set TORCH_CUDA_ARCH_LIST correctly

* shift logits for diffusion generate

* delete unused

* diffusion trainer: token shift

.github/workflows/base.yml

@@ -25,20 +25,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "124"
-            cuda_version: 12.4.1
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
@@ -67,6 +53,13 @@ jobs:
             pytorch: 2.8.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-base"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-base"
 #          - cuda: "128"
 #            cuda_version: 12.8.1
 #            cudnn_version: ""
@@ -122,13 +115,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-            dockerfile: "Dockerfile-uv-base"
           - cuda: "126"
             cuda_version: 12.6.3
             cudnn_version: ""
@@ -150,6 +136,13 @@ jobs:
             pytorch: 2.8.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
             dockerfile: "Dockerfile-uv-base"
+          - cuda: "128"
+            cuda_version: 12.8.1
+            cudnn_version: ""
+            python_version: "3.11"
+            pytorch: 2.9.0
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
+            dockerfile: "Dockerfile-uv-base"
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -15,11 +15,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -88,11 +83,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -162,11 +152,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"

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

@@ -26,13 +26,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
-            num_gpus: 2
-            nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"

.github/workflows/nightlies.yml

@@ -12,16 +12,16 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -65,16 +65,16 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests-nightly.yml

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

.github/workflows/tests.yml

@@ -55,7 +55,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
+        pytorch_version: ["2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -81,12 +81,12 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
           pip3 show torch
-          pip3 install --no-build-isolation -U -e .
+          pip3 install --no-cache-dir --no-build-isolation -U -e .
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
@@ -130,7 +130,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
+        pytorch_version: ["2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -156,13 +156,13 @@ jobs:
 
       - name: Install PyTorch
         run: |
-          pip3 install torch==${{ matrix.pytorch_version }} torchvision
+          pip3 install --no-cache-dir torch==${{ matrix.pytorch_version }} torchvision
 
       - name: Install dependencies
         run: |
           pip3 show torch
           python -m build --no-isolation --sdist
-          pip3 install --no-build-isolation dist/axolotl*.tar.gz
+          pip3 install --no-cache-dir --no-build-isolation dist/axolotl*.tar.gz
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
           pip3 install -r requirements-dev.txt -r requirements-tests.txt
@@ -286,12 +286,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras:
           - cuda: 128
             cuda_version: 12.8.1
             python_version: "3.11"

.pre-commit-config.yaml

@@ -11,13 +11,13 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.12
+    rev: v0.14.0
     hooks:
     -   id: ruff
         args: [--fix]
     -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.1
+    rev: v1.18.2
     hooks:
     - id: mypy
       additional_dependencies:

README.md

@@ -73,7 +73,7 @@ Features:
 
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
 - Python 3.11
-- PyTorch ≥2.6.0
+- PyTorch ≥2.7.1
 
 ### Google Colab
 

_quarto.yml

@@ -267,6 +267,7 @@ website:
             - docs/dataset_loading.qmd
             - docs/qat.qmd
             - docs/quantize.qmd
+            - docs/optimizations.qmd
 
         - section: "Core Concepts"
           contents:

cicd/Dockerfile-uv.jinja

@@ -32,6 +32,7 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN uv pip install packaging==23.2 setuptools==75.8.0
+RUN uv pip install torchvision
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         uv pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/Dockerfile.jinja

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

cicd/single_gpu.py

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

devtools/dev_chat_template.yml

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

docker/Dockerfile-base

@@ -47,6 +47,8 @@ RUN git lfs install --skip-repo && \
     pip3 install -U --no-cache-dir pydantic==1.10.10 && \
     pip3 cache purge
 
-RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "124" ] ; then \
-        FLASH_ATTENTION_FORCE_BUILD="TRUE" pip3 install --no-build-isolation flash-attn==2.8.0.post2; \
+RUN if [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        pip3 install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
     fi

docker/Dockerfile-uv-base

@@ -30,7 +30,13 @@ RUN uv venv --no-project --relocatable axolotl-venv
 ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
 
 RUN uv pip install packaging setuptools wheel psutil \
-    && uv pip install torch==${PYTORCH_VERSION} \
+    && uv pip install torch==${PYTORCH_VERSION} torchvision \
     && uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
     && uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
     && uv pip install awscli pydantic
+
+RUN if [ "$PYTORCH_VERSION" = "2.9.0" ] && [ "$CUDA" = "128" ] ; then \
+        wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        uv pip install --no-cache-dir flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+        rm flash_attn-2.8.3+cu128torch2.9-cp311-cp311-linux_x86_64.whl; \
+    fi

docs/dataset-formats/conversation.qmd

@@ -212,6 +212,14 @@ Instead of passing `tools` via the system prompt, an alternative method would be
 Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
 :::
 
+::: {.callout-warning}
+If you have tool arguments with same name but different dtypes (like `"time": string` and `"time": number`), please save `arguments: ` as JSON string to prevent `datasets` from having casting issues.
+
+```
+"arguments": "{\"...\": \"...\"}"
+```
+:::
+
 Example config for Llama4:
 ```yaml
 chat_template: llama4

docs/dataset-formats/index.qmd

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

docs/debugging.qmd

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

docs/faq.qmd

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

docs/fsdp_qlora.qmd

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

docs/lora_optims.qmd

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

docs/lr_groups.qmd

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

docs/multi-gpu.qmd

@@ -88,6 +88,7 @@ fsdp_sync_module_states | **REMOVED**
 fsdp_cpu_ram_efficient_loading | cpu_ram_efficient_loading
 fsdp_state_dict_type | state_dict_type
 fsdp_use_orig_params | **REMOVED**
+fsdp_activation_checkpointing | activation_checkpointing
 
 For more details, please see the migration guide in the [torchtitan repo](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md). In Axolotl,
 if you were using the following FSDP1 config:

docs/multimodal.qmd

@@ -56,10 +56,14 @@ image_resize_algorithm: bilinear
 
 Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
 
-::: {.callout-warning}
+::: {.callout-tip}
 Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
 :::
 
+::: {.callout-note}
+As of now, we do not truncate nor drop samples based on `sequence_len` as each arch has different ways to process non-text tokens. We are looking for help on this.
+:::
+
 ### Mllama {#sec-mllama}
 
 ```yaml
@@ -168,6 +172,14 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### Qwen3-VL {#sec-qwen3-vl}
+
+```yaml
+base_model: Qwen/Qwen3-VL-4B-Instruct
+
+chat_template: qwen2_vl  # same as qwen2-vl
+```
+
 ### SmolVLM2 {#sec-smolvlm2}
 
 ::: {.callout-tip}

docs/optimizations.qmd

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

docs/qat.qmd

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

docs/rlhf.qmd

@@ -219,6 +219,21 @@ DPO supports the following types with the following dataset format:
 }
 ```
 
+#### chat_template.argilla_chat
+
+```json
+{
+    "chosen": [
+        {"role": "user", "content": "..."},
+        {"role": "assistant", "content": "..."}
+    ],
+    "rejected": [
+        {"role": "user", "content": "..."},
+        {"role": "assistant", "content": "..."}
+    ]
+}
+```
+
 #### chat_template.default
 
 ```yaml

examples/LiquidAI/README.md

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

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

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

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

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

examples/LiquidAI/lfm2-vl-lora.yaml

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

examples/alst/README.md

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

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

@@ -40,7 +40,7 @@
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
     "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@c5aa3ef\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec\""
    ]
   },
   {

examples/llama-2/qlora-fsdp.yml

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

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

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

examples/llama-3/opentelemetry-qlora.yml

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

examples/magistral/think/README.md

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

examples/magistral/vision/README.md

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

examples/mistral/mistral-small/README.md

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

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

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

examples/qwen3-next/README.md

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

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

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

requirements.txt

@@ -5,20 +5,19 @@ bitsandbytes==0.47.0
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-autoawq==0.2.7.post3
-liger-kernel==0.6.1
+liger-kernel==0.6.3
 # END section
 
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft>=0.17.0
-transformers==4.56.1
+peft>=0.17.1
 tokenizers>=0.21.1
+transformers==4.57.1
 accelerate==1.10.1
 datasets==4.0.0
 deepspeed>=0.17.0
-trl==0.23.0
+trl==0.23.1
 hf_xet==1.1.5
 kernels==0.9.0
 trackio

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@c5aa3ef"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"'
 )

setup.py

@@ -26,7 +26,6 @@ def parse_requirements(extras_require_map):
                 _install_requires.append(line)
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
-        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
         if "Darwin" in platform.system():
             # skip packages not compatible with OSX
             skip_packages = [
@@ -34,7 +33,6 @@ def parse_requirements(extras_require_map):
                 "triton",
                 "mamba-ssm",
                 "xformers",
-                "autoawq",
                 "liger-kernel",
             ]
             _install_requires = [
@@ -51,7 +49,7 @@ def parse_requirements(extras_require_map):
             try:
                 torch_version = version("torch")
             except PackageNotFoundError:
-                torch_version = "2.6.0"  # default to torch 2.6
+                torch_version = "2.8.0"  # default to torch 2.8.0
             _install_requires.append(f"torch=={torch_version}")
 
             version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
@@ -87,7 +85,6 @@ def parse_requirements(extras_require_map):
                     _install_requires.append("xformers==0.0.28.post2")
                 else:
                     _install_requires.append("xformers>=0.0.28.post3")
-                _install_requires.pop(_install_requires.index(autoawq_version))
                 extras_require_map.pop("vllm")
             elif (major, minor) >= (2, 4):
                 extras_require_map.pop("vllm")
@@ -162,6 +159,12 @@ extras_require = {
         "llmcompressor==0.5.1",
     ],
     "fbgemm-gpu": ["fbgemm-gpu-genai>=1.2.0"],
+    "opentelemetry": [
+        "opentelemetry-api",
+        "opentelemetry-sdk",
+        "opentelemetry-exporter-prometheus",
+        "prometheus-client",
+    ],
 }
 install_requires, dependency_links, extras_require_build = parse_requirements(
     extras_require

src/axolotl/cli/delinearize_llama4.py

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

src/axolotl/cli/quantize.py

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

src/axolotl/cli/train.py

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

src/axolotl/common/architectures.py

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

src/axolotl/core/builders/base.py

@@ -29,7 +29,11 @@ from transformers.trainer_pt_utils import AcceleratorConfig
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.trainer.lr import patch_trainer_get_lr
-from axolotl.utils import is_comet_available, is_mlflow_available
+from axolotl.utils import (
+    is_comet_available,
+    is_mlflow_available,
+    is_opentelemetry_available,
+)
 from axolotl.utils.callbacks import (
     GCCallback,
     SaveAxolotlConfigtoWandBCallback,
@@ -134,6 +138,12 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoCometCallback(self.cfg.axolotl_config_path)
             )
+        if self.cfg.use_otel_metrics and is_opentelemetry_available():
+            from axolotl.utils.callbacks.opentelemetry import (
+                OpenTelemetryMetricsCallback,
+            )
+
+            callbacks.append(OpenTelemetryMetricsCallback(self.cfg))
         if self.cfg.save_first_step:
             callbacks.append(SaveModelOnFirstStepCallback())
 
@@ -491,6 +501,7 @@ class TrainerBuilderBase(abc.ABC):
             "dion_momentum",
             "dion_rank_fraction",
             "dion_rank_multiple_of",
+            "dataset_num_proc",
         ]:
             if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
                 training_args_kwargs[arg] = getattr(self.cfg, arg)
@@ -514,9 +525,6 @@ class TrainerBuilderBase(abc.ABC):
         training_args_kwargs["max_steps"] = self.cfg.max_steps or total_num_steps or -1
         training_args_kwargs["num_train_epochs"] = self.cfg.num_epochs
 
-        if self.cfg.dataset_processes:
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
         # max_length is not used in CausalTrainer
         if self.cfg.reward_model or self.cfg.rl:
             training_args_kwargs["max_length"] = self.cfg.sequence_len

src/axolotl/core/builders/causal.py

@@ -28,7 +28,6 @@ from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
     LossWatchDogCallback,
-    SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     colab_inference_post_train_callback,
@@ -63,12 +62,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.relora:
             callbacks.append(ReLoRACallback(self.cfg))
 
-        if (
-            hasattr(self.model, "use_bettertransformer")
-            and self.model.use_bettertransformer is True
-        ):
-            callbacks.append(SaveBetterTransformerModelCallback())
-
         # TODO: check if can move to base class
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))

src/axolotl/core/trainers/base.py

@@ -225,17 +225,6 @@ class AxolotlTrainer(
 
         data_collator = self.data_collator if is_training else self.eval_data_collator
 
-        if dataset.column_names and "length" in dataset.column_names:
-            dataset = dataset.remove_columns(["length"])
-        if (
-            dataset.column_names
-            and "position_ids" in dataset.column_names
-            and "attention_mask" in dataset.column_names
-            and self.args.sample_packing
-            and self.args.sample_packing_drop_attention_mask
-        ):
-            dataset = dataset.remove_columns(["attention_mask"])
-
         if isinstance(dataset, datasets.Dataset):
             if is_training:
                 if not self.args.sample_packing or self.args.pretraining:
@@ -294,6 +283,18 @@ class AxolotlTrainer(
         ):
             self.accelerator.even_batches = False
 
+        if dataset.column_names and "length" in dataset.column_names:
+            dataset = dataset.remove_columns(["length"])
+
+        if (
+            dataset.column_names
+            and "position_ids" in dataset.column_names
+            and "attention_mask" in dataset.column_names
+            and self.args.sample_packing
+            and self.args.sample_packing_drop_attention_mask
+        ):
+            dataset = dataset.remove_columns(["attention_mask"])
+
         dataloader = DataLoader(dataset, **dataloader_params)
 
         # Accelerator.free_memory() will destroy the references, so
@@ -560,13 +561,6 @@ class AxolotlTrainer(
 
         super().create_accelerator_and_postprocess()
 
-        if self.is_fsdp_enabled:
-            if (
-                "limit_all_gathers" in self.args.fsdp_config
-                and self.args.fsdp_config["limit_all_gathers"]
-            ):
-                self.accelerator.state.fsdp_plugin.limit_all_gathers = True
-
     def additional_accelerator_args(
         self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
     ) -> dict[str, Any]:

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

@@ -52,6 +52,7 @@ class GRPOStrategy:
             if trl.vllm_mode:
                 grpo_args_kwargs["vllm_mode"] = trl.vllm_mode
             if trl.vllm_mode == "colocate":
+                grpo_args_kwargs["vllm_enable_sleep_mode"] = trl.vllm_enable_sleep_mode  # type: ignore[attr-defined]
                 grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
                     vllm_cfg.gpu_memory_utilization
                 )

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@c5aa3ef"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"
 ```
 
 ## Usage
@@ -31,6 +31,7 @@ plugins:
 
 ## Supported Models
 
+- apertus
 - arcee
 - cohere
 - cohere2
@@ -44,14 +45,22 @@ plugins:
 - glm
 - glm4
 - glm4_moe
+- glm4v
+- glm4v_moe
 - gpt_oss
 - granite
 - granitemoe
+- granitemoeshared
+- granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
+- lfm2
+- lfm2_moe
+- lfm2_vl
 - llama
 - llama4
 - llama4_text
+- llava
 - mistral
 - mistral3
 - mixtral
@@ -65,6 +74,8 @@ plugins:
 - qwen2_5_vl
 - qwen3
 - qwen3_moe
+- qwen3_vl
+- qwen3_vl_moe
 - qwen3_next
 - smollm3
 - seed_oss

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@c5aa3ef"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"`'
 )
 
 

src/axolotl/integrations/diffusion/generation.py

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

src/axolotl/integrations/diffusion/trainer.py

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

src/axolotl/integrations/diffusion/utils.py

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

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

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

src/axolotl/integrations/kd/trainer.py

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

src/axolotl/loaders/model.py

@@ -515,9 +515,6 @@ class ModelLoader:
             if self.cfg.model_quantization_config_kwargs:
                 mxfp4_kwargs = self.cfg.model_quantization_config_kwargs
             self.model_kwargs["quantization_config"] = Mxfp4Config(**mxfp4_kwargs)
-        else:
-            self.model_kwargs["load_in_8bit"] = self.cfg.load_in_8bit
-            self.model_kwargs["load_in_4bit"] = self.cfg.load_in_4bit
 
         if self.cfg.gptq:
             if not hasattr(self.model_config, "quantization_config"):
@@ -552,9 +549,7 @@ class ModelLoader:
                 self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                     **self.model_config.quantization_config
                 )
-        elif self.cfg.adapter == "qlora" and self.model_kwargs.get(
-            "load_in_4bit", False
-        ):
+        elif self.cfg.adapter == "qlora" and self.cfg.load_in_4bit:
             bnb_config = {
                 "load_in_4bit": True,
                 "llm_int8_threshold": 6.0,
@@ -580,9 +575,7 @@ class ModelLoader:
             self.model_kwargs["quantization_config"] = BitsAndBytesConfig(
                 **bnb_config,
             )
-        elif self.cfg.adapter == "lora" and self.model_kwargs.get(
-            "load_in_8bit", False
-        ):
+        elif self.cfg.adapter == "lora" and self.cfg.load_in_8bit:
             bnb_config = {
                 "load_in_8bit": True,
             }
@@ -596,11 +589,6 @@ class ModelLoader:
                 **bnb_config,
             )
 
-        # no longer needed per https://github.com/huggingface/transformers/pull/26610
-        if "quantization_config" in self.model_kwargs or self.cfg.gptq:
-            self.model_kwargs.pop("load_in_8bit", None)
-            self.model_kwargs.pop("load_in_4bit", None)
-
     def _set_attention_config(self):
         """Sample packing uses custom FA2 patch"""
         if self.cfg.attn_implementation:

src/axolotl/loaders/patch_manager.py

@@ -84,6 +84,13 @@ class PatchManager:
         patch_evaluation_loop()
         patch_maybe_log_save_evaluate()
 
+        if self.cfg.context_parallel_size > 1:
+            from axolotl.monkeypatch.transformers.trainer_context_parallel import (
+                patch_prepare_context_parallel_inputs,
+            )
+
+            patch_prepare_context_parallel_inputs()
+
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
         self._apply_llama_flash_attn_patches(model)

src/axolotl/monkeypatch/accelerate/fsdp2.py

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

src/axolotl/monkeypatch/lora_kernels.py

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

src/axolotl/monkeypatch/multipack.py

@@ -45,6 +45,8 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "gpt_oss",
     "arcee",
     "seed_oss",
+    "lfm2",
+    "lfm2_moe",
 ]
 
 

src/axolotl/monkeypatch/transformers/trainer_context_parallel.py

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

src/axolotl/processing_strategies.py

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

src/axolotl/prompt_strategies/chat_template.py

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

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -120,3 +120,123 @@ def default(cfg, dataset_idx=0, **kwargs):
         return result
 
     return transform_fn, {"remove_columns": [field_messages]}
+
+
+def argilla_chat(cfg, dataset_idx=0, **kwargs):
+    """
+    DPO chat template strategy for argilla-style datasets.
+
+    For argilla-style datasets where chosen/rejected contain full conversations
+    instead of single response messages. Extracts the conversation history from
+    the chosen field and formats both chosen/rejected responses using the
+    configured chat template.
+
+    Args:
+        cfg: Configuration object containing chat_template and dataset settings
+        dataset_idx: Index of the dataset in the config (default: 0)
+        **kwargs: Additional keyword arguments (unused)
+
+    Returns:
+        tuple: (transform_fn, dataset_kwargs) where:
+            - transform_fn: Function to transform dataset samples
+            - dataset_kwargs: Dict with 'remove_columns' specifying columns to drop
+
+    Dataset format:
+        {
+            "chosen": [
+                {"role": "user", "content": "..."},
+                {"role": "assistant", "content": "..."}
+            ],
+            "rejected": [
+                {"role": "user", "content": "..."},
+                {"role": "assistant", "content": "..."}
+            ]
+        }
+    """
+    ds_cfg = cfg["datasets"][dataset_idx]
+    ds_cfg = handle_legacy_message_fields_logic(ds_cfg)
+
+    chat_template_choice, chat_template_jinja = extract_chat_template_args(
+        cfg=cfg, ds_cfg=ds_cfg
+    )
+    field_chosen = ds_cfg.get("field_chosen", "chosen")
+    field_rejected = ds_cfg.get("field_rejected", "rejected")
+    message_property_mappings = ds_cfg.get(
+        "message_property_mappings",
+        {
+            "role": "role",
+            "content": "content",
+        },
+    )
+    role_map_inv = ds_cfg.get(
+        "roles",
+        {
+            "user": ["user"],
+            "assistant": ["assistant"],
+            "system": ["system"],
+        },
+    )
+    role_map = {}
+    for target, sources in role_map_inv.items():
+        for source in sources:
+            role_map[source] = target
+
+    def transform_fn(sample, tokenizer=None):
+        chat_template_string = get_chat_template(
+            user_choice=chat_template_choice,
+            jinja_template=chat_template_jinja,
+            tokenizer=tokenizer,
+        )
+
+        chosen_raw = sample[field_chosen]
+        rejected_raw = sample[field_rejected]
+
+        # Extract messages (all but last) and responses (last message)
+        chosen_messages = [
+            {
+                "role": role_map[m[message_property_mappings["role"]]],
+                "content": m[message_property_mappings["content"]],
+            }
+            for m in chosen_raw[:-1]
+        ]
+        chosen_response = {
+            "role": role_map[chosen_raw[-1][message_property_mappings["role"]]],
+            "content": chosen_raw[-1][message_property_mappings["content"]],
+        }
+
+        rejected_response = {
+            "role": role_map[rejected_raw[-1][message_property_mappings["role"]]],
+            "content": rejected_raw[-1][message_property_mappings["content"]],
+        }
+
+        dummy_user_message = {"role": "user", "content": "[[dummy_message]]"}
+
+        result = {}
+        result["prompt"] = tokenizer.apply_chat_template(
+            chosen_messages,
+            add_generation_prompt=True,
+            chat_template=chat_template_string,
+            tokenize=False,
+        )
+
+        result["chosen"] = tokenizer.apply_chat_template(
+            [dummy_user_message, chosen_response],
+            add_generation_prompt=False,
+            chat_template=chat_template_string,
+            tokenize=False,
+        )
+        chosen_strip_index = result["chosen"].find(chosen_response["content"])
+        result["chosen"] = result["chosen"][chosen_strip_index:].rstrip()
+
+        result["rejected"] = tokenizer.apply_chat_template(
+            [dummy_user_message, rejected_response],
+            add_generation_prompt=False,
+            chat_template=chat_template_string,
+            tokenize=False,
+        )
+        rejected_strip_index = result["rejected"].find(rejected_response["content"])
+        result["rejected"] = result["rejected"][rejected_strip_index:].rstrip()
+
+        return result
+
+    return transform_fn, {"remove_columns": [field_chosen, field_rejected]}

src/axolotl/train.py

@@ -40,11 +40,6 @@ from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.train import determine_last_checkpoint
 from axolotl.utils.trainer import setup_trainer
 
-try:
-    from optimum.bettertransformer import BetterTransformer
-except ImportError:
-    BetterTransformer = None
-
 if typing.TYPE_CHECKING:
     from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 
@@ -141,8 +136,6 @@ def setup_signal_handler(
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
                 _model = model_weakref()
-                if cfg.flash_optimum and BetterTransformer:
-                    _model = BetterTransformer.reverse(_model)
                 _model.save_pretrained(
                     cfg.output_dir, safe_serialization=safe_serialization
                 )
@@ -321,9 +314,6 @@ def save_trained_model(
             except FileNotFoundError:
                 pass
     elif cfg.local_rank == 0:
-        if cfg.flash_optimum and BetterTransformer:
-            model = BetterTransformer.reverse(model)
-
         if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
             trainer.model.save_pretrained(
                 cfg.output_dir, safe_serialization=safe_serialization
@@ -535,6 +525,17 @@ def setup_model_and_trainer(
     plugin_manager = PluginManager.get_instance()
     plugin_manager.post_trainer_create(cfg, trainer)
 
+    if cfg.use_ray:
+        try:
+            import ray.train.huggingface.transformers
+
+            trainer = ray.train.huggingface.transformers.prepare_trainer(trainer)
+        except ImportError:
+            LOG.warning(
+                "The Ray integration with Hugging Face Transformers is not available. "
+                "To use Ray, install the 'ray[train]' package."
+            )
+
     return (
         trainer,
         model,

src/axolotl/utils/__init__.py

@@ -17,6 +17,13 @@ def is_comet_available():
     return importlib.util.find_spec("comet_ml") is not None
 
 
+def is_opentelemetry_available():
+    return (
+        importlib.util.find_spec("opentelemetry") is not None
+        and importlib.util.find_spec("prometheus_client") is not None
+    )
+
+
 def get_pytorch_version() -> tuple[int, int, int]:
     """
     Get Pytorch version as a tuple of (major, minor, patch).

src/axolotl/utils/callbacks/__init__.py

@@ -16,8 +16,8 @@ import pandas as pd
 import torch
 import torch.distributed as dist
 import wandb
+import yaml
 from datasets import load_dataset
-from optimum.bettertransformer import BetterTransformer
 from tqdm import tqdm
 from transformers import (
     GenerationConfig,
@@ -28,8 +28,6 @@ from transformers import (
     TrainingArguments,
 )
 from transformers.trainer_utils import (
-    PREFIX_CHECKPOINT_DIR,
-    IntervalStrategy,
     SaveStrategy,
 )
 from trl.models import unwrap_model_for_generation
@@ -56,40 +54,6 @@ IGNORE_INDEX = -100
 LOG = get_logger(__name__)
 
 
-class SaveBetterTransformerModelCallback(TrainerCallback):
-    """Callback to save the BetterTransformer wrapped model"""
-
-    def on_step_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ) -> TrainerControl:
-        # Save
-        if (
-            args.save_strategy == IntervalStrategy.STEPS
-            and args.save_steps > 0
-            and state.global_step % args.save_steps == 0
-        ):
-            control.should_save = True
-
-        if control.should_save:
-            checkpoint_folder = os.path.join(
-                args.output_dir,
-                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
-            )
-
-            model = BetterTransformer.reverse(kwargs["model"])
-            model.save_pretrained(checkpoint_folder)
-            # FIXME - need to cleanup old checkpoints
-
-            # since we're saving here, we don't need the trainer loop to attempt to save too b/c
-            # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
-            control.should_save = False
-        return control
-
-
 class LossWatchDogCallback(TrainerCallback):
     """Callback to track loss and stop training if loss is too high"""
 
@@ -796,6 +760,37 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
 
+            try:
+                with open(self.axolotl_config_path, "r", encoding="utf-8") as f:
+                    cfg = yaml.safe_load(f) or {}
+
+                chat_tpl = cfg.get("chat_template_jinja")
+                if chat_tpl:
+                    with NamedTemporaryFile(
+                        mode="w", delete=True, suffix=".jinja", prefix="chat_template_"
+                    ) as temp_ct_file:
+                        if (
+                            isinstance(chat_tpl, str)
+                            and os.path.exists(chat_tpl)
+                            and os.path.isfile(chat_tpl)
+                        ):
+                            copyfile(chat_tpl, temp_ct_file.name)
+                        else:
+                            temp_ct_file.write(str(chat_tpl))
+                            temp_ct_file.flush()
+
+                        artifact = wandb.Artifact(
+                            f"chat-template-{wandb.run.id}", type="jinja-template"
+                        )
+                        artifact.add_file(temp_ct_file.name)
+                        wandb.log_artifact(artifact)
+                        wandb.save(temp_ct_file.name)
+                        LOG.info(
+                            "The chat_template_jinja has been saved to the WandB run under files."
+                        )
+            except (FileNotFoundError, ConnectionError, yaml.YAMLError) as err:
+                LOG.warning(f"Error while saving chat_template_jinja to WandB: {err}")
+
             if args.deepspeed:
                 try:
                     # sync config to top level in run, cannot delete file right away because wandb schedules it to be synced even w/policy = 'now', so let OS delete it later.

src/axolotl/utils/callbacks/opentelemetry.py

@@ -0,0 +1,238 @@
+"""OpenTelemetry metrics callback for Axolotl training"""
+
+import threading
+from typing import Dict, Optional
+
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+try:
+    from opentelemetry import metrics
+    from opentelemetry.exporter.prometheus import PrometheusMetricReader
+    from opentelemetry.metrics import set_meter_provider
+    from opentelemetry.sdk.metrics import MeterProvider as SDKMeterProvider
+    from prometheus_client import start_http_server
+
+    OPENTELEMETRY_AVAILABLE = True
+except ImportError:
+    LOG.warning("OpenTelemetry not available. pip install [opentelemetry]")
+    OPENTELEMETRY_AVAILABLE = False
+
+
+class OpenTelemetryMetricsCallback(TrainerCallback):
+    """
+    TrainerCallback that exports training metrics to OpenTelemetry/Prometheus.
+
+    This callback automatically tracks key training metrics including:
+    - Training loss
+    - Evaluation loss
+    - Learning rate
+    - Epoch progress
+    - Global step count
+    - Gradient norm
+
+    Metrics are exposed via HTTP endpoint for Prometheus scraping.
+    """
+
+    def __init__(self, cfg):
+        if not OPENTELEMETRY_AVAILABLE:
+            LOG.warning("OpenTelemetry not available, metrics will not be collected")
+            self.metrics_enabled = False
+            return
+
+        self.cfg = cfg
+        self.metrics_host = getattr(cfg, "otel_metrics_host", "localhost")
+        self.metrics_port = getattr(cfg, "otel_metrics_port", 8000)
+        self.metrics_enabled = True
+        self.server_started = False
+        self.metrics_lock = threading.Lock()
+
+        try:
+            # Create Prometheus metrics reader
+            prometheus_reader = PrometheusMetricReader()
+
+            # Create meter provider with Prometheus exporter
+            provider = SDKMeterProvider(metric_readers=[prometheus_reader])
+            set_meter_provider(provider)
+
+            # Get meter for creating metrics
+            self.meter = metrics.get_meter("axolotl.training")
+
+            # Create metrics
+            self._create_metrics()
+
+        except Exception as e:
+            LOG.warning(f"Failed to initialize OpenTelemetry metrics: {e}")
+            self.metrics_enabled = False
+
+    def _create_metrics(self):
+        """Create all metrics that will be tracked"""
+        self.train_loss_gauge = self.meter.create_gauge(
+            name="axolotl_train_loss",
+            description="Current training loss",
+            unit="1",
+        )
+
+        self.eval_loss_gauge = self.meter.create_gauge(
+            name="axolotl_eval_loss",
+            description="Current evaluation loss",
+            unit="1",
+        )
+
+        self.learning_rate_gauge = self.meter.create_gauge(
+            name="axolotl_learning_rate",
+            description="Current learning rate",
+            unit="1",
+        )
+
+        self.epoch_gauge = self.meter.create_gauge(
+            name="axolotl_epoch",
+            description="Current training epoch",
+            unit="1",
+        )
+
+        self.global_step_counter = self.meter.create_counter(
+            name="axolotl_global_steps",
+            description="Total training steps completed",
+            unit="1",
+        )
+
+        self.grad_norm_gauge = self.meter.create_gauge(
+            name="axolotl_gradient_norm",
+            description="Gradient norm",
+            unit="1",
+        )
+
+        self.memory_usage_gauge = self.meter.create_gauge(
+            name="axolotl_memory_usage",
+            description="Current memory usage in MB",
+            unit="MB",
+        )
+
+    def _start_metrics_server(self):
+        """Start the HTTP server for metrics exposure"""
+        if self.server_started:
+            return
+
+        try:
+            start_http_server(self.metrics_port, addr=self.metrics_host)
+            self.server_started = True
+            LOG.info(
+                f"OpenTelemetry metrics server started on http://{self.metrics_host}:{self.metrics_port}/metrics"
+            )
+
+        except Exception as e:
+            LOG.error(f"Failed to start OpenTelemetry metrics server: {e}")
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Called at the beginning of training"""
+        if not self.metrics_enabled:
+            return
+
+        self._start_metrics_server()
+        LOG.info("OpenTelemetry metrics collection started")
+
+    def on_log(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        logs: Optional[Dict[str, float]] = None,
+        **kwargs,
+    ):
+        """Called when logging occurs"""
+        if not self.metrics_enabled or not logs:
+            return
+
+        if "loss" in logs:
+            self.train_loss_gauge.set(logs["loss"])
+
+        if "eval_loss" in logs:
+            self.eval_loss_gauge.set(logs["eval_loss"])
+
+        if "learning_rate" in logs:
+            self.learning_rate_gauge.set(logs["learning_rate"])
+
+        if "epoch" in logs:
+            self.epoch_gauge.set(logs["epoch"])
+
+        if "grad_norm" in logs:
+            self.grad_norm_gauge.set(logs["grad_norm"])
+        if "memory_usage" in logs:
+            self.memory_usage_gauge.set(logs["memory_usage"])
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Called at the end of each training step"""
+        if not self.metrics_enabled:
+            return
+
+        # Update step counter and epoch
+        self.global_step_counter.add(1)
+        if state.epoch is not None:
+            self.epoch_gauge.set(state.epoch)
+
+    def on_evaluate(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        metrics: Optional[Dict[str, float]] = None,
+        **kwargs,
+    ):
+        """Called after evaluation"""
+        if not self.metrics_enabled or not metrics:
+            return
+
+        if "eval_loss" in metrics:
+            self.eval_loss_gauge.set(metrics["eval_loss"])
+
+        # Record any other eval metrics as gauges
+        for key, value in metrics.items():
+            if key.startswith("eval_") and isinstance(value, (int, float)):
+                # Create gauge for this metric if it doesn't exist
+                gauge_name = f"axolotl_{key}"
+                try:
+                    gauge = self.meter.create_gauge(
+                        name=gauge_name,
+                        description=f"Evaluation metric: {key}",
+                        unit="1",
+                    )
+                    gauge.set(value)
+                except Exception as e:
+                    LOG.warning(f"Failed to create/update metric {gauge_name}: {e}")
+
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        """Called at the end of training"""
+        if not self.metrics_enabled:
+            return
+
+        LOG.info("Training completed. OpenTelemetry metrics collection finished.")
+        LOG.info(
+            f"Metrics are still available at http://{self.metrics_host}:{self.metrics_port}/metrics"
+        )

src/axolotl/utils/data/rl.py

@@ -113,7 +113,7 @@ def _map_dataset(
 
     dataset = dataset.map(
         ds_transform_fn,
-        num_proc=cfg.dataset_processes,
+        num_proc=cfg.dataset_num_proc,
         load_from_cache_file=not cfg.is_preprocess,
         desc="Mapping RL Dataset",
         **map_kwargs,
@@ -234,7 +234,7 @@ def _load_split(cfg: DictDefault, split: Literal["train", "test"]) -> Dataset:
             prior_len = len(split_datasets[i])
             split_datasets[i] = split_datasets[i].filter(
                 drop_long,
-                num_proc=cfg.dataset_processes,
+                num_proc=cfg.dataset_num_proc,
                 load_from_cache_file=not cfg.is_preprocess,
                 desc="Dropping Long Sequences",
             )

src/axolotl/utils/data/shared.py

@@ -239,6 +239,11 @@ def _load_from_local_path(
             return load_dataset(dataset_config.path, **load_dataset_kwargs)
     elif local_path.is_file():
         dataset_type = get_dataset_type(dataset_config)
+
+        # For single file datasets, HF always creates only a "train" split
+        if dataset_type in ("json", "csv", "text"):
+            load_dataset_kwargs["split"] = "train"
+
         return load_dataset(
             dataset_type,
             data_files=dataset_config.path,
@@ -409,7 +414,7 @@ def save_preprocessed_dataset(
 ) -> None:
     """Save preprocessed dataset to disk and optionally push to the HF Hub."""
     prepared_ds_path = get_prepared_dataset_path(cfg, dataset_hash)
-    num_workers = cfg.dataset_processes or get_default_process_count()
+    num_workers = cfg.dataset_num_proc or get_default_process_count()
     if isinstance(dataset, IterableDataset):
         ds_from_iter = Dataset.from_generator(
             functools.partial(_generate_from_iterable_dataset, dataset),

src/axolotl/utils/data/utils.py

@@ -223,7 +223,7 @@ def handle_long_seq_in_dataset(
 
     filter_map_kwargs = {}
     if not isinstance(dataset, IterableDataset):
-        filter_map_kwargs["num_proc"] = cfg.dataset_processes
+        filter_map_kwargs["num_proc"] = cfg.dataset_num_proc
         filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
 
     drop_long_kwargs = {}

src/axolotl/utils/data/wrappers.py

@@ -80,7 +80,7 @@ def get_dataset_wrapper(
     """
     # Common parameters for dataset wrapping
     dataset_kwargs: dict[str, Any] = {
-        "process_count": cfg.dataset_processes,
+        "process_count": cfg.dataset_num_proc,
         "keep_in_memory": cfg.dataset_keep_in_memory is True,
     }
 

src/axolotl/utils/datasets.py

@@ -4,6 +4,8 @@ import os
 
 
 def get_default_process_count():
+    if axolotl_dataset_num_proc := os.environ.get("AXOLOTL_DATASET_NUM_PROC"):
+        return int(axolotl_dataset_num_proc)
     if axolotl_dataset_processes := os.environ.get("AXOLOTL_DATASET_PROCESSES"):
         return int(axolotl_dataset_processes)
     if runpod_cpu_count := os.environ.get("RUNPOD_CPU_COUNT"):

src/axolotl/utils/environment.py

@@ -3,66 +3,46 @@ utils to get GPU info for the current environment
 """
 
 import os
-import subprocess  # nosec B404
 from importlib.metadata import version
 
+import torch
 from accelerate.utils.environment import (
     check_cuda_p2p_ib_support as accelerate_check_cuda_p2p_ib_support,
-    get_gpu_info,
 )
 from packaging.version import Version, parse
 
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
 
 def check_cuda_p2p_ib_support():
     if not accelerate_check_cuda_p2p_ib_support():
         return False
-    if not check_runpod_p2p_support():
+    if not check_cuda_p2p_support():
         return False
-    unsupported_devices = {"RTX 6000 Ada", "L40S"}
-    try:
-        device_names, device_count = get_gpu_info()
-        if 1 < device_count < 8:
-            if any(
-                unsupported_device in device_name
-                for device_name in device_names
-                for unsupported_device in unsupported_devices
-            ):
-                return False
-    except Exception:  # nosec B110
-        pass
     return True
 
 
-def check_runpod_p2p_support() -> bool:
-    if "RUNPOD_GPU_COUNT" not in os.environ:
-        return True
+def check_cuda_p2p_support() -> bool:
     try:
-        gpu_count = int(os.environ.get("RUNPOD_GPU_COUNT", "1"))
+        world_size = int(os.environ.get("WORLD_SIZE", "1"))
+        local_rank = int(os.environ.get("LOCAL_RANK", "0"))
     except ValueError:
         return True
-    if gpu_count >= 2:
-        # run `nvidia-smi topo -p2p n` and inspect the GPU0 row
+
+    if world_size > 1:
+        node_world_size = int(os.environ.get("NODE_WORLD_SIZE", "8"))
+        local_other_rank = (local_rank // node_world_size) * node_world_size
+        local_other_rank += 1 if (local_rank % node_world_size) == 0 else 0
         try:
-            result = subprocess.run(  # nosec B603 B607
-                ["nvidia-smi", "topo", "-p2p", "n"],
-                check=True,
-                capture_output=True,
-                text=True,
-                timeout=5,
-            )
-        except (
-            subprocess.CalledProcessError,
-            FileNotFoundError,
-            subprocess.TimeoutExpired,
-        ):
-            return True  # fail-open if detection fails
-        output_lines = result.stdout.strip().split("\n")
-        # filter rows that start with "GPU0" (avoid header row)
-        gpu0_rows = [line for line in output_lines if line.lstrip().startswith("GPU0")]
-        if not gpu0_rows:
+            can_p2p = torch.cuda.can_device_access_peer(local_rank, local_other_rank)
+        except AssertionError as exc:
+            # some sort of logic error in indexing processes, assume p2p is fine for now
+            LOG.warning(exc)
             return True
-        # consider P2P supported if any OK is present in the GPU0 row
-        return "OK" in gpu0_rows[-1]
+        return can_p2p
+
     return True
 
 

src/axolotl/utils/model_shard_quant.py

@@ -148,7 +148,7 @@ def load_sharded_model(
         model = AutoModelForCausalLM.from_pretrained(
             model_name,
             use_cache=False,
-            torch_dtype=torch.float32,
+            dtype=torch.float32,
             _attn_implementation=model_config._attn_implementation,
             trust_remote_code=cfg.trust_remote_code,
         )
@@ -158,7 +158,7 @@ def load_sharded_model(
         with init_empty_weights():
             model = AutoModelForCausalLM.from_config(
                 model_config,
-                torch_dtype=torch_dtype,
+                dtype=torch_dtype,
                 trust_remote_code=cfg.trust_remote_code,
             )
     return model

src/axolotl/utils/samplers/multipack.py

@@ -5,6 +5,7 @@ into fixed-capacity batches to optimize memory usage and training throughput.
 
 import gc
 import math
+import os
 import time
 from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import cpu_count, get_context
@@ -291,7 +292,10 @@ class MultipackBatchSampler(BatchSampler):
         self.total_token_slots = 0
 
         # The number of times to calculate batches to determine minimum packed dataset length
-        self.num_count_samples = num_count_samples
+        world_size = int(os.environ.get("WORLD_SIZE", "1"))
+        self.num_count_samples = (
+            1 if world_size >= num_count_samples else num_count_samples
+        )
 
         if self.sequential and not isinstance(sampler, SequentialSampler):
             LOG.warning(

src/axolotl/utils/schemas/config.py

@@ -24,11 +24,13 @@ from axolotl.utils.schemas.datasets import (
 )
 from axolotl.utils.schemas.deprecated import DeprecatedParameters, RemappedParameters
 from axolotl.utils.schemas.enums import ChatTemplate, RingAttnFunc, RLType
+from axolotl.utils.schemas.fsdp import FSDPConfig
 from axolotl.utils.schemas.integrations import (
     CometConfig,
     GradioConfig,
     LISAConfig,
     MLFlowConfig,
+    OpenTelemetryConfig,
     RayConfig,
     WandbConfig,
 )
@@ -59,6 +61,7 @@ class AxolotlInputConfig(
     WandbConfig,
     MLFlowConfig,
     CometConfig,
+    OpenTelemetryConfig,
     LISAConfig,
     GradioConfig,
     RayConfig,
@@ -233,6 +236,7 @@ class AxolotlInputConfig(
     )
     dataset_processes: int | None = Field(
         default=None,
+        deprecated="Use `dataset_num_proc` instead. This parameter will be removed in a future version.",
         json_schema_extra={
             "description": (
                 "The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()` if not set.\n"
@@ -240,6 +244,16 @@ class AxolotlInputConfig(
             )
         },
     )
+    dataset_num_proc: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": (
+                "The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()` if not set.\n"
+                "For Runpod VMs, it will default to number of vCPUs via RUNPOD_CPU_COUNT."
+            )
+        },
+    )
+
     dataset_exact_deduplication: bool | None = Field(
         default=None,
         json_schema_extra={
@@ -667,8 +681,7 @@ class AxolotlInputConfig(
         json_schema_extra={"description": "FSDP configuration"},
         deprecated="Configuring FSDP using `fsdp` is deprecated. Please use `fsdp_config` instead. ",
     )
-    # TODO @SalmanMohammadi strongly type this as its own schema
-    fsdp_config: dict[str, Any] | None = Field(
+    fsdp_config: FSDPConfig | None = Field(
         default=None, json_schema_extra={"description": "FSDP configuration options"}
     )
     fsdp_version: int | None = Field(
@@ -1314,10 +1327,22 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
 
     @model_validator(mode="before")
     @classmethod
-    def default_dataset_processes(cls, data):
-        if data.get("dataset_processes") is None:
-            data["dataset_processes"] = get_default_process_count()
-
+    def default_dataset_num_proc(cls, data):
+        if data.get("dataset_processes") is not None:
+            if data.get("dataset_num_proc") is None:
+                data["dataset_num_proc"] = data["dataset_processes"]
+                LOG.warning(
+                    "dataset_processes is deprecated and will be removed in a future version. "
+                    "Please use dataset_num_proc instead."
+                )
+            else:
+                LOG.warning(
+                    "Both dataset_processes and dataset_num_proc are set. "
+                    "Using dataset_num_proc and ignoring dataset_processes."
+                )
+            del data["dataset_processes"]
+        elif data.get("dataset_num_proc") is None:
+            data["dataset_num_proc"] = get_default_process_count()
         return data
 
     @model_validator(mode="before")

src/axolotl/utils/schemas/fsdp.py

@@ -0,0 +1,71 @@
+"""
+FSDP Configuration Schema
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class FSDPConfig(BaseModel):
+    """
+    FSDP Configuration Schema
+    """
+
+    activation_checkpointing: bool | None = Field(
+        default=None,
+        description="Enable activation checkpointing to reduce memory usage during forward passes",
+    )
+    offload_params: bool | None = Field(
+        default=None,
+        description="Offload parameters to CPU to reduce GPU memory usage",
+    )
+    sync_module_states: bool | None = Field(
+        default=None,
+        description="Synchronize module states across all processes",
+    )
+    cpu_ram_efficient_loading: bool | None = Field(
+        default=None,
+        description="Enable CPU RAM efficient loading to reduce memory usage during model loading",
+    )
+    cpu_offload_pin_memory: bool | None = Field(
+        default=None,
+        description="Disabling this enables swap memory usage for resource-constrained setups when offload_params is enabled.",
+    )
+    use_orig_params: bool | None = Field(
+        default=None,
+        description="Use original parameters instead of flattened parameters",
+    )
+
+    state_dict_type: (
+        Literal["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"] | None
+    ) = Field(
+        default=None,
+        description="Type of state dict to use for saving/loading checkpoints",
+    )
+    final_state_dict_type: (
+        Literal["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"] | None
+    ) = Field(
+        default=None,
+        description="Final state dict type to use after training completion",
+    )
+
+    auto_wrap_policy: Literal["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP"] | None = (
+        Field(
+            default=None,
+            description="Policy for automatically wrapping modules with FSDP",
+        )
+    )
+    transformer_layer_cls_to_wrap: str | None = Field(
+        default=None,
+        description="Class name of transformer layers to wrap (e.g., 'LlamaDecoderLayer')",
+    )
+
+    reshard_after_forward: bool | None = Field(
+        default=None,
+        description="Reshard parameters after forward pass to save memory",
+    )
+    mixed_precision_policy: str | None = Field(
+        default=None,
+        description="Mixed precision policy for FSDP (e.g., 'fp16', 'bf16')",
+    )

src/axolotl/utils/schemas/integrations.py

@@ -176,3 +176,27 @@ class RayConfig(BaseModel):
             "help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."
         },
     )
+
+
+class OpenTelemetryConfig(BaseModel):
+    """OpenTelemetry configuration subset"""
+
+    use_otel_metrics: bool | None = Field(
+        default=False,
+        json_schema_extra={
+            "description": "Enable OpenTelemetry metrics collection and Prometheus export"
+        },
+    )
+    otel_metrics_host: str | None = Field(
+        default="localhost",
+        json_schema_extra={
+            "title": "OpenTelemetry Metrics Host",
+            "description": "Host to bind the OpenTelemetry metrics server to",
+        },
+    )
+    otel_metrics_port: int | None = Field(
+        default=8000,
+        json_schema_extra={
+            "description": "Port for the Prometheus metrics HTTP server"
+        },
+    )

src/axolotl/utils/schemas/trl.py

@@ -167,3 +167,9 @@ class TRLConfig(BaseModel):
             "description": "Whether to exclude truncated completions from loss calculation."
         },
     )
+    vllm_enable_sleep_mode: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Enable sleep mode for vLLM to offload VRAM when idle"
+        },
+    )

src/axolotl/utils/schemas/validation.py

@@ -783,15 +783,6 @@ class OptimizationValidationMixin:
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_torch_compile_deepspeed(cls, data):
-        if data.get("deepspeed") and data.get("torch_compile"):
-            raise ValueError(
-                "torch_compile should be set within your deepspeed config file"
-            )
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_xentropy_patch_conflicts(cls, data):
@@ -816,21 +807,22 @@ class OptimizationValidationMixin:
             )
         return data
 
-    @model_validator(mode="after")
-    def check_fsdp2_base_model_quant_ram_efficient_loading(self):
-        fsdp_config = self.fsdp_config if hasattr(self, "fsdp_config") else None
-        fsdp_version = self.fsdp_version if hasattr(self, "fsdp_version") else None
-        load_in_8bit = self.load_in_8bit if hasattr(self, "load_in_8bit") else None
-        load_in_4bit = self.load_in_4bit if hasattr(self, "load_in_4bit") else None
-        if fsdp_config and fsdp_version == 2:
-            if fsdp_config.get("cpu_ram_efficient_loading") and (
-                load_in_8bit or load_in_4bit
-            ):
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp2_cpu_offload_pin_memory(cls, data):
+        if not (fsdp_config := data.get("fsdp_config")):
+            return data
+
+        if fsdp_config.get("cpu_offload_pin_memory") is False:
+            if str(data.get("fsdp_version")) != "2":
                 raise ValueError(
-                    "FSDP2 does not support load_in_8bit or load_in_4bit with cpu_ram_efficient_loading. Please do one of the following: use DeepSpeed, "
-                    "set fsdp_version to 1, or disable cpu_ram_efficient_loading."
+                    "FSDP1 does not support disabling cpu_offload_pin_memory, please set `fsdp_version` to 2"
                 )
-        return self
+            if not fsdp_config.get("offload_params"):
+                raise ValueError(
+                    "disabling cpu_offload_pin_memory requires enabling offload_params"
+                )
+        return data
 
     @model_validator(mode="before")
     @classmethod
@@ -889,7 +881,7 @@ class OptimizationValidationMixin:
             and self.fsdp_config
             and self.optimizer
             and "8bit" in self.optimizer.value
-            and self.fsdp_config["offload_params"]
+            and self.fsdp_config.offload_params
             and str(self.fsdp_version) != "2"
         ):
             raise ValueError(

src/axolotl/utils/tee.py

@@ -109,8 +109,8 @@ def prepare_debug_log(cfg, filename: str = "debug.log") -> str:
             cfg.get("resume_from_checkpoint") or cfg.get("auto_resume_from_checkpoints")
         )
 
-        if not append and log_path.exists():
-            log_path.unlink()
+        if not append:
+            log_path.unlink(missing_ok=True)
 
         fh = open(log_path, "a", encoding="utf-8")
         fh.flush()

src/axolotl/utils/trainer.py

@@ -6,6 +6,7 @@ import os
 import random
 from contextlib import contextmanager
 from functools import partial
+from tempfile import NamedTemporaryFile
 from typing import List, Optional
 
 import numpy as np
@@ -15,6 +16,7 @@ from datasets import IterableDataset, disable_caching, enable_caching
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
+from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import init_distributed_state, reduce_and_broadcast
 from axolotl.utils.environment import check_cuda_p2p_ib_support
 from axolotl.utils.logging import get_logger
@@ -276,7 +278,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         prior_len = None
     filter_map_kwargs = {}
     if not isinstance(train_dataset, IterableDataset):
-        filter_map_kwargs["num_proc"] = cfg.dataset_processes
+        filter_map_kwargs["num_proc"] = cfg.dataset_num_proc
         filter_map_kwargs["load_from_cache_file"] = not cfg.is_preprocess
 
     drop_long_kwargs = {}
@@ -316,7 +318,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     if cfg.group_by_length:
         train_dataset = train_dataset.map(
             add_length,
-            num_proc=cfg.dataset_processes,
+            num_proc=cfg.dataset_num_proc,
             load_from_cache_file=not cfg.is_preprocess,
             desc="Group By Length",
         )
@@ -333,7 +335,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         )
         train_dataset = train_dataset.map(
             pose_fn,
-            num_proc=cfg.dataset_processes,
+            num_proc=cfg.dataset_num_proc,
             load_from_cache_file=not cfg.is_preprocess,
             desc="Add position_id column (PoSE)",
         )
@@ -342,7 +344,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             if eval_dataset:
                 eval_dataset = eval_dataset.map(
                     pose_fn,
-                    num_proc=cfg.dataset_processes,
+                    num_proc=cfg.dataset_num_proc,
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
@@ -467,7 +469,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 bin_size=cfg.sample_packing_bin_size,
                 sequential=cfg.sample_packing_sequentially,
                 drop_last=True,
-                num_processes=cfg.dataset_processes,
+                num_processes=cfg.dataset_prcoesses,
                 mp_start_method=cfg.sample_packing_mp_start_method or "fork",
             )
 
@@ -540,6 +542,13 @@ def setup_deepspeed_env(cfg, stage=None):
         )
 
     os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
+    if isinstance(cfg.deepspeed, DictDefault):
+        with NamedTemporaryFile(
+            mode="w", delete=False, suffix=".json", prefix="deepspeed_config_"
+        ) as temp_file:
+            temp_file.write(json.dumps(cfg.deepspeed.to_dict(), indent=4))
+            temp_file.close()
+            cfg.deepspeed = str(temp_file.name)
     os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed
     os.environ["ACCELERATE_GRADIENT_ACCUMULATION_STEPS"] = str(
         cfg.gradient_accumulation_steps
@@ -562,6 +571,7 @@ def setup_deepspeed_env(cfg, stage=None):
     if (
         int(os.environ.get("WORLD_SIZE", "1")) == 1
         and os.environ.get("AXOLOTL_IS_PREPROCESS", "0") != "1"
+        and cfg.use_ray is not True
     ):
         os.environ["WORLD_SIZE"] = "1"  # force it in case not set
         os.environ["LOCAL_RANK"] = "0"  # force it in case not set
@@ -595,6 +605,10 @@ def setup_fsdp_envs(cfg):
         os.environ["FSDP_USE_ORIG_PARAMS"] = "true"
     if cfg.fsdp_config.state_dict_type:
         os.environ["FSDP_STATE_DICT_TYPE"] = cfg.fsdp_config.state_dict_type
+    if cfg.fsdp_config.cpu_offload_pin_memory is not None:
+        os.environ["FSDP_CPU_OFFLOAD_PIN_MEMORY"] = str(
+            cfg.fsdp_config.cpu_offload_pin_memory
+        ).lower()
     if cfg.fsdp_config.auto_wrap_policy:
         os.environ["FSDP_AUTO_WRAP_POLICY"] = cfg.fsdp_config.auto_wrap_policy
     if cfg.fsdp_config.transformer_layer_cls_to_wrap:
@@ -627,6 +641,7 @@ def setup_parallelism_envs(cfg):
 def prepare_optim_env(cfg):
     if not check_cuda_p2p_ib_support():
         if os.getenv("NCCL_P2P_DISABLE") is None:
+            LOG.warning("P2P support not detected, setting `NCCL_P2P_DISABLE=1`")
             os.environ["NCCL_P2P_DISABLE"] = "1"
     # TODO @SalmanMohammadi remove the cfg.fsdp check in 0.12
     if cfg.fsdp or cfg.fsdp_config:
@@ -634,11 +649,15 @@ def prepare_optim_env(cfg):
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
         stage = None
+        deepspeed_config = None
         # check if the cfg.deepspeed is a file
-        if os.path.isfile(cfg.deepspeed):
+        if isinstance(cfg.deepspeed, DictDefault):
+            deepspeed_config = cfg.deepspeed
+        elif os.path.isfile(cfg.deepspeed):
             # parse with json
             with open(cfg.deepspeed, "r", encoding="utf-8") as fin:
                 deepspeed_config = json.load(fin)
+        if deepspeed_config:
             stage = deepspeed_config.get("zero_optimization", {}).get("stage", None)
         setup_deepspeed_env(cfg, stage=stage)
 

src/setuptools_axolotl_dynamic_dependencies.py

@@ -33,7 +33,6 @@ def parse_requirements():
     try:
         xformers_version = [req for req in _install_requires if "xformers" in req][0]
         torchao_version = [req for req in _install_requires if "torchao" in req][0]
-        autoawq_version = [req for req in _install_requires if "autoawq" in req][0]
 
         if "Darwin" in platform.system():
             # don't install xformers on MacOS
@@ -63,7 +62,6 @@ def parse_requirements():
                     _install_requires.append("xformers==0.0.28.post2")
                 else:
                     _install_requires.append("xformers==0.0.28.post3")
-                _install_requires.pop(_install_requires.index(autoawq_version))
             elif (major, minor) >= (2, 4):
                 if patch == 0:
                     _install_requires.pop(_install_requires.index(xformers_version))

tests/core/test_builders.py

@@ -440,7 +440,7 @@ def rand_reward_func(prompts, completions) -> list[float]:
             ]
         else:
             raise ValueError(f"Unhandled cfg_string: {cfg_string}")
-        cfg["dataset_processes"] = 4
+        cfg["dataset_num_proc"] = 4
 
         if cfg_string == "grpo_cfg":
             rewards_dir = tmp_path / "rewards_test"

tests/e2e/integrations/test_kd.py

@@ -104,7 +104,6 @@ class TestKnowledgeDistillation:
             temp_dir + "/runs", "train/loss", 1.4, "Train Loss (%s) is too high"
         )
 
-    @pytest.mark.skip(reason="Chunked KD loss doesn't support PEFT/LoRA")
     @pytest.mark.parametrize(
         "load_in_8bit",
         [True, False],
@@ -120,6 +119,10 @@ class TestKnowledgeDistillation:
                 "lora_r": 16,
                 "lora_alpha": 32,
                 "lora_dropout": 0.0,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "lora_mlp_kernel": False,
+                "lora_qkv_kernel": False,
+                "lora_o_kernel": False,
             }
             | kd_min_cfg
         )

tests/e2e/multigpu/test_llama.py

@@ -353,7 +353,6 @@ class TestMultiGPULlama:
                     "auto_wrap",
                 ],
                 "fsdp_config": {
-                    "fsdp_limit_all_gathers": True,
                     "fsdp_offload_params": False,
                     "fsdp_sync_module_states": True,
                     "fsdp_use_orig_params": False,
@@ -431,7 +430,6 @@ class TestMultiGPULlama:
                     "auto_wrap",
                 ],
                 "fsdp_config": {
-                    "fsdp_limit_all_gathers": True,
                     "fsdp_offload_params": False,
                     "fsdp_sync_module_states": True,
                     "fsdp_use_orig_params": False,
@@ -594,7 +592,6 @@ class TestMultiGPULlama:
                     "auto_wrap",
                 ],
                 "fsdp_config": {
-                    "fsdp_limit_all_gathers": True,
                     "fsdp_offload_params": False,
                     "fsdp_sync_module_states": True,
                     "fsdp_use_orig_params": False,

tests/e2e/multigpu/test_ray.py

@@ -13,7 +13,6 @@ from axolotl.utils.dict import DictDefault
 from tests.e2e.utils import (
     check_tensorboard,
     require_torch_2_7_0,
-    require_torch_lt_2_6_0,
 )
 
 AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
@@ -24,7 +23,7 @@ class TestMultiGPURay:
     Test cases for AnyScale Ray post training
     """
 
-    @require_torch_lt_2_6_0
+    @require_torch_2_7_0
     def test_lora_ddp(self, temp_dir):
         cfg = DictDefault(
             {
@@ -83,7 +82,7 @@ class TestMultiGPURay:
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
         )
 
-    @require_torch_lt_2_6_0
+    @require_torch_2_7_0
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
         [1, 2],

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -160,7 +160,7 @@ def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
         "trl-internal-testing/tiny-Gemma2ForCausalLM",
-        torch_dtype=torch.float16,
+        dtype=torch.float16,
         device_map="cuda:0",
     )
     peft_config = get_peft_config(

tests/e2e/patched/test_activation_checkpointing.py

@@ -69,7 +69,7 @@ class TestActivationCheckpointing:
                 "save_safetensors": True,
                 "gradient_checkpointing": gradient_checkpointing,
                 "save_first_step": False,
-                "dataset_processes": 4,
+                "dataset_num_proc": 4,
             }
         )
 

tests/e2e/test_llama_pretrain.py

@@ -29,7 +29,7 @@ class TestPretrainLlama:
                 "sequence_len": 1024,
                 "sample_packing": sample_packing,
                 "pretrain_multipack_attn": pretrain_multipack_attn,
-                "dataset_processes": 1,
+                "dataset_num_proc": 1,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },

tests/e2e/test_quantization.py

@@ -39,7 +39,7 @@ def model():
     dummy_model = AutoModelForCausalLM.from_pretrained(
         "Qwen/Qwen2-0.5B",
         device_map="auto",
-        torch_dtype=torch.bfloat16,
+        dtype=torch.bfloat16,
     )
     with torch.device(dummy_model.device):
         dummy_model.model.embed_tokens = torch.nn.Embedding(

tests/monkeypatch/test_trainer_context_parallel_patch.py

@@ -0,0 +1,66 @@
+"""Tests for the HF Trainer context parallel patch."""
+
+import pytest
+from transformers import Trainer
+
+from axolotl.monkeypatch.transformers.trainer_context_parallel import (
+    GUARD_PATTERN,
+    PATCHED_GUARD,
+    patch_prepare_context_parallel_inputs,
+)
+
+
+@pytest.fixture
+def restore_trainer_prepare_method():
+    """Ensure Trainer._prepare_context_parallel_inputs is restored after a test."""
+    original_method = getattr(
+        Trainer,
+        "_original_prepare_context_parallel_inputs",
+        Trainer._prepare_context_parallel_inputs,
+    )
+    patched_attr_present = hasattr(
+        Trainer, "_axolotl_prepare_context_parallel_inputs_patched"
+    )
+
+    yield
+
+    Trainer._prepare_context_parallel_inputs = original_method
+    if patched_attr_present:
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched")
+    if hasattr(Trainer, "_original_prepare_context_parallel_inputs"):
+        delattr(Trainer, "_original_prepare_context_parallel_inputs")
+    if hasattr(Trainer, "_axolotl_prepare_context_parallel_inputs_source"):
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_source")
+
+
+def test_patch_attention_guard(restore_trainer_prepare_method):
+    """Patch should swap the guard to allow sdpa or flash attention."""
+    # Ensure we start from the unpatched method
+    if hasattr(Trainer, "_original_prepare_context_parallel_inputs"):
+        Trainer._prepare_context_parallel_inputs = (
+            Trainer._original_prepare_context_parallel_inputs
+        )
+        delattr(Trainer, "_original_prepare_context_parallel_inputs")
+    if hasattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched"):
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched")
+
+    patch_prepare_context_parallel_inputs()
+
+    patched_method = Trainer._prepare_context_parallel_inputs
+    assert patched_method is not None
+    assert getattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched", False)
+
+    source = Trainer._axolotl_prepare_context_parallel_inputs_source
+    assert GUARD_PATTERN not in source
+    assert PATCHED_GUARD in source
+
+
+def test_patch_is_idempotent(restore_trainer_prepare_method):
+    """Calling the patch twice should leave the same patched function in place."""
+    patch_prepare_context_parallel_inputs()
+    first_patched = Trainer._prepare_context_parallel_inputs
+
+    patch_prepare_context_parallel_inputs()
+    second_patched = Trainer._prepare_context_parallel_inputs
+
+    assert first_patched is second_patched

tests/prompt_strategies/conftest.py

@@ -177,6 +177,15 @@ def fixture_devstral_1_1_tokenizer():
     return tokenizer
 
 
+@pytest.fixture(name="qwen3_tokenizer")
+def qwen3_tokenizer_fixture(
+    download_qwen3_half_billion_model,
+):  # pylint: disable=unused-argument,redefined-outer-name
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+
+    return tokenizer
+
+
 @pytest.fixture(name="mistralv03_tokenizer_chat_template_jinja")
 def fixture_mistralv03_chat_template_jinja_w_system() -> str:
     return '{%- if messages[0]["role"] == "system" %}\n    {%- set system_message = messages[0]["content"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message["role"] == "user") != (ns.index % 2 == 0) %}\n            {{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message["role"] == "user" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- "[AVAILABLE_TOOLS] [" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- \'{"type": "function", "function": {\' }}\n                {%- for key, val in tool.items() if key != "return" %}\n                    {%- if val is string %}\n                        {{- \'"\' + key + \'": "\' + val + \'"\' }}\n                    {%- else %}\n                        {{- \'"\' + key + \'": \' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- ", " }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- "}}" }}\n                {%- if not loop.last %}\n                    {{- ", " }}\n                {%- else %}\n                    {{- "]" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- "[/AVAILABLE_TOOLS]" }}\n            {%- endif %}\n        {%- if loop.first and system_message is defined %}\n            {{- "[INST] " + system_message + "\\n\\n" + message["content"] + "[/INST]" }}\n        {%- else %}\n            {{- "[INST] " + message["content"] + "[/INST]" }}\n        {%- endif %}\n    {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n        {{- "[TOOL_CALLS] [" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}\n            {%- endif %}\n            {{- \', "id": "\' + tool_call.id + \'"}\' }}\n            {%- if not loop.last %}\n                {{- ", " }}\n            {%- else %}\n                {{- "]" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message["role"] == "assistant" %}\n        {{- " " + message["content"]|trim + eos_token}}\n    {%- elif message["role"] == "tool_results" or message["role"] == "tool" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- \'[TOOL_RESULTS] {"content": \' + content|string + ", " }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}\n        {%- endif %}\n        {{- \'"call_id": "\' + message.tool_call_id + \'"}[/TOOL_RESULTS]\' }}\n    {%- else %}\n        {{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}\n    {%- endif %}\n{%- endfor %}\n'

tests/prompt_strategies/test_chat_template_ds_schema_unification.py

@@ -6,7 +6,6 @@ import json
 
 import pytest
 from datasets import Dataset
-from transformers import AutoTokenizer
 
 from axolotl.prompt_strategies.chat_template import StrategyLoader
 from axolotl.utils.dict import DictDefault
@@ -23,15 +22,6 @@ def fixture_messages_w_tools():
     return Dataset.from_list(rows)
 
 
-@pytest.fixture(name="qwen3_tokenizer")
-def qwen3_tokenizer_fixture(
-    download_qwen3_half_billion_model,
-):
-    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
-
-    return tokenizer
-
-
 @pytest.fixture(name="qwen3_prompt_strategy")
 def qwen3_chat_template_strategy(qwen3_tokenizer):
     cfg = DictDefault(