avoid crash/oom on train end

.github/workflows/base.yml

@@ -47,18 +47,11 @@ jobs:
             pytorch: 2.7.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
           - cuda: "128"
-            cuda_version: 12.8.1
+            cuda_version: 12.6.3
             cudnn_version: ""
             python_version: "3.11"
             pytorch: 2.7.0
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "126"
-            cuda_version: 12.6.3
-            cudnn_version: ""
-            python_version: "3.11"
-            pytorch: 2.6.0
-            suffix: "-hopper"
-            torch_cuda_arch_list: "9.0+PTX"
           - cuda: "128"
             cuda_version: 12.8.1
             cudnn_version: ""
@@ -94,7 +87,7 @@ jobs:
           context: .
           file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}${{ matrix.suffix || '' }}
+          tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
           labels: ${{ steps.metadata.outputs.labels }}
           build-args: |
             CUDA_VERSION=${{ matrix.cuda_version }}

.github/workflows/main.yml

@@ -31,11 +31,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.0
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.7.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -99,11 +94,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.0
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.7.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -32,25 +32,21 @@ jobs:
             pytorch: 2.6.0
             axolotl_extras: vllm
             num_gpus: 2
-          - cuda: 126
-            cuda_version: 12.6.3
-            python_version: "3.11"
-            pytorch: 2.6.0
-            axolotl_extras:
-            suffix: "-hopper"
-            num_gpus: 2
+            nightly_build: "true"
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
             num_gpus: 2
+            nightly_build: "true"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
             pytorch: 2.7.0
             axolotl_extras:
             num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:
@@ -72,6 +68,7 @@ jobs:
           echo "AXOLOTL_EXTRAS=${{ matrix.axolotl_extras}}" >> $GITHUB_ENV
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "NIGHTLY_BUILD=${{ matrix.nightly_build }}" >> $GITHUB_ENV
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
       - name: Run tests job on Modal
         run: |

.github/workflows/tests.yml

@@ -295,7 +295,6 @@ jobs:
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
   docker-e2e-tests-1st:
-    # Run this job first as a gate for running the remainder of the test matrix
     if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' }}
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
@@ -342,8 +341,6 @@ jobs:
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 90
-    # Only run the remainder of the matrix if the first e2e check passed;
-    # this is to save on wasted compute costs for known failures that get caught in the first run
     needs: [pre-commit, pytest, docker-e2e-tests-1st]
 
     strategy:
@@ -368,12 +365,6 @@ jobs:
             pytorch: 2.7.0
             num_gpus: 1
             axolotl_extras:
-          - cuda: 128
-            cuda_version: 12.8.1
-            python_version: "3.11"
-            pytorch: 2.7.0
-            num_gpus: 1
-            axolotl_extras:
     steps:
       - name: Checkout
         uses: actions/checkout@v4

cicd/Dockerfile.jinja

@@ -32,11 +32,6 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN pip install packaging==23.2 setuptools==75.8.0
-RUN if [ "$PYTORCH_VERSION" = "2.6.0" ] && [ "$CUDA" = "126" ] ; then \
-        curl -L -O https://d1dttdx32dkk5p.cloudfront.net/fa3/cu${CUDA}/torch-${PYTORCH_VERSION}/flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-        pip3 install --no-cache-dir flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-        rm flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-    fi
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
         pip install --no-build-isolation -e .[deepspeed,flash-attn,ring-flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \

cicd/multigpu.py

@@ -70,7 +70,7 @@ def run_cmd(cmd: str, run_folder: str):
     image=cicd_image,
     gpu=GPU_CONFIG,
     timeout=90 * 60,
-    cpu=16.0,
+    cpu=8.0,
     memory=131072 * N_GPUS,
     volumes=VOLUME_CONFIG,
 )

docker/Dockerfile-base

@@ -1,5 +1,5 @@
-ARG CUDA_VERSION="12.4.1"
-ARG CUDNN_VERSION=""
+ARG CUDA_VERSION="11.8.0"
+ARG CUDNN_VERSION="8"
 ARG UBUNTU_VERSION="22.04"
 ARG MAX_JOBS=4
 
@@ -7,16 +7,16 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION A
 
 ENV PATH="/root/miniconda3/bin:${PATH}"
 
-ARG PYTHON_VERSION="3.11"
-ARG PYTORCH_VERSION="2.5.1"
-ARG CUDA="124"
+ARG PYTHON_VERSION="3.10"
+ARG PYTORCH_VERSION="2.1.2"
+ARG CUDA="118"
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 
 ENV PYTHON_VERSION=$PYTHON_VERSION
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
 
 RUN apt-get update \
-    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config curl && rm -rf /var/lib/apt/lists/* \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config && rm -rf /var/lib/apt/lists/* \
     && wget \
     https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
     && mkdir /root/.conda \
@@ -38,10 +38,6 @@ RUN git lfs install --skip-repo && \
     # The base image ships with `pydantic==1.8.2` which is not working
     pip3 install -U --no-cache-dir pydantic==1.10.10
 
-RUN if [ "$TORCH_CUDA_ARCH_LIST" = "9.0+PTX" ] ; then \
-        curl -L -O https://d1dttdx32dkk5p.cloudfront.net/fa3/cu${CUDA}/torch-${PYTORCH_VERSION}/flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-        pip3 install --no-cache-dir flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-        rm flash_attn_3-3.0.0b1-cp311-cp311-linux_x86_64.whl; \
-    elif [ "$PYTORCH_VERSION" = "2.7.0" ] ; then \
+RUN if [ "$PYTORCH_VERSION" = "2.7.0" ] ; then \
         pip3 install flash-attn==2.7.4.post1; \
     fi

docs/config.qmd

@@ -633,9 +633,7 @@ weight_decay:
 # adamw hyperparams
 adam_beta1:
 adam_beta2:
-adam_beta3:  # only used for CAME Optimizer
 adam_epsilon:
-adam_epsilon2:  # only used for CAME Optimizer
 # Gradient clipping max norm
 max_grad_norm:
 

docs/getting-started.qmd

@@ -104,7 +104,7 @@ the `alpaca` dataset format, which has the following format:
 Please see our [Dataset Formats](dataset-formats) for more dataset formats and how to
 format them.
 
-2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca`
+2. Prepare your JSONL data in the specified format (in this case, the expected `alpaca
 format):
 
 ```json
@@ -120,12 +120,6 @@ axolotl train my_training.yml
 
 ## Common Tasks {#sec-common-tasks}
 
-::: {.callout-tip}
-
-The same yaml file is used for training, inference, and merging.
-
-:::
-
 ### Testing Your Model {#sec-testing}
 
 After training, test your model:
@@ -134,16 +128,6 @@ After training, test your model:
 axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out"
 ```
 
-More details can be found in [Inference](inference.qmd).
-
-### Using a UI {#sec-ui}
-
-Launch a Gradio interface:
-
-```bash
-axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
-```
-
 ### Preprocessing Data {#sec-preprocessing}
 
 For large datasets, preprocess first:
@@ -152,22 +136,14 @@ For large datasets, preprocess first:
 axolotl preprocess my_training.yml
 ```
 
-Please make sure to set `dataset_prepared_path: ` in your config to set the path to save the prepared dataset.
+### Using a UI {#sec-ui}
 
-More details can be found in [Dataset Preprocessing](dataset_preprocessing.qmd).
-
-### Merging LoRA weights {#sec-merging-lora}
-
-To merge the LoRA weights back into the base model, run:
+Launch a Gradio interface:
 
 ```bash
-axolotl merge-lora my_training.yml --lora-model-dir="./outputs/lora-out"
+axolotl inference my_training.yml --lora-model-dir="./outputs/lora-out" --gradio
 ```
 
-The merged model will be saved in the `{output_dir}/merged` directory.
-
-More details can be found in [Merging LoRA weights](inference.qmd#sec-merging).
-
 ## Next Steps {#sec-next-steps}
 
 Now that you have the basics, you might want to:
@@ -180,7 +156,6 @@ Now that you have the basics, you might want to:
 Check our other guides for details on these topics:
 
 - [Configuration Guide](config.qmd) - Full configuration options
-- [Dataset Loading](dataset-loading.qmd) - Loading datasets from various sources
 - [Dataset Formats](dataset-formats) - Working with different data formats
 - [Multi-GPU Training](multi-gpu.qmd)
 - [Multi-Node Training](multi-node.qmd)

src/axolotl/core/trainer_builder.py

@@ -387,12 +387,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
         if self.cfg.adam_beta2:
             training_arguments_kwargs["adam_beta2"] = self.cfg.adam_beta2
-        if self.cfg.adam_beta3:
-            training_arguments_kwargs["adam_beta3"] = self.cfg.adam_beta3
         if self.cfg.adam_epsilon:
             training_arguments_kwargs["adam_epsilon"] = self.cfg.adam_epsilon
-        if self.cfg.adam_epsilon2:
-            training_arguments_kwargs["adam_epsilon2"] = self.cfg.adam_epsilon2
         if self.cfg.max_grad_norm:
             training_arguments_kwargs["max_grad_norm"] = self.cfg.max_grad_norm
 
@@ -717,7 +713,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
                 beta1 = training_arguments_kwargs.get("adam_beta1", 0.9)
                 beta2 = training_arguments_kwargs.get("adam_beta2", 0.999)
-                beta3 = training_arguments_kwargs.get("adam_beta3", 0.9999)
+                beta3 = training_arguments_kwargs.get("adam_beta2", 0.9999)
                 eps1 = training_arguments_kwargs.get("adam_epsilon", 1e-30)
                 eps2 = training_arguments_kwargs.get("adam_epsilon2", 1e-16)
                 adam_kwargs["betas"] = (beta1, beta2, beta3)
@@ -1174,8 +1170,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.eval_dataset:
             trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
-            if self.cfg.rl is not RLType.GRPO:
-                trainer_kwargs["peft_config"] = self.peft_config
+            trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
             trainer_kwargs["precompute_ref_log_probs"] = (
                 self.cfg.precompute_ref_log_probs

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

@@ -3,6 +3,7 @@
 # pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
 
 import warnings
+from contextlib import nullcontext
 from typing import Any
 
 import datasets
@@ -13,7 +14,7 @@ from accelerate.utils import (
     broadcast_object_list,
     gather,
     gather_object,
-    is_peft_available,
+    is_peft_model,
 )
 from datasets import Dataset, IterableDataset
 from torch import nn
@@ -29,13 +30,15 @@ from transformers import (
     TrainerCallback,
 )
 from transformers.trainer_utils import seed_worker
+from transformers.utils import is_peft_available
 from trl import GRPOTrainer
 from trl.data_utils import (
     apply_chat_template,
     is_conversational,
     maybe_apply_chat_template,
 )
-from trl.extras.profiling import profiling_context
+from trl.extras.profiling import profiling_context, profiling_decorator
+from trl.import_utils import is_deepspeed_available
 from trl.models import unwrap_model_for_generation
 from trl.trainer.grpo_config import GRPOConfig
 from trl.trainer.grpo_trainer import RewardFunc, nanstd
@@ -49,12 +52,62 @@ if is_peft_available():
     # pylint: disable=unused-import
     from peft import PeftConfig
 
+if is_deepspeed_available():
+    import deepspeed
+
 
 class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
     """Extend the base GRPOTrainer for axolotl helpers"""
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
+    @profiling_decorator
+    def _move_model_to_vllm(self):
+        # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
+        zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
+        gather_if_zero3 = (
+            deepspeed.zero.GatheredParameters if zero_stage_3 else nullcontext
+        )
+
+        if is_peft_model(self.model):
+            # With PEFT and DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as merging
+            # adapters in a sharded manner is not supported.
+            with gather_if_zero3(list(self.model.parameters())):
+                self.model.merge_adapter()
+
+                # Update vLLM weights while parameters are gathered
+                for name, param in self.model.named_parameters():
+                    # When using PEFT, we need to recover the original parameter name and discard some parameters
+                    name = (
+                        name.removeprefix("base_model.model.")
+                        .removeprefix("base_model.model.")
+                        .replace(".base_layer", "")
+                    )
+                    if self.model.prefix in name:
+                        continue
+                    # When module to save, remove its prefix and discard the original module
+                    if "original_module" in name:
+                        continue
+                    name = name.replace("modules_to_save.default.", "")
+
+                    if self.accelerator.is_main_process:
+                        self.vllm_client.update_named_param(name, param.data)
+
+                # Unmerge adapters while parameters are still gathered
+                self.model.unmerge_adapter()
+                # Parameters will automatically be repartitioned when exiting the context
+        else:
+            # For non-PEFT models, simply gather and update each parameter individually.
+            for name, param in self.model.named_parameters():
+                with gather_if_zero3([param]):
+                    if self.accelerator.is_main_process:
+                        self.vllm_client.update_named_param(name, param.data)
+
+        # Reset cache on main process
+        if self.accelerator.is_main_process:
+            self.vllm_client.reset_prefix_cache()
+
 
 class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     """Extend the base GRPOTrainer for sequence parallelism handling"""

src/axolotl/core/training_args.py

@@ -227,19 +227,6 @@ class AxolotlTrainingMixins:
         },
     )
 
-    adam_beta3: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "The beta3 hyperparameter used in some optimizers such as CAME"
-        },
-    )
-    adam_epsilon2: Optional[float] = field(
-        default=None,
-        metadata={
-            "help": "The epsilon2 hyperparameter used in some optimizers such as CAME"
-        },
-    )
-
     # multi-modal section
 
     image_size: int | tuple[int, int] | None = field(

src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py

@@ -20,15 +20,25 @@ from cut_cross_entropy.transformers.utils import (
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.cohere.modeling_cohere import (
+    _CONFIG_FOR_DOC,
+    COHERE_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from transformers.utils.deprecation import deprecate_kwarg
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py

@@ -17,15 +17,25 @@ from cut_cross_entropy.transformers.utils import (
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.gemma.modeling_gemma import (
+    _CONFIG_FOR_DOC,
+    GEMMA_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from transformers.utils.deprecation import deprecate_kwarg
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py

@@ -20,11 +20,15 @@ from torch import nn
 from transformers.cache_utils import Cache, HybridCache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.gemma3.modeling_gemma3 import (
+    _CONFIG_FOR_DOC,
+    GEMMA3_INPUTS_DOCSTRING,
     Gemma3CausalLMOutputWithPast,
     logger,
 )
 from transformers.utils import (
+    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
+    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
@@ -34,6 +38,10 @@ _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,
@@ -162,6 +170,10 @@ def cce_forward(
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward_multimodal(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama.py

@@ -19,9 +19,15 @@ from transformers.modeling_outputs import (
     CausalLMOutputWithPast,
 )
 from transformers.models.llama.modeling_llama import (
+    _CONFIG_FOR_DOC,
+    LLAMA_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -30,6 +36,10 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama4.py

@@ -16,12 +16,22 @@ from torch import nn
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.llama4.modeling_llama4 import (
+    _CONFIG_FOR_DOC,
+    LLAMA4_INPUTS_DOCSTRING,
     Llama4CausalLMOutputWithPast,
 )
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
+@add_start_docstrings_to_model_forward(LLAMA4_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,
@@ -150,6 +160,9 @@ def cce_forward(
     )
 
 
+@replace_return_docstrings(
+    output_type=Llama4CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward_multimodal(
     self,
     input_ids: torch.LongTensor | None = None,  # type: ignore

src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py

@@ -19,11 +19,15 @@ from transformers.models.mistral3.modeling_mistral3 import (
     Mistral3CausalLMOutputWithPast,
 )
 from transformers.models.mistral.modeling_mistral import (
+    _CONFIG_FOR_DOC,
+    MISTRAL_INPUTS_DOCSTRING,
     KwargsForCausalLM,
 )
 from transformers.processing_utils import Unpack
 from transformers.utils import (
+    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
+    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
@@ -31,6 +35,10 @@ _PATCH_OPTS: PatchOptions | None = None
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward(
     self,
     input_ids: torch.LongTensor | None = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_moe.py

@@ -13,10 +13,16 @@ from cut_cross_entropy.transformers.utils import (
     apply_lce,
 )
 from transformers.models.qwen2_moe.modeling_qwen2_moe import (
+    _CONFIG_FOR_DOC,
+    QWEN2MOE_INPUTS_DOCSTRING,
     MoeCausalLMOutputWithPast,
     MoeModelOutputWithPast,
     load_balancing_loss_func,
 )
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -25,6 +31,10 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen2_vl.py

@@ -14,12 +14,22 @@ from cut_cross_entropy.transformers.utils import (
 )
 from torch.nn import CrossEntropyLoss
 from transformers.models.qwen2_vl.modeling_qwen2_vl import (
+    _CONFIG_FOR_DOC,
+    QWEN2_VL_INPUTS_DOCSTRING,
     Qwen2VLCausalLMOutputWithPast,
 )
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 
 _PATCH_OPTS: PatchOptions | None = None
 
 
+@add_start_docstrings_to_model_forward(QWEN2_VL_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Qwen2VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def cce_forward_multimodal(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/cut_cross_entropy/monkeypatch/qwen3_moe.py

@@ -12,13 +12,20 @@ from cut_cross_entropy.transformers.utils import (
     TransformersModelT,
     apply_lce,
 )
+from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.qwen3_moe.modeling_qwen3_moe import (
+    _CONFIG_FOR_DOC,
+    QWEN3_MOE_INPUTS_DOCSTRING,
     KwargsForCausalLM,
     MoeCausalLMOutputWithPast,
     MoeModelOutputWithPast,
     load_balancing_loss_func,
 )
 from transformers.processing_utils import Unpack
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from transformers.utils.deprecation import deprecate_kwarg
 from transformers.utils.generic import can_return_tuple
 
@@ -27,6 +34,10 @@ _PATCH_OPTS: PatchOptions | None = None
 
 @can_return_tuple
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,

src/axolotl/integrations/liger/models/deepseekv2.py

@@ -14,6 +14,10 @@ from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 
+# @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
+# @replace_return_docstrings(
+#    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+# )
 def lce_forward(
     self,
     input_ids: torch.LongTensor = None,

src/axolotl/integrations/liger/models/jamba.py

@@ -13,11 +13,21 @@ from liger_kernel.transformers.fused_linear_cross_entropy import (
 from torch.nn import CrossEntropyLoss
 from transformers.modeling_outputs import MoeCausalLMOutputWithPast
 from transformers.models.jamba.modeling_jamba import (
+    _CONFIG_FOR_DOC,
+    JAMBA_INPUTS_DOCSTRING,
     HybridMambaAttentionDynamicCache,
     load_balancing_loss_func,
 )
+from transformers.utils import (
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 
 
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def lce_forward(
     self,
     input_ids: torch.LongTensor = None,

src/axolotl/monkeypatch/gemma3.py

@@ -7,16 +7,24 @@ from typing import Optional, Tuple, Union
 import torch
 from transformers.cache_utils import Cache
 from transformers.models.gemma3.modeling_gemma3 import (
+    _CONFIG_FOR_DOC,
+    GEMMA3_INPUTS_DOCSTRING,
     Gemma3CausalLMOutputWithPast,
     logger,
 )
 from transformers.utils import (
+    add_start_docstrings_to_model_forward,
     is_torchdynamo_compiling,
+    replace_return_docstrings,
 )
 from transformers.utils.deprecation import deprecate_kwarg
 
 
 @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
+@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
 def new_forward(
     self,
     input_ids: torch.LongTensor = None,

src/axolotl/train.py

@@ -289,16 +289,18 @@ def save_trained_model(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
-    elif cfg.local_rank == 0:
-        if cfg.flash_optimum and BetterTransformer:
-            model = BetterTransformer.reverse(model)
+    else:
+        if 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
-            )
+            if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
+                trainer.model.save_pretrained(
+                    cfg.output_dir, safe_serialization=safe_serialization
+                )
 
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+        trainer.accelerator.wait_for_everyone()
 
     if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
         # TODO: add integration support so this can be implemented completely within the plugin

src/axolotl/utils/callbacks/mlflow_.py

@@ -1,7 +1,6 @@
 """MLFlow module for trainer callbacks"""
 
 import logging
-import os
 from shutil import copyfile
 from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING
@@ -17,11 +16,6 @@ if TYPE_CHECKING:
 LOG = logging.getLogger("axolotl.callbacks")
 
 
-def should_log_artifacts() -> bool:
-    truths = ["TRUE", "1", "YES"]
-    return os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in truths
-
-
 class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
     # pylint: disable=duplicate-code
     """Callback to save axolotl config to mlflow"""
@@ -38,18 +32,13 @@ class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
     ):
         if is_main_process():
             try:
-                if should_log_artifacts():
-                    with NamedTemporaryFile(
-                        mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
-                    ) as temp_file:
-                        copyfile(self.axolotl_config_path, temp_file.name)
-                        mlflow.log_artifact(temp_file.name, artifact_path="")
-                        LOG.info(
-                            "The Axolotl config has been saved to the MLflow artifacts."
-                        )
-                else:
+                with NamedTemporaryFile(
+                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+                ) as temp_file:
+                    copyfile(self.axolotl_config_path, temp_file.name)
+                    mlflow.log_artifact(temp_file.name, artifact_path="")
                     LOG.info(
-                        "Skipping logging artifacts to MLflow (hf_mlflow_log_artifacts is false)"
+                        "The Axolotl config has been saved to the MLflow artifacts."
                     )
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to MLflow: {err}")

src/axolotl/utils/data/rl.py

@@ -72,7 +72,6 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
     data_set = data_set.map(
         ds_transform_fn,
         desc="Mapping RL Dataset",
-        num_proc=cfg.dataset_processes,
         **map_kwargs,
     )
 

src/axolotl/utils/data/sft.py

@@ -484,7 +484,7 @@ def get_dataset_wrapper(
     }
 
     LOG.info(
-        f"Loading dataset: {config_dataset['path']} with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
+        f"Loading dataset with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
     )
 
     if (

src/axolotl/utils/models.py

@@ -629,49 +629,6 @@ class ModelLoader:
             )
 
         if self.cfg.flash_attention:
-            use_fa3 = False
-            if self.cfg.use_flash_attention_3 is True:
-                use_fa3 = True
-            elif self.cfg.use_flash_attention_3 == "auto":
-                if torch.cuda.get_device_capability() >= (9, 0):
-                    # FA3 is only available on Hopper GPUs and newer
-                    use_fa3 = True
-                if not importlib.util.find_spec("flash_attn_interface"):
-                    use_fa3 = False
-            if use_fa3 and not importlib.util.find_spec("flash_attn_interface"):
-                # this can happen when use_flash_attention_3 is explicity set to True
-                # and flash_attn_interface is not installed
-                raise ModuleNotFoundError(
-                    "Please install the flash_attn_interface library to use Flash Attention 3.x"
-                )
-            if use_fa3 and importlib.util.find_spec("flash_attn_interface") is not None:
-                from flash_attn_interface import flash_attn_func as flash_attn_func_v3
-                from flash_attn_interface import (
-                    flash_attn_varlen_func as flash_attn_varlen_func_v3,
-                )
-
-                def flash_attn_func_v3_wrapper(*args, **kwargs):
-                    kwargs.pop("dropout_p", None)
-                    if "softmax_scale" in kwargs and len(args) >= 4:
-                        # if softmax_scale is provided, then the 3rd position is dropout_p that we need to drop
-                        args = (*args[:3],) + args[4:]
-                    return flash_attn_func_v3(*args, **kwargs)[0]
-
-                def flash_attn_varlen_func_v3_wrapper(*args, **kwargs):
-                    kwargs.pop("dropout_p", None)
-                    if "softmax_scale" in kwargs and len(args) >= 4:
-                        # if softmax_scale is provided, then the 3rd position is dropout_p that we need to drop
-                        args = (*args[:3],) + args[4:]
-                    return flash_attn_varlen_func_v3(*args, **kwargs)[0]
-
-                transformers.modeling_flash_attention_utils.flash_attn_func = (
-                    flash_attn_func_v3_wrapper
-                )
-                transformers.modeling_flash_attention_utils.flash_attn_varlen_func = (
-                    flash_attn_varlen_func_v3_wrapper
-                )
-                LOG.info("Switched to Flash Attention v3")
-
             self.patch_attention()
 
         if self.cfg.sample_packing and self.cfg.s2_attention:
@@ -742,7 +699,6 @@ class ModelLoader:
 
                 patch_mllama()
 
-            # TODO deprecate soon
             if self.model_config.model_type == "btlm":
                 from axolotl.monkeypatch.btlm_attn_hijack_flash import (
                     replace_btlm_attn_with_flash_attn,
@@ -750,7 +706,6 @@ class ModelLoader:
 
                 replace_btlm_attn_with_flash_attn(self.cfg.base_model)
 
-            # TODO deprecate soon
             if (
                 self.model_config.model_type == "stablelm_epoch"
                 and self.cfg.sample_packing

src/axolotl/utils/schemas/config.py

@@ -233,7 +233,6 @@ class AxolotlInputConfig(
     flash_attn_fuse_qkv: bool | None = None
     flash_attn_fuse_mlp: bool | None = None
     flash_optimum: bool | None = None
-    use_flash_attention_3: Literal["auto"] | bool | None = None
 
     eager_attention: bool | None = None
 

tests/conftest.py

@@ -421,7 +421,6 @@ def temp_dir():
 
 @pytest.fixture(scope="function", autouse=True)
 def cleanup_monkeypatches():
-    import transformers.modeling_flash_attention_utils
     from transformers import Trainer
     from transformers.models.llama.modeling_llama import (  # LlamaFlashAttention2,
         LlamaAttention,
@@ -435,19 +434,6 @@ def cleanup_monkeypatches():
         Trainer._inner_training_loop  # pylint: disable=protected-access
     )
     original_trainer_training_step = Trainer.training_step
-    original_fa_func = None
-    original_fa_varlen_func = None
-    if (
-        importlib.util.find_spec("flash_attn")
-        and hasattr(transformers.modeling_flash_attention_utils, "flash_attn_func")
-        and hasattr(
-            transformers.modeling_flash_attention_utils, "flash_attn_varlen_func"
-        )
-    ):
-        original_fa_func = transformers.modeling_flash_attention_utils.flash_attn_func
-        original_fa_varlen_func = (
-            transformers.modeling_flash_attention_utils.flash_attn_varlen_func
-        )
     # monkey patches can happen inside the tests
     yield
     # Reset LlamaFlashAttention2 forward
@@ -458,11 +444,6 @@ def cleanup_monkeypatches():
         original_trainer_inner_training_loop
     )
     Trainer.training_step = original_trainer_training_step
-    if original_fa_func:
-        transformers.modeling_flash_attention_utils.flash_attn_func = original_fa_func
-        transformers.modeling_flash_attention_utils.flash_attn_varlen_func = (
-            original_fa_varlen_func
-        )
 
     # Reset other known monkeypatches
     modules_to_reset: list[tuple[str, list[str]]] = [
@@ -477,7 +458,6 @@ def cleanup_monkeypatches():
         ("transformers.trainer",),
         ("transformers", ["Trainer"]),
         ("transformers.loss.loss_utils",),
-        ("transformers.modeling_flash_attention_utils",),
     ]
     for module_name_tuple in modules_to_reset:
         module_name = module_name_tuple[0]

tests/e2e/multigpu/solo/test_grpo.py

@@ -166,6 +166,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 """
             )
 
+    @pytest.mark.skip(reason="flaky test")
     @pytest.mark.parametrize(
         "num_gpus",
         [1, 2],
@@ -230,6 +231,8 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
+            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            # "VLLM_USE_V1": "0",
         }
         vllm_process = start_vllm(
             cfg.base_model,
@@ -263,6 +266,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
         finally:
             recursive_kill(vllm_process)
 
+    @pytest.mark.skip(reason="flaky test")
     @pytest.mark.parametrize(
         "num_gpus",
         [1, 2],
@@ -321,6 +325,8 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
+            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            # "VLLM_USE_V1": "0",
         }
         vllm_process = start_vllm(
             cfg.base_model,

tests/e2e/multigpu/test_llama.py

@@ -101,13 +101,7 @@ class TestMultiGPULlama:
         "gradient_accumulation_steps",
         [1, 2],
     )
-    @pytest.mark.parametrize(
-        "use_flash_attention_3",
-        [False, "auto"],
-    )
-    def test_lora_ddp_packed(
-        self, temp_dir, gradient_accumulation_steps, use_flash_attention_3
-    ):
+    def test_lora_ddp_packed(self, temp_dir, gradient_accumulation_steps):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -144,7 +138,6 @@ class TestMultiGPULlama:
                 "flash_attention": True,
                 "use_tensorboard": True,
                 "bf16": True,
-                "use_flash_attention_3": use_flash_attention_3,
             }
         )
 

tests/e2e/test_packing_loss.py

@@ -4,6 +4,7 @@ E2E tests for packed training
 
 import logging
 import os
+import unittest
 
 from transformers.utils import is_torch_bf16_gpu_available
 
@@ -13,17 +14,18 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import check_tensorboard
+from .utils import check_tensorboard, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestPackedLlama:
+class TestPackedLlama(unittest.TestCase):
     """
     Test case for Packed training of llama models
     """
 
+    @with_temp_dir
     def test_loss_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(