properly handles kernels repo type

.github/CONTRIBUTING.md

@@ -31,11 +31,10 @@ PRs are **greatly welcome**!
 
 Please run below to setup env
 ```bash
-# Install axolotl + dev and test dependencies
+# Install axolotl + dev and test dependencies from lockfile
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv --no-project --relocatable
+uv sync --extra flash-attn --extra deepspeed --group dev --group test
 source .venv/bin/activate
-uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 pre-commit install
 
 # test

.github/workflows/base.yml

@@ -30,6 +30,14 @@ 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: ""
@@ -160,6 +168,14 @@ 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/main.yml

@@ -18,6 +18,12 @@ 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"
@@ -174,6 +180,12 @@ 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/tests.yml

@@ -72,7 +72,7 @@ jobs:
         exclude:
           - python_version: "3.14"
             pytorch_version: "2.9.1"
-    timeout-minutes: 25
+    timeout-minutes: 20
 
     steps:
       - name: cleanup node

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: dpo, dpo_loss_type: ["ipo"]` | Paired preference data (chosen vs rejected) |
+| DPO/IPO | `rl: dpo` / `rl: 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) |

README.md

@@ -29,9 +29,6 @@
 
 ## 🎉 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).

SETUP_MIAAI.md

@@ -1,273 +0,0 @@
-# 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 @@
-0.16.2.dev0
+0.16.0.dev0

_quarto.yml

@@ -311,7 +311,6 @@ website:
             - docs/dataset_loading.qmd
             - docs/qat.qmd
             - docs/quantize.qmd
-            - docs/1_58bit_finetuning.qmd
             - docs/optimizations.qmd
 
         - section: "Core Concepts"

docker/Dockerfile

@@ -24,9 +24,9 @@ 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 [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="optimizers,ray"; \
+        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

docker/Dockerfile-base

@@ -58,3 +58,19 @@ 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

@@ -1,15 +1,16 @@
-ARG CUDA_VERSION="12.8.2"
+ARG CUDA_VERSION="12.8.1"
+ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
 
-FROM nvidia/cuda:12.8.2-devel-ubuntu22.04 AS base-builder
+FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
 
-ENV PATH="/root/miniforge3/bin:${PATH}"
+ENV PATH="/root/miniconda3/bin:${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 12.0+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
@@ -17,13 +18,13 @@ ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 RUN apt-get update \
     && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
     && wget \
-    https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh \
+    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \
-    && bash Miniforge3-Linux-x86_64.sh -b \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b \
-    && rm -f Miniforge3-Linux-x86_64.sh \
+    && rm -f Miniconda3-latest-Linux-x86_64.sh \
-    && /root/miniforge3/bin/conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
+    && conda create -n "py${PYTHON_VERSION}" python="${PYTHON_VERSION}"
 
-ENV PATH="/root/miniforge3/envs/py${PYTHON_VERSION}/bin:${PATH}"
+ENV PATH="/root/miniconda3/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 [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docker/Dockerfile-uv

@@ -24,9 +24,9 @@ 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 [ "$TARGETARCH" = "arm64" ]; then \
-        BASE_EXTRAS="optimizers,ray"; \
+        BASE_EXTRAS="flash-attn,ring-flash-attn,optimizers,ray"; \
     else \
-        BASE_EXTRAS="deepspeed,optimizers,ray"; \
+        BASE_EXTRAS="deepspeed,flash-attn,ring-flash-attn,optimizers,ray"; \
     fi && \
     if [ "$AXOLOTL_EXTRAS" != "" ]; then \
         uv pip install --no-build-isolation -e .[$BASE_EXTRAS,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \

docker/Dockerfile-uv-base

@@ -38,3 +38,20 @@ 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

@@ -1,70 +0,0 @@
----
-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 | `attn_implementation: flash_attention_2` | 256 | ✅ | Fastest when supported |
+| FA2 | `flash_attention: true` | 256 | ✅ | Fastest when supported |
-| FA4 | auto with `attn_implementation: flash_attention_2` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
+| FA4 | auto with `flash_attention: true` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
-| SDPA | `attn_implementation: sdpa` | None | ✅ | Universal fallback |
+| SDPA | `sdp_attention: true` | None | ✅ | Universal fallback |
-| flex | `attn_implementation: flex_attention` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
+| flex | `flex_attention: true` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
-| eager | `attn_implementation: eager` | None | ✅ | Slowest, always works |
+| eager | neither set | 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: dpo, dpo_loss_type: ["ipo"]`
+   - Overfitting → `rl: 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 `attn_implementation: flash_attention_2` or disable `sample_packing` |
+| `sample_packing` + SDPA + bf16 = 0.0 loss | Use `flash_attention: true` 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

@@ -3,71 +3,28 @@ title: Attention
 description: Supported attention modules in Axolotl
 ---
 
-Axolotl routes attention via a single config field:
+## SDP Attention
+
+This is the default built-in attention in PyTorch.
 
 ```yaml
-attn_implementation: <backend>
+sdp_attention: true
 ```
 
-`attn_implementation` is passed through to `transformers` verbatim (via
+For more details: [PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
-`model.config._attn_implementation`). Accepted values are the HF-native
-backends, axolotl-registered backends, or a hub-kernel path.
 
-## Backends
+## Flash Attention
 
-| `attn_implementation` | Description |
+Axolotl supports Flash Attention 2, 3, and 4. The best available version is used automatically
-|---|---|
+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
-attn_implementation: sdpa
+flash_attention: true
 ```
 
-### Flash Attention
+For more details: [Flash Attention](https://github.com/Dao-AILab/flash-attention/)
 
-Axolotl supports FA2, FA3, and FA4. The best available version is used
+### Flash Attention 2
-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)
 
@@ -82,25 +39,23 @@ 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. Auto-applied when `attn_uses_flash_lib`
+Requirements: Hopper or Blackwell GPUs
-is true and FA4 is importable.
-
-FA4 is still a pre-release on PyPI, so `--pre` is required:
 
 ```bash
-pip install --pre flash-attn-4
+pip install flash-attn-4
 ```
 
 Or from source:
@@ -108,6 +63,7 @@ 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.
@@ -130,113 +86,93 @@ 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. See
+Requirements: ROCm 6.0 and above.
-[Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
 
-### Flex Attention
+See [Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).
+
+## Flex Attention
+
+A flexible PyTorch API for attention used in combination with `torch.compile`.
 
 ```yaml
-attn_implementation: flex_attention
+flex_attention: true
-torch_compile: true  # recommended
+
+# recommended
+torch_compile: true
 ```
 
-Requires torch ≥ 2.6. See [PyTorch docs](https://pytorch.org/blog/flexattention/).
+::: {.callout-note}
 
-### SageAttention
+We recommend using latest stable version of PyTorch for best performance.
 
-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
-attn_implementation: sage
+sage_attention: true
 ```
 
+Requirements: Ampere, Ada, or Hopper GPUs
+
 ```bash
 pip install sageattention==2.2.0 --no-build-isolation
 ```
 
 ::: {.callout-warning}
 
-Only LoRA/QLoRA recommended. Full finetuning has been observed to drop loss to 0. See
+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).
-[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)
 
-### xFormers
+::: {.callout-note}
+
+We do not support SageAttention 3 at the moment. If you are interested on adding this or improving SageAttention implementation, please make an Issue.
+
+:::
+
+
+## xFormers
 
 ```yaml
-attn_implementation: xformers
+xformers_attention: true
 ```
 
 ::: {.callout-tip}
 
-Recommended for Turing GPUs or below (e.g. Colab T4).
+We recommend using with Turing GPUs or below (such as on Colab).
 
 :::
 
-### Shifted Sparse Attention
+For more details: [xFormers](https://github.com/facebookresearch/xformers)
+
+## Shifted Sparse Attention
 
 ::: {.callout-warning}
 
-Planned for deprecation. Prefer one of the backends above.
+We plan to deprecate this! If you use this feature, we recommend switching to methods above.
 
 :::
 
-Requirements: LLaMA model architecture. Loaded as FA2 under the hood and
+Requirements: LLaMA model architecture
-patched to implement shifted-sparse attention. Does not support sample packing.
 
 ```yaml
-attn_implementation: s2
+flash_attention: true
+s2_attention: true
 ```
 
-### FP8
+::: {.callout-tip}
 
-torchao low-precision attention. Loaded as SDPA and patched post-load.
+No sample packing support!
-
-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

@@ -77,9 +77,8 @@ Make sure you have an [editable install](https://setuptools.pypa.io/en/latest/us
 
 ```bash
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv --no-project --relocatable
+uv sync --extra flash-attn --extra deepspeed --group dev --group test
 source .venv/bin/activate
-uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 #### Remote Hosts
@@ -219,9 +218,8 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it --name axolotl --
 You will now be in the container.  Next, install Axolotl with dev dependencies:
 
 ```bash
-uv venv --no-project --relocatable
+uv sync --extra flash-attn --extra deepspeed --group dev --group test
 source .venv/bin/activate
-uv pip install --no-build-isolation -e '.[deepspeed]' --group dev --group test
 ```
 
 ### Attach To Container

docs/docker.qmd

@@ -10,16 +10,13 @@ This section describes the different Docker images that are released by AxolotlA
 [Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-### Switch to the `-uv` images
+For Blackwell GPUs, please use the tags with PyTorch 2.9.1 and CUDA 12.8.
+:::
 
-Each image below ships a **uv variant** that uses [uv](https://docs.astral.sh/uv/) with a relocatable venv
+::: {.callout-tip}
-(`/workspace/axolotl-venv`) instead of Miniconda + pip. Append `-uv` to the image name
+Each image below is available in a **uv variant** that uses [uv](https://docs.astral.sh/uv/) with
-(e.g. `axolotlai/axolotl-uv`, `axolotlai/axolotl-base-uv`, `axolotlai/axolotl-cloud-uv`). Tags follow the
+a relocatable venv (`/workspace/axolotl-venv`) instead of Miniconda + pip. Append `-uv` to the image name
-same format as their non-uv counterparts.
+(e.g. `axolotlai/axolotl-base-uv`). Tags follow the same format. We recommend the uv images for new deployments.
-
-**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
@@ -88,7 +85,7 @@ Tags examples:
 - `main-py3.12-cu130-2.10.0`
 - `main-latest`
 - `main-20260315-py3.11-cu128-2.9.1`
-- `0.16.1`
+- `0.12.0`
 
 ## Cloud
 

docs/ebft.qmd

@@ -129,7 +129,7 @@ gradient_accumulation_steps: 4
 max_steps: 20
 learning_rate: 5.0e-6
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 output_dir: ./outputs/ebft-quickstart
 ```
@@ -304,7 +304,7 @@ lora_alpha: 32
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flex_attention
+flex_attention: true
 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 2.10 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).
+> 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.
 
 **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
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

docs/installation.qmd

@@ -15,7 +15,7 @@ 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 ≥2.9.1
+- PyTorch ≥2.9.0
 
 ## Installation {#sec-installation}
 
@@ -36,9 +36,9 @@ source $HOME/.local/bin/env
 Choose your CUDA version (e.g. `cu128`, `cu130`), create a venv, and install:
 ```{.bash}
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv
+uv venv --no-project --relocatable
 source .venv/bin/activate
-uv pip install --no-build-isolation axolotl[deepspeed]
+uv pip install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
 ### Edge/Development Build {#sec-edge-build}
@@ -49,11 +49,12 @@ For the latest features between releases:
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv
+uv sync --extra flash-attn --extra deepspeed
 source .venv/bin/activate
-uv pip install --no-build-isolation -e '.[deepspeed]'
 ```
 
+`uv sync` creates a `.venv`, installs exact pinned versions from `uv.lock`, and sets up an editable install automatically.
+
 ### Docker {#sec-docker}
 
 ```{.bash}
@@ -131,11 +132,11 @@ source $HOME/.local/bin/env
 
 # Create a fresh venv (recommended for a clean start)
 export UV_TORCH_BACKEND=cu128  # or cu130
-uv venv
+uv venv --no-project --relocatable
 source .venv/bin/activate
 
 # Reinstall axolotl
-uv pip install --no-build-isolation axolotl[deepspeed]
+uv pip install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
 ## Using pip (Alternative) {#sec-pip}
@@ -150,13 +151,13 @@ Follow the instructions at: [https://pytorch.org/get-started/locally/](https://p
 
 ```{.bash}
 pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation axolotl[deepspeed]
+pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
 ```
 
 For editable/development installs:
 ```{.bash}
 pip3 install -U packaging setuptools wheel ninja
-pip3 install --no-build-isolation -e '.[deepspeed]'
+pip3 install --no-build-isolation -e '.[flash-attn,deepspeed]'
 ```
 
 ## Troubleshooting {#sec-troubleshooting}

docs/multimodal_assistant_mask.md

@@ -1,84 +0,0 @@
-# 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)**: `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).
+- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `flash_attention: true`. **(Recommended)** The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check [AMD Support](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support).
-- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `attn_implementation: flex_attention`.
+- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `flex_attention: true`.
-- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `attn_implementation: sdpa`. PyTorch's native implementation.
+- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `sdp_attention: true`. PyTorch's native implementation.
-- **[Xformers](https://github.com/facebookresearch/xformers)**: `attn_implementation: xformers`. Works with FP16.
+- **[Xformers](https://github.com/facebookresearch/xformers)**: `xformers_attention: true`. Works with FP16.
 
-See [Attention](attention.qmd) for the full list of backends and the canonical values.
+*Note: You should only enable one attention backend.*
 
 ### LoRA Optimizations
 

docs/rlhf.qmd

@@ -320,10 +320,8 @@ 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: dpo
+rl: ipo
-dpo_loss_type: ["ipo"]
 ```
-*Note:* Passing `rl: ipo` directly is still supported, but will soon be deprecated.
 
 ### ORPO
 
@@ -1147,7 +1145,8 @@ datasets:
     type: ebft_strided_structured.transform
     split: train[:1%]
 
-attn_implementation: flex_attention   # Strided mode uses flex_attention
+flash_attention: false
+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

@@ -20,8 +20,6 @@ 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 (`attn_implementation: flash_attention_2` in config YAML)
+- Flash attention must be enabled for this to work (`flash_attention: true` 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
-attn_implementation: flash_attention_2
+flash_attention: true
 ```
 
 ### Step 6: Offload with DeepSpeed

examples/LiquidAI/README.md

@@ -15,7 +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
-    uv pip install --no-build-isolation 'axolotl>=0.16.1'
+    uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
     ```
 
 2.  Run one of the finetuning examples below.

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

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

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -50,7 +50,8 @@ tf32: true
 
 gradient_checkpointing: true
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
+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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_steps: 100
 saves_per_epoch: 1

examples/apertus/README.md

@@ -11,11 +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 2.9.1 min)
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-uv pip install --no-build-isolation -e '.'
+uv pip 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/apertus/apertus-8b-qlora.yaml

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

examples/arcee/README.md

@@ -13,11 +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 2.9.1 min)
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
 git clone https://github.com/axolotl-ai-cloud/axolotl.git
 cd axolotl
 
-uv pip install --no-build-isolation -e '.'
+uv pip 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/archived/cerebras/btlm-ft.yml

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

examples/archived/cerebras/qlora.yml

@@ -39,7 +39,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

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

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

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

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

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

@@ -40,7 +40,8 @@ tf32: true
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

examples/archived/gptj/qlora.yml

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

examples/archived/jeopardy-bot/config.yml

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

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

@@ -39,6 +39,7 @@ 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
-attn_implementation: flash_attention_2
+flash_attention: true
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 gptq_groupsize:
 gptq_model_v1:
 warmup_ratio: 0.1

examples/archived/qwen/lora.yml

@@ -47,6 +47,7 @@ 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

@@ -47,6 +47,7 @@ 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

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

@@ -40,6 +40,7 @@ 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

@@ -38,6 +38,7 @@ 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
-attn_implementation: flash_attention_2
+flash_attention: true
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

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

@@ -47,6 +47,7 @@ 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 4

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

@@ -71,7 +71,8 @@ early_stopping_patience: 3
 resume_from_checkpoint:
 auto_resume_from_checkpoints: true
 logging_steps: 1
-attn_implementation: xformers
+xformers_attention: true
+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
-attn_implementation: flash_attention_2
+flash_attention: true
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch:

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

@@ -36,7 +36,12 @@
     "id": "msOCO4NRmRLa"
    },
    "outputs": [],
-   "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\""
+   "source": [
+    "%%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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 2

examples/devstral/README.md

@@ -15,8 +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 2.9.1 min)
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-uv pip install --no-build-isolation 'axolotl>=0.16.1'
+uv pip 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

@@ -26,6 +26,7 @@ lora_model_dir:
 sequence_len: 2048
 sample_packing: true
 
+
 lora_r: 32
 lora_alpha: 16
 lora_dropout: 0
@@ -50,8 +51,8 @@ tf32: false
 gradient_checkpointing: true
 resume_from_checkpoint:
 logging_steps: 1
-attn_implementation: flash_attention_2
+flash_attention: true
-# scaling_softmax: true  # needs flex_attention
+scaling_softmax: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 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
-attn_implementation: flash_attention_2
+flash_attention: true
 
 gradient_accumulation_steps: 1
 micro_batch_size: 1  # must be 1 when using context parallel

examples/eaft/eaft-example.yml

@@ -65,7 +65,8 @@ early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
 logging_steps: 1
-attn_implementation: flash_attention_2
+xformers_attention:
+flash_attention: true
 
 warmup_ratio: 0.1
 weight_decay: 0.0

examples/ebft/llama-1b-ebft-opencode-novllm.yaml

@@ -46,7 +46,7 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-1b-ebft-opencode.yaml

@@ -66,7 +66,7 @@ lora_target_linear: true
 
 # --- Hardware ---
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-1b-ebft-strided-structured.yaml

@@ -47,7 +47,8 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
-attn_implementation: flex_attention
+flash_attention: false  # strided EBFT overrides to flex_attention (or eager fallback) at runtime
+flex_attention: true    # fused flex_attention kernel compiles itself; don't set torch_compile: true
                         # (full-model compile conflicts with gradient checkpointing + flex_attention)
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:

examples/ebft/llama-1b-ebft-strided.yaml

@@ -46,6 +46,7 @@ lora_dropout: 0.05
 lora_target_linear: true
 
 bf16: auto
+flash_attention: false  # strided EBFT overrides to flex_attention (or eager fallback) at runtime
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/llama-3b-ebft-strided-fft.yaml

@@ -48,6 +48,7 @@ lora_target_linear: true
 
 bf16: auto
 torch_dtype: bfloat16
+flash_attention: false
 gradient_checkpointing: true
 torch_compile: true
 gradient_checkpointing_kwargs:

examples/ebft/llama-8b-ebft-strided-fft.yaml

@@ -41,6 +41,7 @@ warmup_steps: 10
 weight_decay: 0.01
 
 bf16: auto
+flash_attention: false  # strided EBFT uses flex_attention at runtime
 gradient_checkpointing: true
 gradient_checkpointing_kwargs:
   use_reentrant: false

examples/ebft/qwen35-4b-ebft-structured-async.yaml

@@ -72,7 +72,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/qwen35-4b-ebft-structured.yaml

@@ -63,7 +63,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/ebft/qwen35-9b-ebft-structured.yaml

@@ -68,7 +68,7 @@ lora_dropout: 0.0
 lora_target_modules: ".*\\.layers\\.(3|7|11|15|19|23|27|31)\\.self_attn\\.(q|k|v|o)_proj|.*\\.mlp\\.(gate|up|down)_proj"
 
 bf16: auto
-attn_implementation: flash_attention_2
+flash_attention: true
 gradient_checkpointing: true
 
 special_tokens:

examples/falcon-e/falcon-e-3b-dpo.yaml

@@ -1,93 +0,0 @@
-base_model: axolotl-ai-co/Falcon-E-1.2-3B-Exp-prequantized
-output_dir: ./output
-
-plugins:
-  - axolotl.integrations.kernels.KernelsPlugin
-
-use_kernels: false
-use_scattermoe: false
-use_sonicmoe: false
-use_onebitllms: true
-
-load_in_8bit: false
-load_in_4bit: false
-
-chat_template: tokenizer_default
-
-rl: dpo
-datasets:
-  - path: allenai/Dolci-Think-DPO-7B
-    split: train
-    type: chatml.ultra
-
-dataset_prepared_path: ./axolotl_dataset_cache
-
-sequence_len: 8192
-trust_remote_code: false
-
-gradient_accumulation_steps: 4 # This can run on 4 GPUs
-
-# Very important to enable gradient accumulation with FSDP
-# https://github.com/huggingface/transformers/issues/29425
-accelerator_config:
-  gradient_accumulation_kwargs:
-    sync_each_batch: True
-
-
-micro_batch_size: 1
-num_epochs: 3
-optimizer: adamw_torch
-lr_scheduler: cosine
-learning_rate: 1.0e-5
-# adamw hyperparams
-adam_beta1: 0.9
-adam_beta2: 0.95
-
-bf16: true
-tf32: false
-
-logging_steps: 1
-
-flash_attention: true
-
-loss_watchdog_threshold: 15.0
-loss_watchdog_patience: 3
-
-warmup_steps: 128
-evals_per_epoch: 0
-
-save_steps: 500
-save_strategy: steps
-
-weight_decay: 0.01
-
-shuffle_merged_datasets: true
-experimental_skip_move_to_device: true
-
-fsdp_version: 2
-fsdp_config:
-  offload_params: false
-  auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  transformer_layer_cls_to_wrap: LlamaDecoderLayer
-  state_dict_type: FULL_STATE_DICT
-  reshard_after_forward: true
-  activation_checkpointing: true
-
-# Comment to disable CP
-# The number of GPUs to shard the model parameters across (FSDP dimension).
-dp_shard_size: 1
-
-# The number of times to replicate the sharded model (DDP dimension).
-dp_replicate_size: 1
-
-# Number of GPUs for Tensor Parallelism.
-tensor_parallel_size: 1  # (default is 1, no TP)
-
-# Number of GPUs for Context/Sequence Parallelism.
-context_parallel_size: 1 # (default is 1, no CP)
-
-special_tokens:
-  eos_token: <|end_of_text|>
-
-eot_tokens:
-  - <|im_end|>