fix: correct attention class retrieval for gemma3n model in lora_kernels.py

.github/workflows/main.yml

@@ -20,11 +20,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras: vllm
+            is_latest: true
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -87,8 +88,8 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
@@ -145,8 +146,8 @@ jobs:
     strategy:
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:

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

@@ -26,11 +26,11 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
-            axolotl_extras:
+            axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
           - cuda: 124

.github/workflows/tests.yml

@@ -195,12 +195,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -247,8 +247,8 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 126
-            cuda_version: 12.6.3
+          - cuda: 124
+            cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
@@ -311,7 +311,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
     steps:
       - name: Checkout
         uses: actions/checkout@v4

README.md

@@ -59,8 +59,6 @@ Features:
 
 ### Installation
 
-#### Using pip
-
 ```bash
 pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
@@ -70,13 +68,6 @@ axolotl fetch examples
 axolotl fetch deepspeed_configs  # OPTIONAL
 ```
 
-#### Using Docker
-
-Installing with Docker can be less error prone than installing in your own environment.
-```bash
-docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
-```
-
 Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
 
 ### Your First Fine-tune

cicd/single_gpu.py

@@ -32,8 +32,6 @@ df_args = {
     "NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
     "CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
     "HF_HOME": "/workspace/data/huggingface-cache/hub",
-    "PYTHONUNBUFFERED": os.environ.get("PYTHONUNBUFFERED", "1"),
-    "DEEPSPEED_LOG_LEVEL": os.environ.get("DEEPSPEED_LOG_LEVEL", "WARNING"),
 }
 
 dockerfile_contents = df_template.render(**df_args)

docker/Dockerfile-base

@@ -37,3 +37,7 @@ RUN git lfs install --skip-repo && \
     pip3 install awscli && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
+
+RUN if [ "$PYTORCH_VERSION" = "2.7.1" ] ; then \
+        pip3 install flash-attn==2.7.4.post1; \
+    fi

docker/Dockerfile-uv-base

@@ -34,3 +34,7 @@ RUN uv pip install packaging setuptools wheel psutil \
     && 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" = "2.7.1" ] ; then \
+        uv pip install --no-build-isolation flash-attn==2.7.4.post1; \
+    fi

docs/docker.qmd

@@ -9,7 +9,7 @@ format:
 This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
 
 ::: {.callout-important}
-For Blackwell GPUs, please use the tags with PyTorch 2.7.1 and CUDA 12.8.
+For Blackwell GPUs, please use the tags with Pytorch 2.7.1 and CUDA 12.8.
 :::
 
 ## Base
@@ -34,7 +34,6 @@ Tags examples:
 
 - `main-base-py3.11-cu128-2.7.1`
 - `main-base-py3.11-cu126-2.7.1`
-- `main-base-py3.11-cu126-2.6.0`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
 
@@ -74,15 +73,13 @@ There may be some extra tags appended to the image, like `-vllm` which installs
 
 Tags examples:
 
-- `main-py3.11-cu128-2.7.1`
-- `main-py3.11-cu126-2.7.1`
-- `main-py3.11-cu126-2.6.0`
+- `main-py3.11-cu126-2.7.0`
 - `main-py3.11-cu124-2.6.0`
 - `main-py3.11-cu124-2.5.1`
 - `main-latest`
 - `main-20250303-py3.11-cu124-2.6.0`
 - `main-20250303-py3.11-cu124-2.5.1`
-- `0.10.1`
+- `0.9.2`
 
 ## Cloud
 

docs/multimodal.qmd

@@ -16,7 +16,6 @@ format:
 - [Gemma-3](#sec-gemma-3)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
-- [Phi3-V](#sec-phi3-v)
 
 ## Usage
 
@@ -127,15 +126,6 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
-### Phi3-V {#sec-phi3-v}
-
-```yaml
-base_model: microsoft/Phi-3.5-vision-instruct
-
-trust_remote_code: true
-chat_template: phi_35_vl
-```
-
 ## Dataset Format
 
 For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.46.0
+bitsandbytes==0.45.4
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
@@ -15,7 +15,7 @@ huggingface_hub==0.32.2
 peft==0.15.2
 transformers==4.52.4
 tokenizers>=0.21.1
-accelerate==1.8.1
+accelerate==1.7.0
 datasets==3.6.0
 deepspeed>=0.17.0
 trl==0.18.2
@@ -68,4 +68,4 @@ schedulefree==1.4.1
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.3
 
-mistral-common==1.6.3
+mistral-common==1.6.0

setup.py

@@ -111,9 +111,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.0.post2"],
+    "flash-attn": ["flash-attn==2.7.4.post1"],
     "ring-flash-attn": [
-        "flash-attn==2.8.0.post2",
+        "flash-attn==2.7.4.post1",
         "ring-flash-attn>=0.1.4",
         "yunchang==0.6.0",
     ],

src/axolotl/core/builders/causal.py

@@ -253,10 +253,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["eval_sample_packing"] = bool(
             self.cfg.eval_sample_packing
         )
-        if self.cfg.sample_packing_sequentially is not None:
-            training_arguments_kwargs["sample_packing_sequentially"] = (
-                self.cfg.sample_packing_sequentially
-            )
         if self.cfg.sample_packing_bin_size is not None:
             training_arguments_kwargs["sample_packing_bin_size"] = (
                 self.cfg.sample_packing_bin_size

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

@@ -28,7 +28,7 @@ class DPOStrategy:
         training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
         training_args_kwargs["max_prompt_length"] = cfg.sequence_len
-        training_args_kwargs["generate_during_eval"] = cfg.dpo_generate_during_eval
+        training_args_kwargs["generate_during_eval"] = cfg.use_wandb
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
         if cfg.dpo_padding_free is not None:

src/axolotl/loaders/constants.py

@@ -1,7 +1,6 @@
 """Shared constants for axolotl.loaders module"""
 
 from transformers import (
-    AutoModelForCausalLM,
     Gemma3ForConditionalGeneration,
     Llama4ForConditionalGeneration,
     LlavaForConditionalGeneration,
@@ -19,6 +18,4 @@ MULTIMODAL_AUTO_MODEL_MAPPING = {
     "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
     "mistral3": Mistral3ForConditionalGeneration,
     "gemma3": Gemma3ForConditionalGeneration,
-    # phi3_v modeling code is not available in transformers yet
-    "phi3_v": AutoModelForCausalLM,
 }

src/axolotl/loaders/patch_manager.py

@@ -65,7 +65,6 @@ class PatchManager:
         self._apply_mistral_cross_entropy_patch()
         self._apply_self_attention_lora_patch()
         self._apply_gemma3_conditional_generation_forward_patch()
-        self._apply_sequence_parallel_patches()
 
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
@@ -232,17 +231,6 @@ class PatchManager:
 
             patch_gemma3_conditional_generation_forward()
 
-    def _apply_sequence_parallel_patches(self):
-        """Apply sequence parallelism patches."""
-        if self.cfg.sequence_parallel_degree and self.cfg.sequence_parallel_degree > 1:
-            from axolotl.monkeypatch.ring_attn.patch import (
-                patch_prepare_data_loader,
-                patch_prepare_device_mesh,
-            )
-
-            patch_prepare_data_loader()
-            patch_prepare_device_mesh(self.cfg.sequence_parallel_degree, self.cfg.fsdp)
-
     def _patch_attention(self):
         """Apply attention-specific patches based on model type."""
         if not (self.cfg.flash_attention and hasattr(self.model_config, "model_type")):

src/axolotl/monkeypatch/lora_kernels.py

@@ -156,8 +156,12 @@ def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
         model_cls_prefix = "".join(
             [part.capitalize() for part in model_type.split("_")]
         )
-        module = __import__(module_path, fromlist=[f"{model_cls_prefix}Attention"])
-        attention_cls = getattr(module, f"{model_cls_prefix}Attention")
+        if model_type == "gemma3n":
+            module = __import__(module_path, fromlist=[f"{model_cls_prefix}TextAttention"])
+            attention_cls = getattr(module, f"{model_cls_prefix}TextAttention")
+        else:
+            module = __import__(module_path, fromlist=[f"{model_cls_prefix}Attention"])
+            attention_cls = getattr(module, f"{model_cls_prefix}Attention")
 
         return attention_cls
     except (ImportError, AttributeError) as e:

src/axolotl/monkeypatch/ring_attn/patch.py

@@ -152,7 +152,7 @@ def update_ring_attn_params(position_ids: torch.Tensor | None):
 def patch_prepare_data_loader():
     """Patch `accelerate.data_loader.prepare_data_loader` to respect the SP degree.
 
-    Raises:
+    Raies:
         RuntimeError: If source code to patch does not exist.
     """
     original_fn = accelerate.data_loader.prepare_data_loader
@@ -168,34 +168,23 @@ def patch_prepare_data_loader():
         ORIGINAL_PREPARE_DATALOADER_CODE, NEW_PREPARE_DATALOADER_CODE
     )
 
-    items_to_import = []
-    for item in dir(accelerate.data_loader):
-        if item in patched_source:
-            items_to_import.append(item)
-
     # Create a new function from the patched source
     namespace = {}
     exec(  # pylint: disable=exec-used  # nosec B102
-        f"from accelerate.data_loader import ({', '.join(items_to_import)})",
-        globals(),
+        patched_source, accelerate.data_loader.__dict__, namespace
     )
-    exec(  # pylint: disable=exec-used  # nosec B102
-        patched_source, globals(), namespace
-    )
-
     patched_function = namespace["prepare_data_loader"]
-    original_fn.__code__ = patched_function.__code__
 
+    accelerate.data_loader.prepare_data_loader = patched_function
     LOG.info("Patched accelerate.data_loader.prepare_data_loader for SP support")
 
 
-def patch_prepare_device_mesh(sequence_parallel_degree: int, fsdp: bool = False):
+def patch_prepare_device_mesh(sequence_parallel_degree: int):
     """Patches the `Accelerator._prepare_device_mesh` method to create a device mesh
     that includes sequence parallelism with the specified degree.
 
     Args:
-        sequence_parallel_degree: The degree of sequence parallelism to use.
-        fsdp: Whether to use FSDP.
+        sequence_parallel_degree (int): The degree of sequence parallelism to use.
     """
 
     def _prepare_device_mesh(self):
@@ -218,14 +207,12 @@ def patch_prepare_device_mesh(sequence_parallel_degree: int, fsdp: bool = False)
         )
         device_ids = list(range(world_size))
 
-        # NOTE: We use "cp" instead of "sp" to match the PyTorch native "context
-        # parallelism" implementation naming.
-        # NOTE: We have a simplified FSDP handling here; i.e., if FSDP is enabled, we
-        # only use "fsdp" and "cp" for the device mesh.
+        # Note that we use "cp" instead of "sp" to match the PyTorch native "context
+        # parallelism" implementation naming
         return dist.DeviceMesh(
             "cuda",
             torch.tensor(device_ids).reshape(mesh_shape),
-            mesh_dim_names=("dp", "cp") if not fsdp else ("fsdp", "cp"),
+            mesh_dim_names=("dp", "cp"),
         )
 
     # Replace the original method with our new method

src/axolotl/processing_strategies.py

@@ -264,23 +264,6 @@ class Gemma3ProcessingStrategy(ProcessingStrategy):
         return labels
 
 
-class Phi35VLProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for Phi-3.5-vision-instruct"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
-        self.image_token = "<|image|>"  # nosec
-        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-            self.image_token
-        )
-
-
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -296,10 +279,6 @@ def get_processing_strategy(
         return Gemma3ProcessingStrategy(
             processor, chat_template, image_size, image_resize_algorithm
         )
-    if chat_template_type == "phi_35_vl":
-        return Phi35VLProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
     if chat_template_type in [
         "llama3_2_vision",
         "llama4",

src/axolotl/train.py

@@ -223,9 +223,8 @@ def execute_training(
             )
 
         LOG.info("Starting trainer...")
-        # TODO: disabling for now as not compatible with FSDP2 + torchao low bit optimizers
-        # if cfg.bf16:
-        #     torch.set_default_dtype(torch.bfloat16)
+        if cfg.bf16:
+            torch.set_default_dtype(torch.bfloat16)
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
 

src/axolotl/utils/chat_templates.py

@@ -32,7 +32,6 @@ _CHAT_TEMPLATES = {
     "llava": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
     "phi_3": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
     "phi_35": "{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}",
-    "phi_35_vl": "{% set image_count = namespace(value=0) %}{% for message in messages %}{{'<|' + message['role'] + '|>\n' }}{% if message['content'] is string %}{{ message['content'] }}{% else %}{% set message_images = [] %}{% set message_text = [] %}{% for chunk in message['content'] %}{% if chunk['type'] == 'image' or 'image' in chunk or 'image_url' in chunk %}{% set image_count.value = image_count.value + 1 %}{% set _ = message_images.append('<|image_' + image_count.value|string + '|>\n') %}{% elif chunk['type'] == 'text' %}{% set _ = message_text.append(chunk['text']) %}{% endif %}{% endfor %}{{ message_images | join('') }}{{ message_text | join('') }}{% endif %}{{ '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
     "phi_4": "{% set system_message = 'You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:' -%}{%- if messages and messages[0]['role'] == 'system' -%}{%- set system_message = messages[0]['content'] -%}{%- set messages = messages[1:] -%}{%- endif -%}<|im_start|>system<|im_sep|>{{ system_message }}<|im_end|>{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>'}}{% generation %}{{message['content'] + '<|im_end|>'}}{% endgeneration %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}",
     "deepseek_v2": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<｜User｜>' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '<｜Assistant｜>' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<｜Assistant｜>' }}{% endif %}",
     "deepseek_v3": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\\n\\n' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and 'tool_calls' in message %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message['content'] is none %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{'<｜Assistant｜>' + message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and 'tool_calls' not in message %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}",

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -12,6 +12,8 @@ from transformers.utils import ModelOutput
 
 from axolotl.monkeypatch.ring_attn import (
     get_ring_attn_group,
+    patch_prepare_data_loader,
+    patch_prepare_device_mesh,
     register_ring_attn,
     update_ring_attn_params,
 )
@@ -236,6 +238,12 @@ class SequenceParallelContextManager:
             ring_attn_func=self.ring_attn_func,
         )
 
+        # Patches for accelerate functionality
+        patch_prepare_data_loader()
+        patch_prepare_device_mesh(
+            sequence_parallel_degree=self.sequence_parallel_degree
+        )
+
     def _register_model_hooks(self):
         # Forward pre-hook to apply sequence parallelism
         def sequence_parallel_pre_hook(_, args, kwargs):

src/axolotl/utils/data/shared.py

@@ -524,24 +524,13 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
         Merged dataset.
     """
     if len(datasets) == 1:
-        ds = datasets[0]
-
-        # Do not shuffle if curriculum sampling is enabled
-        if cfg.curriculum_sampling:
-            return ds
-
-        return ds.shuffle(seed=cfg.seed)
+        return datasets[0]
 
     LOG.info("Merging datasets...")
     merged_dataset = concatenate_datasets(datasets)
 
     if cfg.shuffle_merged_datasets:
         LOG.debug("Shuffling merged datasets...")
-        if cfg.curriculum_sampling:
-            LOG.warning(
-                "Shuffling merged datasets with curriculum sampling is not recommended. "
-                "This will randomize the order of samples."
-            )
         merged_dataset = merged_dataset.shuffle(seed=cfg.seed)
     else:
         LOG.debug("Not shuffling merged datasets.")

src/axolotl/utils/mistral_tokenizer.py

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Optional
 import numpy as np
 from huggingface_hub import hf_hub_download
 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
-from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy, Tekkenizer
+from mistral_common.tokens.tokenizers.tekken import Tekkenizer
 from torch import Tensor
 from transformers.utils import PaddingStrategy
 
@@ -251,13 +251,10 @@ class HFMistralTokenizer:
             token_ids = [token_ids]
 
         if skip_special_tokens:
-            return self._mistral.instruct_tokenizer.tokenizer.decode(
-                token_ids, special_token_policy=SpecialTokenPolicy.IGNORE
-            )
+            return self._mistral.instruct_tokenizer.tokenizer.decode(token_ids)
 
-        return self._mistral.instruct_tokenizer.tokenizer.decode(
-            token_ids, special_token_policy=SpecialTokenPolicy.KEEP
-        )
+        # to_string returns a string with special tokens
+        return self._mistral.instruct_tokenizer.tokenizer.to_string(token_ids)
 
     def _create_mistral_chat_completion_request(
         self, conversation: list[dict], tools: list[dict] | None = None

src/axolotl/utils/schemas/config.py

@@ -146,7 +146,6 @@ class AxolotlInputConfig(
     dpo_label_smoothing: float | None = None
     dpo_norm_loss: bool | None = None
     dpo_padding_free: bool | None = None
-    dpo_generate_during_eval: bool | None = None
 
     datasets: (
         Annotated[

src/axolotl/utils/schemas/enums.py

@@ -48,8 +48,6 @@ class ChatTemplate(str, Enum):
     llama4 = "llama4"
     phi_3 = "phi_3"
     phi_35 = "phi_35"
-    phi_35_vl = "phi_35_vl"
-    phi_4 = "phi_4"
     deepseek_v2 = "deepseek_v2"
     deepseek_v3 = "deepseek_v3"
     jamba = "jamba"

tests/conftest.py

@@ -4,14 +4,12 @@ shared pytest fixtures
 
 import functools
 import importlib
-import logging
 import os
 import shutil
 import sys
 import tempfile
 import time
-from pathlib import Path, PosixPath
-from typing import Generator
+from pathlib import Path
 
 import datasets
 import pytest
@@ -26,8 +24,6 @@ from tests.hf_offline_utils import (
     hf_offline_context,
 )
 
-logging.getLogger("filelock").setLevel(logging.CRITICAL)
-
 
 def retry_on_request_exceptions(max_retries=3, delay=1):
     # pylint: disable=duplicate-code
@@ -415,7 +411,7 @@ def tokenizer_mistral_7b_instruct_chatml(tokenizer_mistral_7b_instruct):
 
 
 @pytest.fixture
-def temp_dir() -> Generator[str, None, None]:
+def temp_dir():
     # Create a temporary directory
     _temp_dir = tempfile.mkdtemp()
     yield _temp_dir
@@ -423,11 +419,6 @@ def temp_dir() -> Generator[str, None, None]:
     shutil.rmtree(_temp_dir)
 
 
-@pytest.fixture(scope="function", autouse=True)
-def unique_triton_cache_dir(temp_dir: str | PosixPath) -> None:
-    os.environ["TRITON_CACHE_DIR"] = str(temp_dir) + "/.triton/cache"
-
-
 @pytest.fixture(scope="function", autouse=True)
 def cleanup_monkeypatches():
     from transformers import Trainer

tests/e2e/multigpu/patched/test_sp.py

@@ -54,7 +54,6 @@ class TestSequenceParallelism:
                 "micro_batch_size": micro_batch_size,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/solo/test_flex.py

@@ -54,7 +54,6 @@ class TestPackedFlex:
                 "gradient_accumulation_steps": 2,
                 "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/solo/test_grpo.py

@@ -309,7 +309,6 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "warmup_steps": 10,
                 "val_set_size": 0.0,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -401,7 +400,6 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "warmup_steps": 10,
                 "val_set_size": 0.0,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_eval.py

@@ -38,13 +38,12 @@ class TestMultiGPUEval:
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.05,
+                "val_set_size": 0.004,
                 "special_tokens": {"pad_token": "<|endoftext|>"},
                 "datasets": [
                     {
                         "path": "teknium/GPT4-LLM-Cleaned",
                         "type": "alpaca",
-                        "split": "train[:5%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -52,7 +51,6 @@ class TestMultiGPUEval:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -109,13 +107,12 @@ class TestMultiGPUEval:
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "lora_modules_to_save": ["embed_tokens", "lm_head"],
-                "val_set_size": 0.01,
+                "val_set_size": 0.0004,
                 "special_tokens": {"pad_token": "<|endoftext|>"},
                 "datasets": [
                     {
                         "path": "teknium/GPT4-LLM-Cleaned",
                         "type": "alpaca",
-                        "split": "train[:5%]",
                     },
                 ],
                 "num_epochs": 1,
@@ -123,7 +120,6 @@ class TestMultiGPUEval:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_gemma3.py

@@ -64,7 +64,6 @@ class TestMultiGPUGemma3:
                 },
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.0001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_llama.py

@@ -62,7 +62,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -128,7 +127,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -202,7 +200,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -281,7 +278,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "warmup_steps": 0,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
@@ -344,7 +340,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -417,7 +412,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -497,7 +491,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_8bit",
                 "lr_scheduler": "cosine",
@@ -580,7 +573,6 @@ class TestMultiGPULlama:
                 "gradient_accumulation_steps": 2,
                 # "gradient_checkpointing": True,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -677,7 +669,6 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -752,7 +743,6 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
@@ -827,7 +817,6 @@ class TestMultiGPULlama:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_qwen2.py

@@ -46,7 +46,6 @@ class TestMultiGPUQwen2:
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",

tests/e2e/multigpu/test_ray.py

@@ -48,7 +48,6 @@ class TestMultiGPURay:
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
@@ -108,7 +107,6 @@ class TestMultiGPURay:
                 "micro_batch_size": 1,
                 "gradient_accumulation_steps": gradient_accumulation_steps,
                 "output_dir": temp_dir,
-                "dataset_prepared_path": temp_dir + "/last_run_prepared",
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -396,7 +396,7 @@ def test_model_architecture(model_config):
 
 
 # pylint: disable=duplicate-code
-def test_kernel_training_integration(temp_dir):
+def test_kernel_training_integration():
     """Test model loading with kernel patches enabled."""
     from axolotl.cli.utils import load_model_and_tokenizer
 
@@ -426,14 +426,6 @@ def test_kernel_training_integration(temp_dir):
         }
     )
 
-    # Write cfg to yaml file
-    path = Path(temp_dir) / "config.yaml"
-    with open(path, "w", encoding="utf-8") as fout:
-        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-    # Load config
-    cfg = load_cfg(str(path))
-
     # Load model
     model, _, _ = load_model_and_tokenizer(cfg=cfg)
 
@@ -513,7 +505,7 @@ def test_kernel_training_integration_auto_enable(temp_dir):
     assert found_patched_attn
 
 
-def test_kernel_training_integration_dropout_non_zero(temp_dir):
+def test_kernel_training_integration_dropout_non_zero():
     """Test model loading with dropout non-zero should not patch."""
 
     from axolotl.cli.utils import load_model_and_tokenizer
@@ -541,14 +533,6 @@ def test_kernel_training_integration_dropout_non_zero(temp_dir):
         }
     )
 
-    # Write cfg to yaml file
-    path = Path(temp_dir) / "config.yaml"
-    with open(path, "w", encoding="utf-8") as fout:
-        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
-
-    # Load config
-    cfg = load_cfg(str(path))
-
     # Get original attention class
     attention_cls = get_attention_cls_from_config(cfg)