo systemctl stop ollama
+
+# 2. Activate conda env
+export PATH="/opt/miniconda3/bin:$PATH"
+conda activate axolotl
+
+# 3. Set env vars
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+
+# 4. Confirm GPU is clear (should show no processes before training)
+nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
+
+# 5. Go to axolotl directory
+cd /home/tocmo0nlord/axolotl
+```
+
+## Run Training
+```bash
+axolotl train ~/human_chat_qlora.yml
+```
+
+## After Training
+```bash
+# Restart Ollama
+sudo systemctl start ollama
+
+# Test the adapter interactively
+axolotl inference ~/human_chat_qlora.yml \
+  --lora-model-dir ~/outputs/llama31-8b-humanchat \
+  --prompter chat
+
+# (Optional) Merge adapter into base model for standalone deployment
+axolotl merge-lora ~/human_chat_qlora.yml
+```
+
+---
+
+## One-time Setup (fresh machine — after bare Ubuntu steps above)
+
+### 1. Install Miniconda
+```bash
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+bash miniconda.sh -b -p /opt/miniconda3
+/opt/miniconda3/bin/conda init bash
+source ~/.bashrc
+```
+
+### 2. Create Python 3.11 environment
+```bash
+conda create -n axolotl python=3.11 -y
+conda activate axolotl
+```
+
+### 3. Clone axolotl repo
+```bash
+git clone https://git.activeblue.net/tocmo0nlord/axolotl.git /home/tocmo0nlord/axolotl
+cd /home/tocmo0nlord/axolotl
+git remote add upstream https://github.com/axolotl-ai-cloud/axolotl.git
+git fetch upstream
+git rebase upstream/main        # keeps activeblue patches on top
+```
+
+### 4. Install CUDA toolkit (needed to compile flash-attn and bitsandbytes)
+```bash
+conda install -y -c "nvidia/label/cuda-12.8.0" cuda-toolkit
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+```
+
+> NOTE: Despite installing from the cuda-12.8.0 channel, conda resolves nvcc to **13.2.78**.
+> This is fine — use cu132 everywhere to match.
+
+### 5. Install PyTorch — use cu132 (matches nvcc from conda)
+```bash
+# torchaudio has no cu132 wheel — skip it, not needed for LLM training
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu132
+python -c "import torch; print('CUDA:', torch.version.cuda); print('GPU:', torch.cuda.get_device_name(0))"
+```
+
+### 6. Install Axolotl
+```bash
+cd /home/tocmo0nlord/axolotl
+pip install -e "."
+```
+
+### 7. Install flash-attn
+> Compiles CUDA kernels from source — takes 15–25 min on 10 cores of i7-14700K.
+```bash
+MAX_JOBS=10 pip install flash-attn --no-build-isolation
+```
+
+### 8. Compile bitsandbytes from source for sm_120 (RTX 5080 / Blackwell)
+
+Prebuilt wheels do not include sm_120. CUDA 13.2 also dropped sm_50–53.
+Must compile from source with a patched CMakeLists.txt.
+
+```bash
+# Clone bitsandbytes v0.49.1
+git clone --branch v0.49.1 --depth 1 \
+  https://github.com/bitsandbytes-foundation/bitsandbytes.git /tmp/bnb_0491
+
+# Patch CMakeLists.txt: insert sm_120 override before the foreach loop
+# (cmake >= 3.23.0 uses its own built-in arch list which does not include sm_120)
+sed -i '/    foreach(capability \${CMAKE_CUDA_ARCHITECTURES_ALL})/i\    # RTX 5080 sm_120 patch\n    set(CMAKE_CUDA_ARCHITECTURES_ALL 120)' /tmp/bnb_0491/CMakeLists.txt
+
+# Verify patch landed correctly — set() line must appear immediately before foreach
+grep -n "ARCHITECTURES_ALL\|foreach" /tmp/bnb_0491/CMakeLists.txt | tail -5
+
+# Configure — must point cmake at conda's nvcc explicitly
+cmake \
+  -DCMAKE_CUDA_COMPILER=/opt/miniconda3/envs/axolotl/bin/nvcc \
+  -DCOMPUTE_BACKEND=cuda \
+  -S /tmp/bnb_0491 \
+  -B /tmp/bnb_0491/build 2>&1 | grep -E "(Capabilit|CUDA Ver|Error)"
+# Must show: CUDA Capabilities Selected: 120
+
+# Build (adjust -j to your CPU core count)
+cmake --build /tmp/bnb_0491/build -j10
+
+# Install into conda site-packages
+cp -r /tmp/bnb_0491/bitsandbytes \
+  /opt/miniconda3/envs/axolotl/lib/python3.11/site-packages/
+
+# Verify CUDA works
+python3 -c "
+import torch, bitsandbytes as bnb
+x = torch.randn(64, 64, device='cuda')
+l = bnb.nn.Linear8bitLt(64, 64).cuda()
+print('bitsandbytes CUDA OK:', l(x).shape)
+"
+```
+
+### 9. Copy training config to home
+```bash
+cp /home/tocmo0nlord/axolotl/human_chat_qlora.yml ~/human_chat_qlora.yml
+```
+
+### 10. Verify the full stack
+```bash
+python3 -c "
+import torch, bitsandbytes as bnb, flash_attn, transformers
+print('torch      :', torch.__version__, '| CUDA:', torch.version.cuda)
+print('bitsandbytes:', bnb.__version__)
+print('flash_attn :', flash_attn.__version__)
+print('transformers:', transformers.__version__)
+print('GPU        :', torch.cuda.get_device_name(0))
+print('VRAM       :', round(torch.cuda.get_device_properties(0).total_memory/1e9, 1), 'GB')
+"
+```
+
+Expected output:
+```
+torch      : 2.x.x | CUDA: 13.2
+bitsandbytes: 0.50.0.dev0
+flash_attn : 2.x.x
+transformers: 5.x.x
+GPU        : NVIDIA GeForce RTX 5080
+VRAM       : 16.3 GB
+```
+
+---
+
+## Training Config — human_chat_qlora.yml
+
+Key settings tuned for RTX 5080 (16GB):
+
+| Setting | Value | Notes |
+|---|---|---|
+| `sequence_len` | `2048` | 4096 OOMs during loss computation (logits x 128k vocab) |
+| `micro_batch_size` | `1` | Effective batch = micro x grad_accum = 8 |
+| `gradient_accumulation_steps` | `8` | Keeps effective batch size at 8 |
+| `adapter` | `qlora` | 4-bit via bitsandbytes compiled from source |
+| `attn_implementation` | `flash_attention_2` | Not the deprecated `flash_attention: true` |
+| `type` (datasets) | `chat_template` | Not the deprecated `sharegpt` |
+
+Expected training metrics (RTX 5080, ~65k samples, 2 epochs):
+- VRAM: ~10–11 GB active, ~11 GB allocated
+- Training duration: ~3.5 hours
+- Initial eval loss: ~0.81, perplexity ~2.25
+- Final loss target: ~0.55–0.60
+
+To push VRAM to ~14GB and improve training: set `micro_batch_size: 2` and `gradient_accumulation_steps: 4`.
+
+---
+
+## Common Pitfalls
+
+| Problem | Cause | Fix |
+|---|---|---|
+| `externally-managed-environment` | System Python 3.13 blocks pip | Use conda env, never system pip |
+| `No module named torch` (flash-attn) | pip builds in isolated env | Use `--no-build-isolation` |
+| `CUDA_HOME not set` | CUDA toolkit not installed | `conda install cuda-toolkit` from nvidia channel |
+| `CUDA version mismatch 13.2 vs 12.8` | Conda nvcc is 13.2, torch was cu128 | Reinstall torch with `--index-url .../cu132` |
+| `torchaudio` not found for cu132 | No cu132 wheel exists | Skip torchaudio — not needed |
+| flash-attn compile is slow | Single-threaded by default | Set `MAX_JOBS=<cpu_count>` before pip install |
+| `nvcc fatal: Unsupported gpu architecture 'compute_50'` | bitsandbytes CMakeLists.txt hardcodes sm_50; CUDA 13.2 dropped it | Patch CMakeLists.txt (see step 8 above) |
+| `CUDA Capabilities Selected: 50;52;...` ignores -D flag | cmake >= 3.23 built-in arch list lacks sm_120; CMakeLists.txt overrides -D | Insert `set(CMAKE_CUDA_ARCHITECTURES_ALL 120)` before foreach loop |
+| `BackendUnavailable: scikit_build_core` | pip install of bnb triggers cmake rebuild | Copy .so directly to site-packages instead |
+| `torch.OutOfMemoryError` during eval | logits tensor (batch x 4096 x 128k vocab) too large | Set `sequence_len: 2048`, `micro_batch_size: 1` |
+| `type: sharegpt` deprecation warning | axolotl removed sharegpt type | Use `type: chat_template` with field mappings |
+| `flash_attention: true` deprecation | Old config key removed | Use `attn_implementation: flash_attention_2` |
+| Capybara dataset `field_messages null` | Capybara uses input/output format, not conversations | Switch to SlimOrca or OpenHermes-2.5 |
+| Ollama loads model mid-training | Ollama is enabled and receives a request | `sudo systemctl stop ollama` before training |
+| Training much slower than eval speed | The fast it/s on screen is the eval loop (forward only) | Normal — training includes backward pass and optimizer (~3.5h total) |
+| ubuntu-drivers installs wrong NVIDIA version | Multiple driver candidates available | Force with `apt install nvidia-driver-580` |

VERSION

@@ -1 +1 @@
-0.16.0.dev0
+0.16.2.dev0

_quarto.yml

@@ -134,7 +134,6 @@ quartodoc:
         - monkeypatch.stablelm_attn_hijack_flash
         - monkeypatch.trainer_fsdp_optim
         - monkeypatch.transformers_fa_utils
-        - monkeypatch.unsloth_
         - monkeypatch.data.batch_dataset_fetcher
         - monkeypatch.mixtral
         - monkeypatch.gradient_checkpointing.offload_cpu
@@ -312,6 +311,7 @@ website:
             - docs/dataset_loading.qmd
             - docs/qat.qmd
             - docs/quantize.qmd
+            - docs/1_58bit_finetuning.qmd
             - docs/optimizations.qmd
 
         - section: "Core Concepts"
@@ -327,7 +327,6 @@ website:
         - section: "Advanced Features"
           contents:
             - docs/fsdp_qlora.qmd
-            - docs/unsloth.qmd
             - docs/torchao.qmd
             - docs/custom_integrations.qmd
             - docs/sequence_parallelism.qmd

cicd/Dockerfile-uv.jinja

@@ -22,15 +22,6 @@ WORKDIR /workspace/axolotl
 RUN git fetch origin +$GITHUB_REF && \
     git checkout FETCH_HEAD
 
-# If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
-        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
-        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
-        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
-        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
-        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
-    fi
-
 RUN uv pip install packaging==26.0 setuptools==78.1.1
 RUN uv pip install torchvision
 RUN uv pip uninstall causal_conv1d
@@ -40,11 +31,21 @@ RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
     fi
 
-RUN python scripts/unsloth_install.py --uv | sh
+# Override with nightly HF packages for nightly builds
+RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
+        uv pip install --no-deps \
+            "transformers @ git+https://github.com/huggingface/transformers.git@main" \
+            "peft @ git+https://github.com/huggingface/peft.git@main" \
+            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
+            "trl @ git+https://github.com/huggingface/trl.git@main" \
+            "datasets @ git+https://github.com/huggingface/datasets.git@main"; \
+    fi
+
 RUN python scripts/cutcrossentropy_install.py --uv | sh
 
 # So we can test the Docker image
-RUN uv pip install -r requirements-dev.txt -r requirements-tests.txt
+RUN uv pip install black mypy pre-commit types-requests quartodoc jupyter blobfile tiktoken \
+    codecov codecov-cli pytest pytest-cov pytest-retry pytest-sugar pytest-xdist tbparse
 
 # fix so that git fetch/pull from remote works
 RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \

cicd/Dockerfile.jinja

@@ -1,54 +0,0 @@
-FROM axolotlai/axolotl-base:{{ BASE_TAG }}
-
-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 }}"
-ENV PYTORCH_VERSION="{{ PYTORCH_VERSION }}"
-ENV GITHUB_REF="{{ GITHUB_REF }}"
-ENV GITHUB_SHA="{{ GITHUB_SHA }}"
-ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
-ENV HF_HOME="{{ HF_HOME }}"
-ENV AXOLOTL_DATASET_NUM_PROC="8"
-
-RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano zstd libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
-
-WORKDIR /workspace
-
-RUN git clone --depth=1 https://github.com/axolotl-ai-cloud/axolotl.git
-
-WORKDIR /workspace/axolotl
-
-RUN git fetch origin +$GITHUB_REF && \
-    git checkout FETCH_HEAD
-
-# If AXOLOTL_EXTRAS is set, append it in brackets
-RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
-        sed -i 's#^transformers.*#transformers @ git+https://github.com/huggingface/transformers.git@main#' requirements.txt; \
-        sed -i 's#^peft.*#peft @ git+https://github.com/huggingface/peft.git@main#' requirements.txt; \
-        sed -i 's#^accelerate.*#accelerate @ git+https://github.com/huggingface/accelerate.git@main#' requirements.txt; \
-        sed -i 's#^trl.*#trl @ git+https://github.com/huggingface/trl.git@main#' requirements.txt; \
-        sed -i 's#^datasets.*#datasets @ git+https://github.com/huggingface/datasets.git@main#' requirements.txt; \
-    fi
-
-RUN pip install packaging==26.0 setuptools==78.1.1 psutil
-RUN pip uninstall -y causal_conv1d
-RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
-    else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
-    fi
-
-RUN python scripts/unsloth_install.py | sh
-RUN python scripts/cutcrossentropy_install.py | sh
-
-# So we can test the Docker image
-RUN pip install -r requirements-dev.txt -r requirements-tests.txt
-
-# fix so that git fetch/pull from remote works
-RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
-    git config --get remote.origin.fetch
-
-# helper for huggingface-login cli
-RUN git config --global credential.helper store

cicd/cicd.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 set -e
 
-python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
+python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__, f'Expected torch $PYTORCH_VERSION but got {torch.__version__}'"
 
 set -o pipefail
 for i in 1 2 3; do

cicd/multigpu.py

@@ -17,7 +17,7 @@ template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
 template_env = jinja2.Environment(
     loader=template_loader, autoescape=select_autoescape()
 )
-dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
+dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile-uv.jinja")
 df_template = template_env.get_template(dockerfile)
 
 df_args = {

cicd/single_gpu.py

@@ -16,7 +16,7 @@ template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
 template_env = jinja2.Environment(
     loader=template_loader, autoescape=select_autoescape()
 )
-dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile.jinja")
+dockerfile = os.environ.get("E2E_DOCKERFILE", "Dockerfile-uv.jinja")
 df_template = template_env.get_template(dockerfile)
 
 df_args = {

docker/Dockerfile

@@ -24,15 +24,15 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
 RUN pip uninstall -y causal_conv1d
 RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
         pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
-    fi && \    python scripts/unsloth_install.py | sh && \
+    fi && \
     python scripts/cutcrossentropy_install.py | sh && \
     pip install pytest && \
     pip cache purge

docker/Dockerfile-base

@@ -58,19 +58,3 @@ RUN git lfs install --skip-repo && \
     # 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
-
-# Map Python version (e.g., 3.12 -> cp312)
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
-    # Map architecture
-    case "$TARGETARCH" in \
-        amd64) ARCH_TAG="x86_64" ;; \
-        arm64) ARCH_TAG="aarch64" ;; \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
-    esac && \
-    WHL_VERSION="v0.7.16" && \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-linux_${ARCH_TAG}.whl" && \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
-    pip3 install --no-cache-dir "${WHL_FILE}" && \
-    rm "${WHL_FILE}"

docker/Dockerfile-base-next

@@ -1,16 +1,15 @@
-ARG CUDA_VERSION="12.8.1"
+ARG CUDA_VERSION="12.8.2"
-ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
 
-FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
+FROM nvidia/cuda:12.8.2-devel-ubuntu22.04 AS base-builder
 
-ENV PATH="/root/miniconda3/bin:${PATH}"
+ENV PATH="/root/miniforge3/bin:${PATH}"
 
 ARG PYTHON_VERSION="3.11"
 ARG PYTORCH_VERSION="next"
 ARG CUDA="128"
-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0 12.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
@@ -18,13 +17,13 @@ ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 RUN apt-get update \
     && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
     && wget \
-    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh \
     && mkdir /root/.conda \
-    && bash Miniconda3-latest-Linux-x86_64.sh -b \
+    && bash Miniforge3-Linux-x86_64.sh -b \
-    && rm -f Miniconda3-latest-Linux-x86_64.sh \
+    && rm -f Miniforge3-Linux-x86_64.sh \
-    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+    && /root/miniforge3/bin/conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
 
-ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+ENV PATH="/root/miniforge3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 
 WORKDIR /workspace
 

docker/Dockerfile-tests

@@ -24,9 +24,9 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docker/Dockerfile-uv

@@ -24,16 +24,15 @@ WORKDIR /workspace/axolotl
 # If AXOLOTL_EXTRAS is set, append it in brackets; don't install deepspeed with arm64
 RUN uv pip uninstall causal_conv1d
 RUN if [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         uv pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
         uv pip install --no-build-isolation -e .[$BASE_EXTRAS] $AXOLOTL_ARGS; \
     fi && \
-    python scripts/unsloth_install.py --uv | sh && \
     python scripts/cutcrossentropy_install.py --uv | sh && \
     uv pip install pytest && \
     uv cache clean

docker/Dockerfile-uv-base

@@ -38,20 +38,3 @@ RUN uv pip install packaging setuptools wheel psutil \
 RUN if [ "$TARGETARCH" = "amd64" ]; then \
         MAMBA_SKIP_CUDA_BUILD=TRUE CAUSAL_CONV1D_SKIP_CUDA_BUILD=TRUE uv pip install --no-build-isolation mamba_ssm causal_conv1d; \
     fi
-
-# Map Python version (e.g., 3.12 -> cp312)
-RUN PYTHON_CP="cp$(echo $PYTHON_VERSION | tr -d '.')" && \
-    # Map PyTorch version (e.g., 2.9.1 -> torch2.9, 2.10.0 -> torch2.10)
-    TORCH_TAG="torch$(echo $PYTORCH_VERSION | grep -oP '^\d+\.\d+')" && \
-    LINUX_TAG="manylinux_" && \
-    # Map architecture
-    case "$TARGETARCH" in \
-        amd64) ARCH_TAG="2_24_x86_64.manylinux_2_28_x86_64" ;; \
-        arm64) ARCH_TAG="2_34_aarch64" ;; \
-        *) echo "Unsupported architecture: $TARGETARCH"; exit 1 ;; \
-    esac && \
-    WHL_VERSION="v0.7.16" && \
-    WHL_FILE="flash_attn-2.8.3+cu${CUDA}${TORCH_TAG}-${PYTHON_CP}-${PYTHON_CP}-${LINUX_TAG}${ARCH_TAG}.whl" && \
-    wget -nv "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/${WHL_VERSION}/${WHL_FILE}" && \
-    uv pip install --no-cache-dir "${WHL_FILE}" && \
-    rm "${WHL_FILE}"

docs/1_58bit_finetuning.qmd

@@ -0,0 +1,70 @@
+---
+title: "1.58-bit Finetuning"
+back-to-top-navigation: true
+toc: true
+toc-expand: 2
+toc-depth: 4
+---
+
+## Overview
+
+1.58-bit finetuning allows you to finetune BitNet models when their prequantized weights are provided. In theory, it will be possible to fine-tune any LLM in 1.58bit format but the performance degradation will be dramatic.
+
+Axolotl supports 1.58-bit finetuning via the [`onebitllms`](https://github.com/tiiuae/onebitllms) library, which replaces standard linear layers with BitNet-compatible counterparts ready to use for training.
+
+::: {.callout-note}
+LoRA is not supported for BitNet models
+:::
+
+## Installation
+
+Install the `onebitllms` package before using this feature:
+
+```bash
+uv pip install onebitllms
+```
+
+Or from source:
+
+```bash
+uv pip install git+https://github.com/tiiuae/onebitllms
+```
+
+## Supported models
+
+For now, only `Falcon-E` series of models are supported. Make sure to use their `-prequantized` version:
+
+```bash
+tiiuae/Falcon-E-3B-Base-prequantized
+tiiuae/Falcon-E-1B-Base-prequantized
+```
+
+In theory, any other model would 'work' but the performance degradation will be huge. This remains an area of exploration.
+
+## Configuration
+
+To enable 1.58-bit finetuning, set the following in your configuration file:
+
+```yaml
+base_model: tiiuae/Falcon-E-3B-Base-prequantized  # A BitNet-compatible model
+
+use_onebitllms: true
+```
+
+::: {.callout-note}
+For BitNet models, it is recommended to use a higher learning rate than classic models (usually in the order of magnitude of 10x).
+:::
+
+## Considerations after training
+
+Once your model has been trained with 1.58bit fine-tuning, you can convert the trained model in ternary format using the `onebitllms` CLI:
+
+```bash
+onebitllms quantize_to_1bit INPUT_PATH OUTPUT_PATH
+```
+
+After that, you can use supported packages such as `llama.cpp` or Apple MLX package to run the trained model.
+
+## Example Configuration
+
+You can find example configurations in `examples/falcon-e` which contain one configuration for SFT and one configuration for DPO.

docs/agents/new_model_support.md

@@ -121,11 +121,11 @@ Older models that use `_prepare_4d_causal_attention_mask` (Llama, Mistral, Qwen2
 
 | Backend | Config | head_dim limit | torch_compile | Notes |
 |---------|--------|---------------|---------------|-------|
-| FA2 | `flash_attention: true` | 256 | ✅ | Fastest when supported |
+| FA2 | `attn_implementation: flash_attention_2` | 256 | ✅ | Fastest when supported |
-| FA4 | auto with `flash_attention: true` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
+| FA4 | auto with `attn_implementation: flash_attention_2` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
-| SDPA | `sdp_attention: true` | None | ✅ | Universal fallback |
+| SDPA | `attn_implementation: sdpa` | None | ✅ | Universal fallback |
-| flex | `flex_attention: true` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
+| flex | `attn_implementation: flex_attention` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
-| eager | neither set | None | ✅ | Slowest, always works |
+| eager | `attn_implementation: eager` | None | ✅ | Slowest, always works |
 
 **Check model support**: Look at `_supports_flash_attn_2`, `_supports_flex_attn`, `_supports_sdpa` attributes on the model class.
 

docs/agents/preference_tuning.md

@@ -38,7 +38,7 @@ No vLLM server needed (unlike GRPO). Offline RL with pre-collected preference da
 
 1. Paired preference data (chosen + rejected)?
    - Default → `rl: dpo`
-   - Overfitting → `rl: ipo`
+   - Overfitting → `rl: dpo, dpo_loss_type: ["ipo"]`
    - VRAM-limited → `rl: orpo` (no ref model)
    - Length-sensitive → `rl: simpo` (no ref model)
 2. Only binary labels (good/bad)? → `rl: kto`

docs/agents/sft.md

@@ -83,7 +83,7 @@ Watch for: loss never decreasing (check `train_on_inputs`, dataset, LR), loss go
 | Issue | Fix |
 |-------|-----|
 | OOM during training | Reduce `micro_batch_size`, enable `gradient_checkpointing`, reduce `sequence_len` |
-| `sample_packing` + SDPA + bf16 = 0.0 loss | Use `flash_attention: true` or disable `sample_packing` |
+| `sample_packing` + SDPA + bf16 = 0.0 loss | Use `attn_implementation: flash_attention_2` or disable `sample_packing` |
 | Missing chat template error | Set `chat_template: chatml` explicitly |
 | Label masking wrong | Run `axolotl preprocess config.yaml --debug` and inspect labels |
 | Loss NaN | Use `bf16: auto`, lower LR, check data for empty samples |

docs/attention.qmd

@@ -3,28 +3,71 @@ title: Attention
 description: Supported attention modules in Axolotl
 ---
 
-## SDP Attention
+Axolotl routes attention via a single config field:
-
-This is the default built-in attention in PyTorch.
 
 ```yaml
-sdp_attention: true
+attn_implementation: <backend>
 ```
 
-For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
+`attn_implementation` is passed through to `transformers` verbatim (via
+`model.config._attn_implementation`). Accepted values are the HF-native
+backends, axolotl-registered backends, or a hub-kernel path.
 
-## Flash Attention
+## Backends
 
-Axolotl supports Flash Attention 2, 3, and 4. The best available version is used automatically
+| `attn_implementation` | Description |
-based on your installed packages and GPU.
+|---|---|
+| `eager` | Plain PyTorch attention. No packing support. |
+| `sdpa` | PyTorch `scaled_dot_product_attention`. No packing support. |
+| `flash_attention_2` | Dao-AILab Flash Attention 2. |
+| `flash_attention_3` | Dao-AILab Flash Attention 3 (Hopper+). |
+| `flex_attention` | Torch Flex Attention (requires torch ≥ 2.6). |
+| `xformers` | xFormers memory-efficient attention. |
+| `sage` | SageAttention (QK int8 / PV fp16). |
+| `s2` | Shifted-Sparse Attention (LLaMA only, FA2 under the hood). |
+| `fp8` | torchao FP8 low-precision attention (requires SM90+, torch ≥ 2.11). Loaded as SDPA and patched post-load. |
+| `kernels-community/flash-attn3` | HF hub FA3 kernel. |
+| `kernels-community/sage-attention` | HF hub SageAttention kernel. |
+| Other `<org>/<name>` path | Any hub-kernel path supported by `transformers`. |
+
+Short-form aliases (`flash`, `fa2`, `flex`, `sdp`, etc.) are **not accepted** —
+set the canonical name above.
+
+### Capability flags
+
+Axolotl derives three boolean capability flags from `attn_implementation` and
+exposes them on the validated config:
+
+- `cfg.attn_supports_packing` — backend supports varlen sample packing via
+  `position_ids`. Gates multipack patches and `sample_packing_drop_attention_mask`.
+- `cfg.attn_uses_flash_lib` — backend needs the `flash_attn` (Dao-AILab)
+  monkeypatches (FA4 auto, LLaMA flash hijack, ring-FA).
+- `cfg.attn_needs_dtype_cast` — backend requires fp16/bf16 embeddings
+  (everything except `eager` and `sdpa`).
+
+These are **computed** — they cannot be overridden from YAML.
+
+## Per-backend notes
+
+### SDPA
+
+Default PyTorch attention. See
+[PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).
 
 ```yaml
-flash_attention: true
+attn_implementation: sdpa
 ```
 
-For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
+### Flash Attention
 
-### Flash Attention 2
+Axolotl supports FA2, FA3, and FA4. The best available version is used
+automatically based on your installed packages and GPU.
+
+```yaml
+attn_implementation: flash_attention_2  # or flash_attention_3
+```
+
+#### Flash Attention 2
 
 Requirements: Ampere, Ada, or Hopper GPUs (Turing or lower not supported)
 
@@ -39,23 +82,25 @@ Alternatively, try reinstall or downgrade a version.
 
 :::
 
-### Flash Attention 3
+#### Flash Attention 3
 
 Requirements: Hopper only and CUDA 12.8 (recommended)
 
 ```bash
 git clone https://github.com/Dao-AILab/flash-attention.git
 cd flash-attention/hopper
-
 python setup.py install
 ```
 
-### Flash Attention 4
+#### Flash Attention 4
 
-Requirements: Hopper or Blackwell GPUs
+Requirements: Hopper or Blackwell GPUs. Auto-applied when `attn_uses_flash_lib`
+is true and FA4 is importable.
+
+FA4 is still a pre-release on PyPI, so `--pre` is required:
 
 ```bash
-pip install flash-attn-4
+pip install --pre flash-attn-4
 ```
 
 Or from source:
@@ -63,7 +108,6 @@ Or from source:
 ```bash
 git clone https://github.com/Dao-AILab/flash-attention.git
 cd flash-attention/flash_attn/cute
-
 pip install -e .
 
 # FA2's flash_attn package includes a cute/ stub that shadows FA4.
@@ -86,93 +130,113 @@ and falls back to FA2/3.
 
 :::
 
-For more details: [flash-attention/flash_attn/cute](https://github.com/Dao-AILab/flash-attention/tree/main/flash_attn/cute)
-
 ### AMD
 
-Requirements: ROCm 6.0 and above.
+Requirements: ROCm 6.0 and above. See
+[Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
 
-See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
+### Flex Attention
-
-## Flex Attention
-
-A flexible PyTorch API for attention used in combination with `torch.compile`.
 
 ```yaml
-flex_attention: true
+attn_implementation: flex_attention
-
+torch_compile: true  # recommended
-# recommended
-torch_compile: true
 ```
 
-::: {.callout-note}
+Requires torch ≥ 2.6. See [PyTorch docs](https://pytorch.org/blog/flexattention/).
 
-We recommend using latest stable version of PyTorch for best performance.
+### SageAttention
 
-:::
+Requirements: Ampere, Ada, or Hopper GPUs.
-
-For more details: [PyTorch docs](https://pytorch.org/blog/flexattention/)
-
-## SageAttention
-
-Attention kernels with QK Int8 and PV FP16 accumulator.
 
 ```yaml
-sage_attention: true
+attn_implementation: sage
 ```
 
-Requirements: Ampere, Ada, or Hopper GPUs
-
 ```bash
 pip install sageattention==2.2.0 --no-build-isolation
 ```
 
 ::: {.callout-warning}
 
-Only LoRA/QLoRA recommended at the moment. We found loss drop to 0 for full finetuning. See [GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
+Only LoRA/QLoRA recommended. Full finetuning has been observed to drop loss to 0. See
+[GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).
 
 :::
 
-For more details: [Sage Attention](https://github.com/thu-ml/SageAttention)
+For more details: [Sage Attention](https://github.com/thu-ml/SageAttention).
 
-::: {.callout-note}
+### xFormers
-
-We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
-
-:::
-
-
-## xFormers
 
 ```yaml
-xformers_attention: true
+attn_implementation: xformers
 ```
 
 ::: {.callout-tip}
 
-We recommend using with Turing GPUs or below (such as on Colab).
+Recommended for Turing GPUs or below (e.g. Colab T4).
 
 :::
 
-For more details: [xFormers](https://github.com/facebookresearch/xformers)
+### Shifted Sparse Attention
-
-## Shifted Sparse Attention
 
 ::: {.callout-warning}
 
-We plan to deprecate this! If you use this feature, we recommend switching to methods above.
+Planned for deprecation. Prefer one of the backends above.
 
 :::
 
-Requirements: LLaMA model architecture
+Requirements: LLaMA model architecture. Loaded as FA2 under the hood and
+patched to implement shifted-sparse attention. Does not support sample packing.
 
 ```yaml
-flash_attention: true
+attn_implementation: s2
-s2_attention: true
 ```
 
-::: {.callout-tip}
+### FP8
 
-No sample packing support!
+torchao low-precision attention. Loaded as SDPA and patched post-load.
+
+Requirements: SM90+ (Hopper/Blackwell), PyTorch ≥ 2.11, torchao ≥ 0.17,
+flash-attn with FA3. KV caching must be disabled.
+
+```yaml
+attn_implementation: fp8
+```
+
+### Hub kernels
+
+```yaml
+attn_implementation: kernels-community/flash-attn3
+```
+
+Passed through to `transformers`; axolotl does not install the kernel itself.
+For recognized hub paths the capability flags are set automatically; for
+arbitrary paths axolotl uses conservative defaults (`attn_supports_packing=False`,
+`attn_uses_flash_lib=False`).
+
+## Migrating from legacy boolean flags
+
+The following legacy config fields are **deprecated** and will be removed in a
+future release. Each emits a `DeprecationWarning` when set and is stripped from
+the validated config.
+
+| Legacy | Canonical |
+|---|---|
+| `flash_attention: true` | `attn_implementation: flash_attention_2` |
+| `sdp_attention: true` | `attn_implementation: sdpa` |
+| `xformers_attention: true` | `attn_implementation: xformers` |
+| `flex_attention: true` | `attn_implementation: flex_attention` |
+| `sage_attention: true` | `attn_implementation: sage` |
+| `s2_attention: true` | `attn_implementation: s2` |
+| `eager_attention: true` | `attn_implementation: eager` |
+
+Combining `attn_implementation` with a legacy flag (e.g. `attn_implementation:
+flash_attention_2` **and** `flash_attention: true`) raises — pick one.
+
+::: {.callout-note}
+
+Existing example configs under `examples/` still use the legacy flags. They
+continue to work with a deprecation warning; they will be migrated in a
+follow-up pass.
 
 :::

docs/debugging.qmd

@@ -76,8 +76,10 @@ datasets:
 Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) of Axolotl, which ensures that changes you make to the code are reflected at runtime.  Run the following commands from the root of this project:
 
 ```bash
-pip3 install packaging
+export UV_TORCH_BACKEND=cu128  # or cu130
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+uv venv --no-project --relocatable
+source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 #### Remote Hosts
@@ -208,17 +210,18 @@ cd axolotl
 Next, run the desired docker image and mount the current directory. Below is a docker command you can run to do this:[^2]
 
 ```bash
-docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl:main-py3.10-cu118-2.0.1
+docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --mount type=bind,src="${PWD}",target=/workspace/axolotl -v ${HOME}/.cache/huggingface:/root/.cache/huggingface axolotlai/axolotl-uv:main-latest
 ```
 
 >[!Tip]
 > To understand which containers are available, see the [Docker section of the README](../README.md#docker) and the [DockerHub repo](https://hub.docker.com/r/axolotlai/axolotl/tags).  For details of how the Docker containers are built, see axolotl's [Docker CI builds](../.github/workflows/main.yml).
 
-You will now be in the container.  Next, perform an editable install of Axolotl:
+You will now be in the container.  Next, install Axolotl with dev dependencies:
 
 ```bash
-pip3 install packaging
+uv venv --no-project --relocatable
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 ### Attach To Container

docs/docker.qmd

@@ -6,23 +6,33 @@ format:
     toc-depth: 4
 ---
 
-This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
+This section describes the different Docker images that are released by AxolotlAI at
+[Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with PyTorch 2.7.1 and CUDA 12.8.
+### Switch to the `-uv` images
+
+Each image below ships a **uv variant** that uses [uv](https://docs.astral.sh/uv/) with a relocatable venv
+(`/workspace/axolotl-venv`) instead of Miniconda + pip. Append `-uv` to the image name
+(e.g. `axolotlai/axolotl-uv`, `axolotlai/axolotl-base-uv`, `axolotlai/axolotl-cloud-uv`). Tags follow the
+same format as their non-uv counterparts.
+
+**We recommend switching to the `-uv` images early.** In the near future we will publish the uv-based
+build to the non-uv tags as well. The non-uv names will continue to work, but they will start serving
+the uv image.
 :::
 
 ## Base
 
-The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
+The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image.
+It includes python, torch, git, git-lfs, awscli, pydantic, and more.
 
 #### Image
 
-```
+| Variant | Image | Docker Hub |
-axolotlai/axolotl-base
+|---------|-------|------------|
-```
+| pip | `axolotlai/axolotl-base` | [Link](https://hub.docker.com/r/axolotlai/axolotl-base) |
-
+| uv | `axolotlai/axolotl-base-uv` | [Link](https://hub.docker.com/r/axolotlai/axolotl-base-uv) |
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-base)
 
 #### Tags format
 
@@ -32,8 +42,10 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
 
 Tags examples:
 
-- `main-base-py3.11-cu128-2.8.0`
 - `main-base-py3.11-cu128-2.9.1`
+- `main-base-py3.12-cu128-2.10.0`
+- `main-base-py3.12-cu130-2.9.1`
+- `main-base-py3.12-cu130-2.10.0`
 
 ## Main
 
@@ -41,11 +53,10 @@ The main image is the image that is used to run Axolotl. It is based on the `axo
 
 #### Image
 
-```
+| Variant | Image | Docker Hub |
-axolotlai/axolotl
+|---------|-------|------------|
-```
+| pip | `axolotlai/axolotl` | [Link](https://hub.docker.com/r/axolotlai/axolotl) |
-
+| uv | `axolotlai/axolotl-uv` | [Link](https://hub.docker.com/r/axolotlai/axolotl-uv) |
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
 
 #### Tags format {#sec-main-tags}
 
@@ -53,7 +64,7 @@ Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
 # on push to main
 main-py{python_version}-cu{cuda_version}-{pytorch_version}
 
-# latest main (currently torch 2.6.0, python 3.11, cuda 12.4)
+# latest main (currently torch 2.9.1, python 3.11, cuda 12.8)
 main-latest
 
 # nightly build
@@ -71,12 +82,13 @@ There may be some extra tags appended to the image, like `-vllm` which installs
 
 Tags examples:
 
-- `main-py3.11-cu128-2.8.0`
 - `main-py3.11-cu128-2.9.1`
+- `main-py3.12-cu128-2.10.0`
+- `main-py3.12-cu130-2.9.1`
+- `main-py3.12-cu130-2.10.0`
 - `main-latest`
-- `main-20250303-py3.11-cu124-2.6.0`
+- `main-20260315-py3.11-cu128-2.9.1`
-- `main-20250303-py3.11-cu126-2.6.0`
+- `0.16.1`
-- `0.12.0`
 
 ## Cloud
 
@@ -90,11 +102,10 @@ Jupyter lab is run by default. Set `JUPYTER_DISABLE=1` in the environment variab
 
 #### Image
 
-```
+| Variant | Image | Docker Hub |
-axolotlai/axolotl-cloud
+|---------|-------|------------|
-```
+| pip | `axolotlai/axolotl-cloud` | [Link](https://hub.docker.com/r/axolotlai/axolotl-cloud) |
-
+| uv | `axolotlai/axolotl-cloud-uv` | [Link](https://hub.docker.com/r/axolotlai/axolotl-cloud-uv) |
-Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud)
 
 #### Tags format
 

docs/ebft.qmd

@@ -129,7 +129,7 @@ gradient_accumulation_steps: 4
 max_steps: 20
 learning_rate: 5.0e-6
 bf16: auto
-flash_attention: true
+attn_implementation: flash_attention_2
 gradient_checkpointing: true
 output_dir: ./outputs/ebft-quickstart
 ```
@@ -304,7 +304,7 @@ lora_alpha: 32
 lora_target_linear: true
 
 bf16: auto
-flex_attention: true
+attn_implementation: flex_attention
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true          # Required with flex_attention

docs/faq.qmd

@@ -57,7 +57,7 @@ description: Frequently asked questions
 
 **Q: vLLM is not working with Axolotl**
 
-> A: We currently recommend torch 2.6.0 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.11-cu124-2.6.0` tag.
+> A: We currently recommend torch 2.10 for use with `vllm`. Please ensure you use the right version. For Docker, please use the `main-py3.12-cu128-2.10.0` tag (note: torch 2.10 images are built with Python 3.12).
 
 **Q: FA2 2.8.0 `undefined symbol` runtime error on CUDA 12.4**
 

docs/grpo.qmd

@@ -154,7 +154,7 @@ lr_scheduler: cosine
 warmup_steps: 10
 
 bf16: true
-flash_attention: true
+attn_implementation: flash_attention_2
 gradient_checkpointing: true
 
 special_tokens:

docs/installation.qmd

@@ -15,64 +15,30 @@ This guide covers all the ways you can install and set up Axolotl for your envir
 
 - NVIDIA GPU (Ampere architecture or newer for `bf16` and Flash Attention) or AMD GPU
 - Python ≥3.11
-- PyTorch ≥2.6.0
+- PyTorch ≥2.9.1
 
-## Installation Methods {#sec-installation-methods}
+## Installation {#sec-installation}
-
-::: {.callout-important}
-Please make sure to have Pytorch installed before installing Axolotl in your local environment.
-
-Follow the instructions at: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
-:::
 
 ::: {.callout-important}
 For Blackwell GPUs, please use Pytorch 2.9.1 and CUDA 12.8.
 :::
 
-### PyPI Installation (Recommended) {#sec-pypi}
+### Quick Install {#sec-uv}
 
-```{.bash}
+Axolotl uses [uv](https://docs.astral.sh/uv/) as its package manager. uv is a fast, reliable Python package installer and resolver built in Rust.
-pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
-```
 
-We use `--no-build-isolation` in order to detect the installed PyTorch version (if
+Install uv if not already installed:
-installed) in order not to clobber it, and so that we set the correct version of
-dependencies that are specific to the PyTorch version or other installed
-co-dependencies.
-
-### uv Installation {#sec-uv}
-
-uv is a fast, reliable Python package installer and resolver built in Rust. It offers significant performance improvements over pip and provides better dependency resolution, making it an excellent choice for complex environments.
-
-Install uv if not already installed
 ```{.bash}
 curl -LsSf https://astral.sh/uv/install.sh | sh
 source $HOME/.local/bin/env
 ```
 
-Choose your CUDA version to use with PyTorch; e.g. `cu124`, `cu126`, `cu128`,
+Choose your CUDA version (e.g. `cu128`, `cu130`), create a venv, and install:
-then create the venv and activate
 ```{.bash}
-export UV_TORCH_BACKEND=cu126
+export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv --no-project --relocatable
+uv venv
 source .venv/bin/activate
-```
+uv pip install --no-build-isolation axolotl[deepspeed]
-
-Install PyTorch
-- PyTorch 2.6.0 recommended
-```{.bash}
-uv pip install packaging setuptools wheel
-uv pip install torch==2.6.0
-uv pip install awscli pydantic
-```
-
-Install axolotl from PyPi
-```{.bash}
-uv pip install --no-build-isolation axolotl[deepspeed,flash-attn]
-
-# optionally install with vLLM if you're using torch==2.6.0 and want to train w/ GRPO
-uv pip install --no-build-isolation axolotl[deepspeed,flash-attn,vllm]
 ```
 
 ### Edge/Development Build {#sec-edge-build}
@@ -82,14 +48,16 @@ For the latest features between releases:
 ```{.bash}
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
-pip3 install -U packaging setuptools wheel ninja
+export UV_TORCH_BACKEND=cu128  # or cu130
-pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+uv venv
+source .venv/bin/activate
+uv pip install --no-build-isolation -e '.[deepspeed]'
 ```
 
 ### Docker {#sec-docker}
 
 ```{.bash}
-docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
+docker run --gpus '"all"' --rm -it --ipc=host axolotlai/axolotl-uv:main-latest
 ```
 
 For development with Docker:
@@ -106,12 +74,12 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
   --ulimit memlock=-1 --ulimit stack=67108864 \
   --mount type=bind,src="${PWD}",target=/workspace/axolotl \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
-  axolotlai/axolotl:main-latest
+  axolotlai/axolotl-uv:main-latest
 ```
 :::
 
 ::: {.callout-important}
-For Blackwell GPUs, please use `axolotlai/axolotl:main-py3.11-cu128-2.9.1` or the cloud variant `axolotlai/axolotl-cloud:main-py3.11-cu128-2.9.1`.
+For Blackwell GPUs, please use `axolotlai/axolotl-uv:main-py3.11-cu128-2.9.1` or the cloud variant `axolotlai/axolotl-cloud-uv:main-py3.11-cu128-2.9.1`.
 :::
 
 Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.
@@ -122,7 +90,7 @@ Please refer to the [Docker documentation](docker.qmd) for more information on t
 
 For providers supporting Docker:
 
-- Use `axolotlai/axolotl-cloud:main-latest`
+- Use `axolotlai/axolotl-cloud-uv:main-latest`
 - Available on:
     - [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)
     - [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=axolotl&utm_medium=partner&utm_campaign=template_launch_july2025&utm_content=docs_link)
@@ -141,7 +109,7 @@ For providers supporting Docker:
 ### macOS {#sec-macos}
 
 ```{.bash}
-pip3 install --no-build-isolation -e '.'
+uv pip install --no-build-isolation -e '.'
 ```
 
 See @sec-troubleshooting for Mac-specific issues.
@@ -152,21 +120,44 @@ See @sec-troubleshooting for Mac-specific issues.
 We recommend using WSL2 (Windows Subsystem for Linux) or Docker.
 :::
 
-## Environment Managers {#sec-env-managers}
+## Migrating from pip to uv {#sec-migrating}
 
-### Conda/Pip venv {#sec-conda}
+If you have an existing pip-based Axolotl installation, you can migrate to uv:
 
-1. Install Python ≥3.11
+```{.bash}
-2. Install PyTorch: https://pytorch.org/get-started/locally/
+# Install uv
-3. Install Axolotl:
+curl -LsSf https://astral.sh/uv/install.sh | sh
-   ```{.bash}
+source $HOME/.local/bin/env
-   pip3 install -U packaging setuptools wheel ninja
+
-   pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
+# Create a fresh venv (recommended for a clean start)
-   ```
+export UV_TORCH_BACKEND=cu128  # or cu130
-4. (Optional) Login to Hugging Face:
+uv venv
-   ```{.bash}
+source .venv/bin/activate
-   hf auth login
+
-   ```
+# Reinstall axolotl
+uv pip install --no-build-isolation axolotl[deepspeed]
+```
+
+## Using pip (Alternative) {#sec-pip}
+
+If you are unable to install uv, you can still use pip directly.
+
+::: {.callout-important}
+Please make sure to have PyTorch installed before installing Axolotl with pip.
+
+Follow the instructions at: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
+:::
+
+```{.bash}
+pip3 install -U packaging setuptools wheel ninja
+pip3 install --no-build-isolation axolotl[deepspeed]
+```
+
+For editable/development installs:
+```{.bash}
+pip3 install -U packaging setuptools wheel ninja
+pip3 install --no-build-isolation -e '.[deepspeed]'
+```
 
 ## Troubleshooting {#sec-troubleshooting}
 

docs/multimodal_assistant_mask.md

@@ -0,0 +1,84 @@
+# Multimodal assistant-only loss masking
+
+## Correct placement
+
+```yaml
+# Top-level: only train_on_inputs lives here.
+train_on_inputs: false
+
+datasets:
+  - path: data/train.jsonl
+    type: chat_template
+    roles_to_train:          # per-dataset — this is what the MM scanner reads
+      - assistant
+    train_on_eos: turn       # per-dataset — same
+
+test_datasets:
+  - path: data/val.jsonl
+    type: chat_template
+    split: train
+    roles_to_train:
+      - assistant
+    train_on_eos: turn
+```
+
+## How to verify at runtime
+
+`build_collator` logs the resolved knobs at INFO:
+
+```text
+MM collator: train_on_inputs=False roles_to_train=['assistant'] train_on_eos=turn role_boundaries_override=none
+```
+
+If `roles_to_train` logs as `None`, the YAML knobs are not reaching the
+scanner — check that they are under `datasets[0]`, not at the root.
+
+Each verified strategy additionally logs its resolved boundary token ids at
+strategy init (e.g. `<|turn>model` → `[105, 4368]`, `<turn|>` → `[106]` for
+Gemma 4). If a strategy emits the "has no built-in role boundaries ... only
+pad and media tokens are masked" one-shot warning instead, it is on the
+fallback path — declare per-role markers in YAML via `cfg.role_boundaries`
+(below) to activate masking. The strategies currently on this path are
+listed in the audit table above under `fallback + warn`.
+
+## Config-based override: `cfg.role_boundaries`
+
+For the "unverified" strategies above, or for custom chat templates that
+don't match a built-in strategy's markers, users can declare role boundaries
+directly in YAML without subclassing:
+
+```yaml
+role_boundaries:
+  - role: assistant
+    start: "<|turn>model"
+    end: "<turn|>"
+  - role: user
+    start: "<|turn>user"
+    end: "<turn|>"
+  # Optional keys:
+  # include_start: false   # default False
+  # include_end: true      # default True, respects cfg.train_on_eos
+  # end: eos_token         # sentinel: resolves to tokenizer.eos_token_id
+  # end: null              # span runs to end of sequence
+```
+
+Semantics:
+
+- `start` and `end` are literal strings; axolotl encodes them at strategy
+  init via `tokenizer.encode(..., add_special_tokens=False)` and logs the
+  resolved token-id sequences at INFO level.
+- The special value `end: eos_token` is the portable way to express
+  "Pixtral-style assistant turns end at EOS" without hard-coding an id.
+- `role_boundaries` is an **opt-in override**. A non-empty list **replaces**
+  the strategy's built-in declarations wholesale (partial overlays are
+  intentionally unsupported — they're hard to reason about at review time).
+  Leaving the field unset *or* setting it to an empty list (`[]`) both mean
+  "use the strategy's built-ins." Writing `role_boundaries: []` is almost
+  always a typo or leftover — honoring it literally would produce all-masked
+  labels and zero gradient, so it is treated the same as unset.
+- `cfg.roles_to_train` still governs which declared roles contribute to
+  loss. You can declare `user` and `assistant` boundaries and set
+  `roles_to_train: ["assistant"]` to have the scanner correctly identify
+  user spans as masking boundaries without training on their content.
+- Invalid specs fail loudly at strategy init (missing `role`/`start`,
+  unencodable markers), not silently at loss-compute time.

docs/optimizations.qmd

@@ -22,12 +22,12 @@ Improves GPU utilization by combining multiple short sequences into a single pac
 
 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).
+- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `attn_implementation: flash_attention_2`. **(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`.
+- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `attn_implementation: flex_attention`.
-- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `sdp_attention: true`. PyTorch's native implementation.
+- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `attn_implementation: sdpa`. PyTorch's native implementation.
-- **[Xformers](https://github.com/facebookresearch/xformers)**: `xformers_attention: true`. Works with FP16.
+- **[Xformers](https://github.com/facebookresearch/xformers)**: `attn_implementation: xformers`. Works with FP16.
 
-*Note: You should only enable one attention backend.*
+See [Attention](attention.qmd) for the full list of backends and the canonical values.
 
 ### LoRA Optimizations
 

docs/rlhf.qmd

@@ -320,8 +320,10 @@ The input format is a simple JSON input with customizable fields based on the ab
 As IPO is just DPO with a different loss function, all supported dataset formats for [DPO](#dpo) are also supported for IPO.
 
 ```yaml
-rl: ipo
+rl: dpo
+dpo_loss_type: ["ipo"]
 ```
+*Note:* Passing `rl: ipo` directly is still supported, but will soon be deprecated.
 
 ### ORPO
 
@@ -1145,8 +1147,7 @@ datasets:
     type: ebft_strided_structured.transform
     split: train[:1%]
 
-flash_attention: false
+attn_implementation: flex_attention   # Strided mode uses flex_attention
-flex_attention: true     # Strided mode uses flex_attention
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: true    # Required for flex_attention

docs/scripts/examples-allowlist.yml

@@ -20,6 +20,8 @@ examples:
     title: Arcee AFM
 
   # MistralAI
+  - name: mistral-medium-3_5
+    title: Mistral Medium 3.5
   - name: ministral3/think
     title: Ministral 3 Thinking
   - name: ministral3/vision

docs/sequence_parallelism.qmd

@@ -55,7 +55,7 @@ To use sequence parallelism, you need:
 
 ## Limitations
 
-- Flash attention must be enabled for this to work (`flash_attention: true` in config YAML)
+- Flash attention must be enabled for this to work (`attn_implementation: flash_attention_2` in config YAML)
 - May have a small performance overhead due to communication between GPUs
 
 ## Example

docs/training_stability.qmd

@@ -245,7 +245,7 @@ For GRPO, also reduce `max_completion_length`. Memory scales quadratically with
 Reduces attention memory from O(n^2) to O(n):
 
 ```yaml
-flash_attention: true
+attn_implementation: flash_attention_2
 ```
 
 ### Step 6: Offload with DeepSpeed

docs/unsloth.qmd

@@ -1,53 +0,0 @@
----
-title: "Unsloth"
-description: "Hyper-optimized QLoRA finetuning for single GPUs"
----
-
-### Overview
-
-Unsloth provides hand-written optimized kernels for LLM finetuning that slightly improve speed and VRAM over
-standard industry baselines.
-
-::: {.callout-important}
-Due to breaking changes in transformers `v4.48.0`, users will need to downgrade to `<=v4.47.1` to use this patch.
-
-This will later be deprecated in favor of [LoRA Optimizations](lora_optims.qmd).
-:::
-
-
-### Installation
-
-The following will install the correct unsloth and extras from source.
-
-```bash
-python scripts/unsloth_install.py | sh
-```
-
-### Usage
-
-Axolotl exposes a few configuration options to try out unsloth and get most of the performance gains.
-
-Our unsloth integration is currently limited to the following model architectures:
- - llama
-
-These options are specific to LoRA finetuning and cannot be used for multi-GPU finetuning
-```yaml
-unsloth_lora_mlp: true
-unsloth_lora_qkv: true
-unsloth_lora_o: true
-```
-
-These options are composable and can be used with multi-gpu finetuning
-```yaml
-unsloth_cross_entropy_loss: true
-unsloth_rms_norm: true
-unsloth_rope: true
-```
-
-### Limitations
-
-- Single GPU only; e.g. no multi-gpu support
-- No deepspeed or FSDP support (requires multi-gpu)
-- LoRA + QLoRA support only. No full fine tunes or fp8 support.
-- Limited model architecture support. Llama, Phi, Gemma, Mistral only
-- No MoE support.

examples/LiquidAI/README.md

@@ -15,8 +15,7 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
     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
+    uv pip install --no-build-isolation 'axolotl>=0.16.1'
-    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
     ```
 
 2.  Run one of the finetuning examples below.
@@ -35,7 +34,7 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
 
     **LFM2-MoE**
     ```bash
-    pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
+    uv pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
 
     # LoRA SFT (1x48GB @ 16.2GiB)
     axolotl train examples/LiquidAI/lfm2-8b-a1b-lora.yaml
@@ -45,7 +44,7 @@ Thanks to the team at LiquidAI for giving us early access to prepare for these r
 
 - **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:
   ```bash
-  pip uninstall -y causal-conv1d
+  uv pip uninstall causal-conv1d
   ```
 
 - **Dataset Loading**: Read more on how to load your own dataset in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).

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

@@ -39,7 +39,7 @@ tf32: true
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

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

@@ -48,7 +48,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -50,8 +50,7 @@ tf32: true
 
 gradient_checkpointing: true
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
-eager_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/alst/llama3-8b-deepspeed-alst.yaml

@@ -39,7 +39,7 @@ activation_offloading: legacy
 
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_steps: 100
 saves_per_epoch: 1

examples/alst/llama3-8b-fsdp2-alst.yaml

@@ -39,7 +39,7 @@ activation_offloading: legacy
 
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_steps: 100
 saves_per_epoch: 1

examples/apertus/README.md

@@ -11,12 +11,11 @@ This guide shows how to fine-tune it with Axolotl with multi-turn conversations
     Here is an example of how to install from main for pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+uv pip install --no-build-isolation -e '.'
-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
@@ -31,7 +30,7 @@ python scripts/cutcrossentropy_install.py | sh
 # For those using our Docker image, use the below path.
 export CUDA_HOME=/usr/local/cuda
 
-pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+uv pip install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
 ```
 
 For any installation errors, see [XIELU Installation Issues](#xielu-installation-issues)
@@ -67,7 +66,7 @@ If those didn't help, please try the below solutions:
 1. Pass env for CMAKE and try install again:
 
     ```bash
-    Python_EXECUTABLE=$(which python) pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+    Python_EXECUTABLE=$(which python) uv pip install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
     ```
 
 2. Git clone the repo and manually hardcode python path:
@@ -92,7 +91,7 @@ If those didn't help, please try the below solutions:
     ```
 
     ```bash
-    pip3 install . --no-build-isolation --no-deps
+    uv pip install . --no-build-isolation --no-deps
     ```
 
 ## Optimization Guides

examples/apertus/apertus-8b-qlora.yaml

@@ -55,7 +55,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/arcee/README.md

@@ -13,12 +13,11 @@ Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the A
     Here is an example of how to install from main for pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+uv pip install --no-build-isolation -e '.'
-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

examples/arcee/afm-4.5b-qlora.yaml

@@ -55,7 +55,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/archived/cerebras/btlm-ft.yml

@@ -59,8 +59,7 @@ gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
 
-flash_attention: true
+attn_implementation: flash_attention_2
-sdp_attention:
 flash_optimum:
 
 gptq_groupsize:

examples/archived/cerebras/qlora.yml

@@ -39,8 +39,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/code-llama/13b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/13b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/34b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/34b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/7b/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/code-llama/7b/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/dbrx/16bit-lora.yaml

@@ -52,7 +52,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/dbrx/8bit-lora.yaml

@@ -55,7 +55,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/dbrx/fft-ds-zero3.yaml

@@ -39,7 +39,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/deepcoder/deepcoder-14B-preview-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/archived/falcon/config-7b-lora.yml

@@ -43,8 +43,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/falcon/config-7b-qlora.yml

@@ -73,8 +73,7 @@ early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/falcon/config-7b.yml

@@ -40,8 +40,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/gemma/qlora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/gptj/qlora.yml

@@ -36,8 +36,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/jeopardy-bot/config.yml

@@ -37,8 +37,7 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/mpt-7b/config.yml

@@ -39,7 +39,6 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/config.yml

@@ -39,7 +39,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/lora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/openllama-3b/qlora.yml

@@ -40,7 +40,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/qwen/lora.yml

@@ -47,7 +47,6 @@ tf32: false
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qlora.yml

@@ -47,7 +47,6 @@ tf32: false
 gradient_checkpointing: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention:
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qwen2-moe-lora.yaml

@@ -43,7 +43,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/qwen/qwen2-moe-qlora.yaml

@@ -46,7 +46,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/redpajama/config-3b.yml

@@ -40,7 +40,6 @@ bf16: auto
 tf32: true
 resume_from_checkpoint:
 logging_steps: 5
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/replit-3b/config-lora.yml

@@ -38,7 +38,6 @@ tf32: true
 gradient_checkpointing:
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/stablelm-2/1.6b/fft.yml

@@ -44,7 +44,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 flash_attn_fuse_mlp: true

examples/archived/stablelm-2/1.6b/lora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 

examples/archived/starcoder2/qlora.yml

@@ -46,7 +46,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/tiny-llama/lora-mps.yml

@@ -47,7 +47,6 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: false
 
 warmup_ratio: 0.1
 evals_per_epoch: 0

examples/archived/tiny-llama/lora.yml

@@ -45,7 +45,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/tiny-llama/pretrain.yml

@@ -36,7 +36,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/archived/tiny-llama/qlora.yml

@@ -47,7 +47,7 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/xgen-7b/xgen-7b-8k-qlora.yml

@@ -71,8 +71,7 @@ early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
 logging_steps: 1
-xformers_attention: true
+attn_implementation: xformers
-flash_attention:
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/yi-34B-chat/qlora.yml

@@ -10,7 +10,7 @@ load_in_4bit: true
 sequence_len: 1024
 bf16: auto
 tf32: false
-flash_attention: true
+attn_implementation: flash_attention_2
 special_tokens:
   bos_token: "<|startoftext|>"
   eos_token: "<|endoftext|>"

examples/cohere/command-r-7b-qlora.yml

@@ -48,7 +48,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch:

examples/colab-notebooks/colab-axolotl-example.ipynb

@@ -36,12 +36,7 @@
     "id": "msOCO4NRmRLa"
    },
    "outputs": [],
-   "source": [
+   "source": "%%capture\n# This step can take ~5-10 minutes to install dependencies\n!pip install --no-build-isolation \"axolotl>=0.16.1\"\n!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fec1a88\""
-    "%%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@fec1a88\""
-   ]
   },
   {
    "cell_type": "markdown",

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -45,7 +45,7 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -35,7 +35,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -59,7 +59,7 @@ gradient_checkpointing_kwargs:
   use_reentrant: false
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/devstral/README.md

@@ -15,9 +15,8 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
     Here is an example of how to install from pip:
 
 ```bash
-# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+# Ensure you have Pytorch installed (Pytorch 2.9.1 min)
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
+uv pip install --no-build-isolation 'axolotl>=0.16.1'
-pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage

examples/devstral/devstral-small-qlora.yml

@@ -26,7 +26,6 @@ lora_model_dir:
 sequence_len: 2048
 sample_packing: true
 
-
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0
@@ -51,8 +50,8 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-flash_attention: true
+attn_implementation: flash_attention_2
-scaling_softmax: true
+# scaling_softmax: true  # needs flex_attention
 
 loss_watchdog_threshold: 5.0
 loss_watchdog_patience: 3

examples/distributed-parallel/llama-3_1-8b-hsdp-tp.yaml

@@ -29,7 +29,7 @@ output_dir: ./outputs/ndp-out/
 
 sequence_len: 2048
 sample_packing: true
-flash_attention: true
+attn_implementation: flash_attention_2
 
 gradient_accumulation_steps: 1
 micro_batch_size: 1

examples/distributed-parallel/qwen3-8b-fsdp-tp-cp.yaml

@@ -26,7 +26,7 @@ output_dir: ./outputs/ndp-out/
 
 sequence_len: 8192
 sample_packing: true
-flash_attention: true
+attn_implementation: flash_attention_2
 
 gradient_accumulation_steps: 1
 micro_batch_size: 1  # must be 1 when using context parallel