fix compile

* simplify logging

* remove comment

* progress on debug.log

* add debug-level logger for file log

* simplify

* case insensitivity; 3rd party logging improvements

* simplify

* fix

* tests

* lint

* nits

* nit

* Update tests/test_utils_tee.py

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

* cleanup / comments

* fix

* oops

---------

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

.bandit

@@ -1,3 +1,3 @@
 [bandit]
 exclude = tests
-skips = B101,B615
+skips = B101,B615,B102,B110

.coderabbit.yaml

@@ -12,6 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
-    auto_incremental_review: true
+    auto_incremental_review: false
 chat:
   auto_reply: true

.flake8

@@ -1,5 +0,0 @@
-[flake8]
-max-line-length = 88
-
-select = C,E,F,W,B,B950
-extend-ignore = E203, E501, W503

.github/workflows/main.yml

@@ -36,6 +36,11 @@ jobs:
             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
@@ -110,6 +115,11 @@ jobs:
             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
@@ -169,6 +179,12 @@ jobs:
             pytorch: 2.7.1
             axolotl_extras: vllm
             is_latest: true
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras:
+            is_latest:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

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

@@ -33,13 +33,6 @@ jobs:
             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"
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"
@@ -47,6 +40,13 @@ jobs:
             axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            axolotl_extras: fbgemm-gpu
+            num_gpus: 2
+            nightly_build: "true"
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     steps:

.github/workflows/tests.yml

@@ -55,7 +55,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
+        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -130,7 +130,7 @@ jobs:
       fail-fast: false
       matrix:
         python_version: ["3.11"]
-        pytorch_version: ["2.6.0", "2.7.0", "2.7.1"]
+        pytorch_version: ["2.6.0", "2.7.1", "2.8.0"]
     timeout-minutes: 20
 
     steps:
@@ -240,7 +240,7 @@ 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:
             dockerfile: "Dockerfile-uv.jinja"
@@ -298,6 +298,13 @@ jobs:
             pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
+          - cuda: 128
+            cuda_version: 12.8.1
+            python_version: "3.11"
+            pytorch: 2.8.0
+            num_gpus: 1
+            gpu_type: "B200"
+            axolotl_extras: fbgemm-gpu
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -318,6 +325,7 @@ jobs:
           echo "CUDA=${{ matrix.cuda }}" >> $GITHUB_ENV
           echo "MODAL_IMAGE_BUILDER_VERSION=2024.10" >> $GITHUB_ENV
           echo "N_GPUS=${{ matrix.num_gpus }}" >> $GITHUB_ENV
+          echo "GPU_TYPE=${{ matrix.gpu_type || 'L40S'}}" >> $GITHUB_ENV
           echo "CODECOV_TOKEN=${{ secrets.CODECOV_TOKEN }}" >> $GITHUB_ENV
           echo "E2E_DOCKERFILE=${{ matrix.dockerfile || 'Dockerfile.jinja'}}" >> $GITHUB_ENV
       - name: Run tests job on Modal
@@ -334,10 +342,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
+          - cuda: 126
+            cuda_version: 12.6.3
             python_version: "3.11"
-            pytorch: 2.6.0
+            pytorch: 2.7.1
             num_gpus: 1
             axolotl_extras:
     steps:

.gitignore

@@ -190,3 +190,6 @@ out/
 
 # vim
 *.swp
+
+# scm auto-versioning
+src/axolotl/_version.py

.isort.cfg

@@ -1,4 +0,0 @@
-[settings]
-profile=black
-known_third_party=wandb,comet_ml
-known_local_folder=src,tests

.pre-commit-config.yaml

@@ -10,22 +10,12 @@ repos:
     -   id: trailing-whitespace
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
--   repo: https://github.com/psf/black
-    rev: 25.1.0
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.12.12
     hooks:
-    -   id: black
--   repo: https://github.com/pycqa/isort
-    rev: 6.0.1
-    hooks:
-      - id: isort
--   repo: https://github.com/PyCQA/flake8
-    rev: 7.3.0
-    hooks:
-    - id: flake8
--   repo: https://github.com/pylint-dev/pylint
-    rev: v3.3.8
-    hooks:
-    - id: pylint
+    -   id: ruff
+        args: [--fix]
+    -   id: ruff-format
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.17.1
     hooks:

.pylintrc

@@ -1,15 +0,0 @@
-[MASTER]
-init-hook="from pylint.config import find_default_config_files; import sys; sys.path.append(next(find_default_config_files()).parent.as_posix())"
-
-[TYPECHECK]
-
-# List of members which are set dynamically and missed by Pylint inference
-# system, and so shouldn't trigger E1101 when accessed.
-generated-members=numpy.*, torch.*
-
-
-[pylint.messages_control]
-disable=missing-function-docstring, line-too-long, import-error,
-    too-many-arguments, too-many-locals, too-many-statements, too-many-branches, too-few-public-methods,
-    too-many-instance-attributes, fixme, import-outside-toplevel, logging-fstring-interpolation,
-    too-many-positional-arguments, possibly-used-before-assignment

CITATION.cff

@@ -1,6 +1,6 @@
 cff-version: 1.2.0
 type: software
-title: "Axolotl: Post-Training for AI Models"
+title: "Axolotl: Open Source LLM Post-Training"
 message: "If you use this software, please cite it as below."
 authors:
   - name: "Axolotl maintainers and contributors"

README.md

@@ -5,6 +5,9 @@
         <img alt="Axolotl" src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/887513285d98132142bf5db2a74eb5e0928787f1/image/axolotl_logo_digital_black.svg" width="400" height="104" style="max-width: 100%;">
     </picture>
 </p>
+  <p align="center">
+      <strong>A Free and Open Source LLM Fine-tuning Framework</strong><br>
+  </p>
 
 <p align="center">
     <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
@@ -17,6 +20,7 @@
     <br/>
     <a href="https://discord.com/invite/HhrNrHJPRb"><img src="https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord" alt="discord" style="height: 20px;"></a>
     <a href="https://twitter.com/axolotl_ai"><img src="https://img.shields.io/twitter/follow/axolotl_ai?style=social" alt="twitter" style="height: 20px;"></a>
+    <a href="https://colab.research.google.com/github/axolotl-ai-cloud/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google-colab" style="height: 20px;"></a>
     <br/>
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
@@ -49,20 +53,21 @@
 
 ## ✨ Overview
 
-Axolotl is a tool designed to streamline post-training for various AI models.
+Axolotl is a free and open-source tool designed to streamline post-training and fine-tuning for the latest large language models (LLMs).
 
 Features:
 
-- **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models.
-- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM).
-- **Easy Configuration**: Re-use a single YAML file between dataset preprocess, training, evaluation, quantization, and inference.
+- **Multiple Model Support**: Train various models like GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many more models available on the Hugging Face Hub.
+- **Multimodal Training**: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, and audio models like Voxtral with image, video, and audio support.
+- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).
+- **Easy Configuration**: Re-use a single YAML configuration file across the full fine-tuning pipeline: dataset preprocessing, training, evaluation, quantization, and inference.
 - **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!
 - **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
 - **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.
 
 
 
-## 🚀 Quick Start
+## 🚀 Quick Start - LLM Fine-tuning in Minutes
 
 **Requirements**:
 
@@ -70,6 +75,10 @@ Features:
 - Python 3.11
 - PyTorch ≥2.6.0
 
+### Google Colab
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/axolotl-ai-cloud/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb#scrollTo=msOCO4NRmRLa)
+
 ### Installation
 
 #### Using pip
@@ -155,7 +164,7 @@ If you use Axolotl in your research or projects, please cite it as follows:
 
 ```bibtex
 @software{axolotl,
-  title = {Axolotl: Post-Training for AI Models},
+  title = {Axolotl: Open Source LLM Post-Training},
   author = {{Axolotl maintainers and contributors}},
   url = {https://github.com/axolotl-ai-cloud/axolotl},
   license = {Apache-2.0},

_quarto.yml

@@ -153,7 +153,7 @@ quartodoc:
         - utils.distributed
         - utils.dict
         - utils.optimizers.adopt
-        - utils.data.pretraining
+        - utils.data.streaming
         - utils.data.sft
         - utils.quantization
     - title: Schemas
@@ -272,6 +272,7 @@ website:
           contents:
             - docs/batch_vs_grad.qmd
             - docs/dataset_preprocessing.qmd
+            - docs/streaming.qmd
             - docs/multipack.qmd
             - docs/mixed_precision.qmd
             - docs/optimizers.qmd
@@ -284,6 +285,7 @@ website:
             - docs/custom_integrations.qmd
             - docs/sequence_parallelism.qmd
             - docs/gradient_checkpointing.qmd
+            - docs/moe_backends.md
             - docs/nd_parallelism.qmd
 
         - section: "Troubleshooting"

cicd/multigpu.py

@@ -2,8 +2,6 @@
 modal application to run axolotl gpu tests in Modal
 """
 
-# pylint: disable=duplicate-code
-
 import os
 import pathlib
 import tempfile
@@ -63,7 +61,7 @@ def run_cmd(cmd: str, run_folder: str):
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(cmd.split(), cwd=run_folder):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)
 
 
 @app.function(

cicd/single_gpu.py

@@ -1,7 +1,5 @@
 """Modal app to run axolotl GPU tests"""
 
-# pylint: disable=duplicate-code
-
 import os
 import pathlib
 import tempfile
@@ -59,7 +57,8 @@ VOLUME_CONFIG = {
 }
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = f"L40S:{N_GPUS}"
+GPU_TYPE = os.environ.get("GPU_TYPE", "L40S")
+GPU_CONFIG = f"{GPU_TYPE}:{N_GPUS}"
 
 
 def run_cmd(cmd: str, run_folder: str):
@@ -70,4 +69,4 @@ def run_cmd(cmd: str, run_folder: str):
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(cmd.split(), cwd=run_folder, env=sp_env):  # nosec
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)

codecov.yml

@@ -12,7 +12,7 @@ coverage:
       default:
         # basic
         target: auto
-        threshold: 0%
+        threshold: 1%
         base: auto
         # advanced
         branches: null
@@ -27,7 +27,7 @@ coverage:
       default:
         # basic
         target: auto
-        threshold: 0%
+        threshold: 1%
         base: auto
         # advanced
         branches: null

docs/installation.qmd

@@ -134,7 +134,7 @@ For providers supporting Docker:
 
 ### Google Colab {#sec-colab}
 
-Use our [example notebook](../examples/colab-notebooks/colab-axolotl-example.ipynb).
+[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/axolotl-ai-cloud/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb#scrollTo=msOCO4NRmRLa)
 
 ## Platform-Specific Instructions {#sec-platform-specific}
 

docs/moe_backends.md

@@ -0,0 +1,18 @@
+MoE Backends in Axolotl
+
+Axolotl supports selecting a Mixture-of-Experts (MoE) compute backend via the training config (YAML):
+
+- Set `moe_backend: auto|torch_grouped|naive`
+
+Behavior
+- auto (default): prefers PyTorch 2.8+ grouped GEMM; otherwise naive.
+- torch_grouped: targets PyTorch 2.8+ grouped GEMM (H100/SM90+ recommended).
+- naive: keeps the reference per-expert loop.
+
+Notes
+- Current implementation wires the backend selector and routes Mixtral MoE through it. Torch grouped uses cuBLASLt grouped GEMM when available; otherwise, the code falls back to the naive per-expert loop.
+- No changes to training scripts are required; selection happens inside the model forward.
+
+Example
+moe_backend: torch_grouped
+accelerate launch -m axolotl.cli.train path/to/config.yaml

docs/multi-gpu.qmd

@@ -63,15 +63,6 @@ Start from Stage 1 -> Stage 2 -> Stage 3.
 
 :::
 
-::: {.callout-tip}
-
-Using ZeRO Stage 3 with Single-GPU training
-
-ZeRO Stage 3 can be used for training on a single GPU by manually setting the environment variables:
-`WORLD_SIZE=1 LOCAL_RANK=0 MASTER_ADDR=0.0.0.0 MASTER_PORT=29500`
-
-:::
-
 ## Fully Sharded Data Parallel (FSDP) {#sec-fsdp}
 
 ::: {.callout-note}

docs/qat.qmd

@@ -23,10 +23,17 @@ To enable QAT in axolotl, add the following to your configuration file:
 
 ```yaml
 qat:
-  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
-  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4" and "int8"
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4", "int8", "float8"
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4", "fp8", and "nvfp4".
   group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
   fake_quant_after_n_steps: # Optional[int] = None. The number of steps to apply fake quantization after
 ```
 
+We support the following quantization schemas:
+- `Int4WeightOnly` (requires the `fbgemm-gpu` extra when installing Axolotl)
+- `Int8DynamicActivationInt4Weight`
+- `Float8DynamicActivationFloat8Weight`
+- `Float8DynamicActivationInt4Weight`
+- `NVFP4`
+
 Once you have finished training, you must quantize your model by using the same quantization configuration which you used to train the model with. You can use the [`quantize`](./quantize.qmd) command to do this.

docs/quantize.qmd

@@ -22,8 +22,8 @@ Quantization is configured using the `quantization` key in your configuration fi
 ```yaml
 base_model: # The path to the model to quantize.
 quantization:
-  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are uintX for X in [1, 2, 3, 4, 5, 6, 7], or int4, or int8
-  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4" and "int8"
+  activation_dtype: # Optional[str] = "int8". Fake quantization layout to use for activation quantization. Valid options are "int4", "int8", "float8"
+  weight_dtype: # Optional[str] = "int8". Fake quantization layout to use for weight quantization. Valid options are "int4", "fp8", and "nvfp4".
   group_size: # Optional[int] = 32. The number of elements in each group for per-group fake quantization
   quantize_embedding: # Optional[bool] = False. Whether to quantize the embedding layer.
 
@@ -39,9 +39,8 @@ you used to train the model:
 # qat.yml
 qat:
   activation_dtype: int8
-  weight_dtype: int8
+  weight_dtype: int4
   group_size: 256
-  quantize_embedding: true
 
 output_dir: # The path to the output directory used during training where the final checkpoint has been saved.
 ```
@@ -51,3 +50,11 @@ axolotl quantize qat.yml
 ```
 
 This ensures that an identical quantization configuration is used to quantize the model as was used to train it.
+
+
+::: {.callout-note}
+
+If you have configured pushing to hub with `hub_model_id`, your model hub name will have the quantization schema appended to it,
+e.g. `axolotl-ai-cloud/qat-nvfp4-llama3B` will become `axolotl-ai-cloud/qat-nvfp4-llama3B-nvfp4w`
+
+:::

docs/reward_modelling.qmd

@@ -11,6 +11,7 @@ We support the reward modelling techniques supported by `trl`.
 ### (Outcome) Reward Models
 
 Outcome reward models are trained using data which contains preference annotations for an entire interaction between the user and model (e.g. rather than per-turn or per-step).
+For improved training stability, you can use the `center_rewards_coefficient` parameter to encourage mean-zero reward outputs ([see TRL docs](https://huggingface.co/docs/trl/v0.10.1/en/reward_trainer#centering-rewards)).
 
 ```yaml
 base_model: google/gemma-2-2b

docs/scripts/generate_config_docs.py

@@ -47,7 +47,6 @@ class QuartoGenerator:
         """Check if a type is a Pydantic BaseModel."""
         return inspect.isclass(type_obj) and issubclass(type_obj, BaseModel)
 
-    # pylint: disable=too-many-return-statements
     def _extract_nested_type(self, field_type) -> Any:
         """Extract the actual type from complex type annotations."""
         # Handle Annotated types (Python 3.9+)
@@ -124,7 +123,6 @@ class QuartoGenerator:
 
         return field_type
 
-    # pylint: disable=too-many-return-statements
     def _extract_all_pydantic_models_from_type(
         self, field_type
     ) -> list[type[BaseModel]]:
@@ -318,7 +316,6 @@ class QuartoGenerator:
 
         return all_groups
 
-    # pylint: disable=too-many-return-statements
     def _extract_field_groups_from_source(
         self, model_class: type[BaseModel]
     ) -> list[dict]:
@@ -503,7 +500,7 @@ class QuartoGenerator:
                     nested_schema = nested_model.model_json_schema()
                     nested_properties = nested_schema.get("properties", {})
                     nested_required = nested_schema.get("required", [])
-                except Exception:  # pylint: disable=broad-exception-caught
+                except Exception:
                     # Fallback: use model fields directly
                     nested_properties = {}
                     nested_required = []
@@ -607,7 +604,7 @@ class QuartoGenerator:
             schema = model_class.model_json_schema()
             properties = schema.get("properties", {})
             required = schema.get("required", [])
-        except Exception as e:  # pylint: disable=broad-exception-caught
+        except Exception as e:
             print(
                 f"Warning: Could not generate JSON schema ({e}). Using model fields instead."
             )

docs/streaming.qmd

@@ -0,0 +1,120 @@
+---
+title: Streaming Datasets
+description: How to use streaming mode for large-scale datasets and memory-efficient training
+order: 10
+---
+
+Streaming enables memory-efficient training with large datasets by loading data
+incrementally rather than loading the entire dataset into memory at once.
+
+Use streaming when:
+
+- Your dataset is too large to fit in memory (e.g. when you're doing pretraining with massive text corpora)
+- You want to start training immediately without preprocessing the entire dataset
+
+Streaming works with both remote and locally stored datasets!
+
+::: {.callout-note}
+Streaming currently only supports a single dataset. Multi-dataset support will be added soon.
+:::
+
+
+## Configuration
+
+### Basic Streaming
+
+Enable streaming mode by setting the `streaming` flag:
+
+```yaml
+streaming: true
+```
+
+### Pretraining with Streaming
+
+For pretraining tasks, streaming is automatically enabled when using `pretraining_dataset`:
+
+```yaml
+pretraining_dataset:
+  - path: HuggingFaceFW/fineweb-edu
+    type: pretrain
+    text_column: text
+    split: train
+
+# Optionally, enable sample packing
+streaming_multipack_buffer_size: 10000
+sample_packing: true
+```
+
+### SFT with Streaming
+
+For supervised fine-tuning with streaming:
+
+```yaml
+streaming: true
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+    split: train
+
+# Optionally, enable sample packing
+streaming_multipack_buffer_size: 10000
+sample_packing: true
+```
+
+## Configuration Options
+
+### `streaming_multipack_buffer_size`
+
+Controls the buffer size for multipack streaming (default: 10,000). This determines how
+many samples are buffered before packing. Larger buffers can improve packing efficiency
+but use more memory.
+
+### `shuffle_merged_datasets`
+
+When enabled, shuffles the streaming dataset using the buffer. This requires additional
+memory for the shuffle buffer.
+
+## Sample Packing with Streaming
+
+Sample packing is supported for streaming datasets. When enabled, multiple samples are
+packed into a single sequence to maximize GPU utilization:
+
+```yaml
+sample_packing: true
+streaming_multipack_buffer_size: 10000
+
+# For SFT: attention is automatically isolated between packed samples
+# For pretraining: control with pretrain_multipack_attn
+pretrain_multipack_attn: true  # prevent cross-attention between packed samples
+```
+
+For more information, see our [documentation](multipack.qmd) on multipacking.
+
+## Important Considerations
+
+### Memory Usage
+
+While streaming reduces memory usage compared to loading entire datasets, you still need
+to consider:
+
+- You can control the memory usage by adjusting `streaming_multipack_buffer_size`
+- Sample packing requires buffering multiple samples
+- Shuffling requires additional memory for the shuffle buffer
+
+### Performance
+
+- Streaming may have slightly higher latency compared to preprocessed datasets, as samples are processed on-the-fly
+- Network speed and disk read speed are important when streaming from remote sources or a local dataset, respectively
+- Consider using `axolotl preprocess` for smaller or more frequently used datasets
+
+### Evaluation Datasets
+
+Evaluation datasets are not streamed to ensure consistent evaluation metrics. They're
+loaded normally even when training uses streaming.
+
+## Examples
+
+See the `examples/streaming/` directory for complete configuration examples:
+
+- `pretrain.yaml`: Pretraining with streaming dataset
+- `sft.yaml`: Supervised fine-tuning with streaming

examples/cloud/baseten.yaml

@@ -0,0 +1,10 @@
+provider: baseten
+project_name:
+
+secrets:
+  - HF_TOKEN
+  - WANDB_API_KEY
+
+gpu: h100
+gpu_count: 8
+node_count: 1

examples/devstral/README.md

@@ -20,7 +20,13 @@ pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 
-2. Run the finetuning example:
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
+
+```bash
+python scripts/cutcrossentropy_install.py | sh
+```
+
+3. Run the finetuning example:
 
 ```bash
 axolotl train examples/devstral/devstral-small-qlora.yml

examples/gemma3/270m-qlora.yml

@@ -0,0 +1,68 @@
+base_model: google/gemma-3-270m-it
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
+load_in_8bit: false
+load_in_4bit: true
+
+# huggingface repo
+chat_template: gemma3
+eot_tokens:
+  - <end_of_turn>
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/gpt-oss/README.md

@@ -106,6 +106,16 @@ See [Nanobit/text-tools-2k-test](https://huggingface.co/datasets/Nanobit/text-to
 
 Refer to [our docs](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#using-tool-use) for more info.
 
+### Thinking and chat_template masking conflict
+
+OpenAI’s Harmony template hides `thinking` in all non-final turns, which conflicts with Axolotl’s `chat_template` masking.
+
+If your dataset has `thinking` content mid-turn, there are two paths we recommend:
+
+- Train only on the last turn. This can be accomplished via chat_template's [train on last doc](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#training-on-last-message).
+
+- Adjust your dataset to only have `thinking` content in the last turn.
+
 ### TIPS
 
 - Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).

examples/hunyuan/README.md

@@ -0,0 +1,85 @@
+# Finetune HunYuan with Axolotl
+
+Tencent released a family of opensource models called HunYuan with varying parameter scales of 0.5B, 1.8B, 4B, and 7B scale for both Pre-trained and Instruct variants. The models can be found at [HuggingFace](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as HunYuan is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. Run the finetuning example:
+
+```bash
+axolotl train examples/hunyuan/hunyuan-v1-dense-qlora.yaml
+```
+
+This config uses about 4.7 GB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### Dataset
+
+HunYuan Instruct models can choose to enter a slow think or fast think pattern. For best performance on fine-tuning their Instruct models, your dataset should be adjusted to match their pattern.
+
+```python
+# fast think pattern
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "/no_think What color is the sun?" },
+    {"role": "assistant", "content": "<think>\n\n</think>\n<answer>\nThe sun is yellow.\n</answer>"}
+]
+
+# slow think pattern
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "/no_think What color is the sun?" },
+    {"role": "assistant", "content": "<think>\nThe user is asking about the color of the sun. I need to ...\n</think>\n<answer>\nThe sun is yellow.\n</answer>"}
+]
+```
+
+### TIPS
+
+- For inference, the official Tencent team recommends
+
+```json
+
+{
+  "do_sample": true,
+  "top_k": 20,
+  "top_p": 0.8,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7
+}
+
+```
+
+- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Related Resources
+
+- [Tencent HunYuan Blog](https://hunyuan.tencent.com/)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/hunyuan/hunyuan-v1-dense-qlora.yaml

@@ -0,0 +1,64 @@
+base_model: tencent/Hunyuan-0.5B-Instruct
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

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

@@ -15,20 +15,18 @@ liger_glu_activation: true
 liger_layer_norm: true
 liger_fused_linear_cross_entropy: true
 
+
 datasets:
   - path: yahma/alpaca-cleaned
     type: alpaca
+    split: train[:95%]
 
 output_dir: ./outputs/qat_out/
+dataset_prepared_path: ./outputs/qat_out/dataset_prepared
 
-sample_packing: true
-
-sequence_len: 512
-
-flex_attention: true
-flex_attn_compile_kwargs:
-  dynamic: false
-  mode: max-autotune-no-cudagraphs
+sample_packing: false
+sequence_len: 8192
+flash_attention: true
 
 qat:
   activation_dtype: int8
@@ -67,7 +65,7 @@ fsdp:
 fsdp_config:
   fsdp_version: 2
   fsdp_offload_params: false
-  fsdp_cpu_ram_efficient_loading: true
+  fsdp_cpu_ram_efficient_loading: false
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
   fsdp_state_dict_type: FULL_STATE_DICT
@@ -76,6 +74,6 @@ fsdp_config:
   fsdp_activation_checkpointing: true
 
 special_tokens:
-  pad_token: <|end_of_text|>
+  pad_token: <|finetune_right_pad_id|>
 
 # save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-3/3b-qat-nvfp4.yaml

@@ -0,0 +1,64 @@
+base_model: meta-llama/Llama-3.2-3B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+
+datasets:
+  - path: yahma/alpaca-cleaned
+    type: alpaca
+    split: train[:95%]
+
+output_dir: ./outputs/qat_out/
+dataset_prepared_path: ./outputs/dataset_prepared
+
+sequence_len: 8192
+flash_attention: true
+
+qat:
+  activation_dtype: nvfp4
+  weight_dtype: nvfp4
+  group_size: 16 # only group_size of 16 is supported with nvfp4
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_checkpointing: true
+gradient_accumulation_steps: 1
+micro_batch_size: 64
+num_epochs: 1
+optimizer: adamw_torch_fused
+
+cosine_constant_lr_ratio: 0
+cosine_min_lr_ratio: 1.0
+learning_rate: 2e-5
+save_only_model: true
+bf16: true
+
+resume_from_checkpoint:
+logging_steps: 1
+
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+warmup_ratio: 0.1
+weight_decay: 0.0
+
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-3/diffusion/pretrain-1b.yaml

@@ -0,0 +1,56 @@
+base_model: meta-llama/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+pretraining_dataset:
+  - path: wikitext
+    name: wikitext-103-raw-v1
+    type: completion
+    field: text
+
+plugins:
+  - axolotl.integrations.diffusion.DiffusionPlugin
+
+diffusion:
+  noise_schedule: cosine
+  min_mask_ratio: 0.15
+  max_mask_ratio: 0.85
+  num_diffusion_steps: 128
+  eps: 5e-4
+  importance_weighting: true
+  mask_token_id: 128002
+  generate_samples: true
+  generation_interval: 250
+
+output_dir: ./outputs/model-out
+
+sequence_len: 512
+sample_packing: true
+
+gradient_accumulation_steps: 8
+micro_batch_size: 4
+max_steps: 10000
+warmup_ratio: 0.1
+
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 3e-4
+sdp_attention: true
+
+bf16: auto
+tf32: true
+
+logging_steps: 1
+save_strategy: steps
+save_steps: 1000
+
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-3/diffusion/sft-1b.yaml

@@ -0,0 +1,59 @@
+base_model: meta-llama/Llama-3.2-1B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+val_set_size: 0.05
+
+plugins:
+  - axolotl.integrations.diffusion.DiffusionPlugin
+
+diffusion:
+  noise_schedule: cosine
+  min_mask_ratio: 0.1
+  max_mask_ratio: 0.9
+  num_diffusion_steps: 128
+  eps: 1e-3
+  importance_weighting: true
+  mask_token_id: 128002
+  generate_samples: true
+  generation_interval: 250
+
+output_dir: ./outputs/model-out
+
+sequence_len: 512
+sample_packing: true
+eval_sample_packing: true
+
+gradient_accumulation_steps: 4
+micro_batch_size: 4
+num_epochs: 1
+warmup_steps: 0.1
+
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+sdp_attention: true
+
+logging_steps: 1
+save_strategy: best
+eval_strategy: epoch
+
+special_tokens:
+  pad_token: "<|end_of_text|>"
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/magistral/README.md

@@ -18,7 +18,13 @@ pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 ```
 
-2. Run the finetuning example:
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
+
+```bash
+python scripts/cutcrossentropy_install.py | sh
+```
+
+3. Run the finetuning example:
 
 ```bash
 axolotl train examples/magistral/magistral-small-qlora.yaml

examples/moe/qwen2-moe-qlora-10gb.yaml

@@ -0,0 +1,53 @@
+base_model: Qwen/Qwen1.5-MoE-A2.7B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+
+# Keep VRAM low
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/qwen2-moe-qlora-10gb
+
+# Train small to fit 10GB
+sequence_len: 512
+sample_packing: false
+pad_to_sequence_len: false
+
+adapter: qlora
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 5
+flash_attention: true
+
+warmup_ratio: 0.03
+evals_per_epoch: 2
+saves_per_epoch: 1
+weight_decay: 0.0
+
+model_config:
+  output_router_logits: true
+
+special_tokens:

examples/qwen3/reward-model.yaml

@@ -0,0 +1,44 @@
+base_model: Skywork/Skywork-Reward-V2-Qwen3-8B
+model_type: AutoModelForSequenceClassification
+num_labels: 1
+
+reward_model: true
+center_rewards_coefficient: 0.01  # Incentivize mean-zero rewards for improved stability
+chat_template: qwen3
+datasets:
+  - path: argilla/distilabel-intel-orca-dpo-pairs
+    type: bradley_terry.chat_template
+
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 8192
+sample_packing: false
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+deepspeed: deepspeed_configs/zero1.json
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+eval_batch_size: 1
+num_epochs: 3
+optimizer: adamw_bnb_8bit
+lr_scheduler: linear
+learning_rate: 0.00002
+
+bf16: true
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+warmup_ratio: 0.1
+logging_steps: 1
+weight_decay: 0.01

examples/seed-oss/README.md

@@ -0,0 +1,54 @@
+# Finetune ByteDance's Seed-OSS with Axolotl
+
+[Seed-OSS](https://huggingface.co/collections/ByteDance-Seed/seed-oss-68a609f4201e788db05b5dcd) are a series of 36B parameter open source models trained by ByteDance's Seed Team.
+
+This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Seed-OSS is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install Cut Cross Entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. Run the finetuning example:
+
+```bash
+axolotl train examples/seed-oss/seed-oss-36b-qlora.yaml
+```
+
+This config uses about 27.7 GiB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### TIPS
+
+- For inference, the official Seed Team recommends `top_p=0.95` and `temperature=1.1`.
+- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Related Resources
+
+- [ByteDance Seed Website](https://seed.bytedance.com/)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/seed-oss/seed-oss-36b-qlora.yaml

@@ -0,0 +1,56 @@
+base_model: ByteDance-Seed/Seed-OSS-36B-Instruct
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/streaming/README.md

@@ -0,0 +1,50 @@
+# Streaming Dataset Examples
+
+This directory contains example configurations for using Axolotl's streaming dataset
+functionality, which enables memory-efficient training with large datasets.
+
+## Examples
+
+Run the following examples with e.g. `axolotl train examples/streaming/sft.yaml`; no
+`axolotl preprocess` required!
+
+### Pretraining (`pretrain.yaml`)
+
+Demonstrates streaming configuration for pretraining tasks using the fineweb-edu dataset
+with SmolLM2-135M.
+
+- Uses `pretraining_dataset` configuration for automatic streaming
+- Multipack attention control to prevent cross-attention between packed sequences
+- Buffer size configuration for memory management
+
+### SFT (`sft.yaml`)
+
+Shows how to use streaming for supervised fine-tuning with the Alpaca dataset.
+
+- Explicit `streaming: true` flag for SFT datasets
+- Memory-efficient training on instruction datasets
+- Evaluation datasets are currently not streamed
+
+## Key Configuration Options
+
+### `streaming`
+- Enables streaming mode for standard datasets
+- Automatically enabled for `pretraining_dataset`
+
+### `streaming_multipack_buffer_size`
+- Controls buffer size for sample packing (default: 10,000)
+- Larger values improve packing efficiency but use more memory
+- Adjust based on available memory
+
+### `shuffle_merged_datasets`
+- Enables shuffling of streaming datasets
+- Requires additional memory for shuffle buffer
+
+### `sample_packing`
+- Packs multiple samples into single sequences
+- Minimize per-step padding tokens
+
+## Performance Tips
+
+- Download small / frequently-used datasets locally for better performance
+- Larger buffer sizes improve packing efficiency

examples/streaming/pretrain.yaml

@@ -0,0 +1,57 @@
+base_model: HuggingFaceTB/SmolLM2-135M
+
+# Streaming pretraining configuration
+pretraining_dataset:
+  - path: HuggingFaceFW/fineweb-edu
+    name: sample-10BT
+    type: pretrain
+    text_column: text
+    split: train
+
+# Streaming-specific settings
+streaming_multipack_buffer_size: 10000
+shuffle_merged_datasets: true
+
+# Training configuration
+max_steps: 1000
+output_dir: ./outputs/smollm2-135m-pretrain-streaming
+
+# Sequence and packing settings
+sequence_len: 1024
+sample_packing: true
+pretrain_multipack_attn: true  # Prevent cross-attention between packed sequences
+flash_attention: true
+
+# Batch size settings
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+
+# Optimizer and scheduler
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 5e-4
+warmup_ratio: 0.1
+weight_decay: 0.01
+
+# Precision and performance
+bf16: auto
+tf32: true
+
+# Logging and checkpointing
+logging_steps: 10
+save_strategy: steps
+save_steps: 250
+save_total_limit: 3
+
+# Weights & Biases (optional)
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# Special tokens
+special_tokens:
+  pad_token: "<|endoftext|>"
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/streaming/sft.yaml

@@ -0,0 +1,55 @@
+base_model: HuggingFaceTB/SmolLM2-135M
+
+# Dataset configuration
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+    split: train
+
+# Streaming-specific settings
+streaming: true
+streaming_multipack_buffer_size: 10000
+shuffle_merged_datasets: true
+
+# Training configuration
+max_steps: 1000
+output_dir: ./outputs/smollm2-135m-sft-streaming
+
+# Sequence and packing settings
+sequence_len: 1024
+sample_packing: true
+flash_attention: true
+
+# Batch size settings
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+
+# Optimizer and scheduler
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-4
+warmup_ratio: 0.1
+weight_decay: 0.0
+
+# Precision and performance
+bf16: auto
+tf32: true
+
+# Logging and checkpointing
+logging_steps: 10
+save_strategy: steps
+save_steps: 100
+save_total_limit: 3
+
+# Weights & Biases (optional)
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+# Special tokens
+special_tokens:
+  pad_token: "<|endoftext|>"
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/voxtral/README.md

@@ -22,6 +22,9 @@ pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
 # audio
 pip3 install librosa==0.11.0
 pip3 install 'mistral_common[audio]==1.8.3'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
 ```
 
 3. Run the finetuning example:

pyproject.toml

@@ -26,3 +26,34 @@ include-package-data = true
 
 [tool.setuptools.cmdclass]
 build_py = "setuptools_axolotl_dynamic_dependencies.BuildPyCommand"
+
+[tool.ruff]
+line-length = 88
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "W", "C90", "B", "I"]
+ignore = [
+    "E203",  # Whitespace before ':'
+    "E501",  # Line too long
+    "C901",  # Too complex
+    "B019",  # Use of functools.cache on methods
+    "E722",  # Bare except
+    "F821",  # Undefined name (for dynamic exec)
+]
+
+[tool.ruff.lint.isort]
+known-third-party = ["wandb", "comet_ml"]
+known-local-folder = ["src", "tests"]
+# Black-compatible isort settings
+force-single-line = false
+combine-as-imports = true
+split-on-trailing-comma = true
+
+[tool.ruff.format]
+# Use black's formatting style exactly
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+docstring-code-format = false

requirements.txt

@@ -2,8 +2,7 @@
 
 # START section of dependencies that don't install on Darwin/MacOS
 bitsandbytes==0.47.0
-# triton 3.4.0 is not compatible with CCE
-triton>=3.0.0,<3.4.0
+triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
 autoawq==0.2.7.post3
@@ -14,12 +13,12 @@ packaging==23.2
 
 huggingface_hub>=0.33.0
 peft>=0.17.0
-transformers==4.55.3
+transformers==4.56.1
 tokenizers>=0.21.1
-accelerate==1.10.0
+accelerate==1.10.1
 datasets==4.0.0
 deepspeed>=0.17.0
-trl==0.21.0
+trl==0.23.0
 hf_xet==1.1.5
 kernels==0.9.0
 trackio
@@ -65,7 +64,7 @@ langdetect==1.0.9
 immutabledict==4.2.0
 antlr4-python3-runtime==4.13.2
 
-torchao==0.12.0
+torchao==0.13.0
 schedulefree==1.4.1
 
 axolotl-contribs-lgpl==0.0.6

scripts/bench_moe.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+"""Benchmark Hugging Face Qwen2 MoE block with and without grouped_mm."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+import weakref
+from pathlib import Path
+
+import torch
+import torch._dynamo as dynamo
+
+try:
+    from axolotl.kernels.moe import torch_grouped as tg
+except Exception:  # pragma: no cover
+    tg = None
+
+
+def bench(run, *, iters: int, warmup: int, sync: bool = True) -> float:
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    for _ in range(warmup):
+        run()
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+    times = []
+    for _ in range(iters):
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+        start = time.perf_counter()
+        run()
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+        times.append((time.perf_counter() - start) * 1000.0)
+    return sum(times) / len(times)
+
+
+def estimate_moe_flops(tokens: int, hidden: int, inter: int, top_k: int) -> float:
+    return 6.0 * tokens * top_k * hidden * inter
+
+
+def load_hf_block(
+    hidden: int,
+    inter: int,
+    experts: int,
+    top_k: int,
+    *,
+    device: torch.device,
+    dtype: torch.dtype,
+):
+    project_root = Path(__file__).resolve().parents[2]
+    transformers_src = project_root / "transformers" / "src"
+    if transformers_src.exists() and str(transformers_src) not in sys.path:
+        sys.path.append(str(transformers_src))
+
+    from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+    from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
+
+    cfg = Qwen2MoeConfig(
+        hidden_size=hidden,
+        moe_intermediate_size=inter,
+        shared_expert_intermediate_size=inter,
+        num_experts=experts,
+        num_experts_per_tok=top_k,
+        norm_topk_prob=True,
+        qkv_bias=True,
+    )
+
+    block = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+    block_grouped = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+    block_grouped.load_state_dict(block.state_dict())
+    return block, block_grouped
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="Qwen2 MoE grouped_mm benchmark")
+    p.add_argument("--bsz", type=int, default=8)
+    p.add_argument("--seq", type=int, default=1024)
+    p.add_argument("--hidden", type=int, default=4096)
+    p.add_argument("--inter", type=int, default=14336)
+    p.add_argument("--experts", type=int, default=32)
+    p.add_argument("--top_k", type=int, default=4)
+    p.add_argument("--dtype", choices=["bf16", "fp16", "fp32"], default="bf16")
+    p.add_argument("--iters", type=int, default=50)
+    p.add_argument("--warmup", type=int, default=10)
+    p.add_argument("--profile", action="store_true")
+    p.add_argument(
+        "--compile",
+        action="store_true",
+        help="Torch.compile both paths before benchmarking",
+    )
+    args = p.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    dtype = {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[args.dtype]
+
+    torch.manual_seed(0)
+    if device.type == "cuda":
+        torch.cuda.manual_seed(0)
+
+    block_naive, block_grouped = load_hf_block(
+        args.hidden,
+        args.inter,
+        args.experts,
+        args.top_k,
+        device=device,
+        dtype=dtype,
+    )
+
+    tokens = args.bsz * args.seq
+    flops_total = estimate_moe_flops(tokens, args.hidden, args.inter, args.top_k)
+    print(
+        f"Device={device} dtype={dtype} tokens={tokens} hidden={args.hidden} inter={args.inter} "
+        f"experts={args.experts} top_k={args.top_k}"
+    )
+
+    x = torch.randn(args.bsz, args.seq, args.hidden, device=device, dtype=dtype)
+
+    # Optional torch.compile
+    run_grouped_impl = None
+    if args.compile:
+        dynamo.config.capture_scalar_outputs = True
+        dynamo.config.allow_unspec_int_on_nn_module = True
+        try:
+            block_naive = torch.compile(block_naive)  # type: ignore[arg-type]
+        except Exception as exc:  # pragma: no cover
+            print(f"torch.compile naive failed ({exc}); using eager")
+        else:
+
+            def grouped_forward(inp, *, block=block_grouped):
+                block.experts._ax_parent_block_ref = weakref.ref(block)  # type: ignore[attr-defined]
+                y, _ = tg.moe_ffn_forward_grouped(
+                    inp, block.gate, block.experts, block.top_k
+                )
+                return y
+
+            try:
+                run_grouped_impl = torch.compile(grouped_forward)  # type: ignore[arg-type]
+            except Exception as exc:  # pragma: no cover
+                print(f"torch.compile grouped failed ({exc}); using eager")
+                run_grouped_impl = None
+
+    def run_naive(block=block_naive, data=x):
+        y, _ = block(data)
+        return y
+
+    def run_grouped(block=block_grouped, data=x, impl=run_grouped_impl):
+        if impl is not None:
+            return impl(data)
+        if tg is None or not tg.available():
+            return torch.empty(0)
+        block.experts._ax_parent_block_ref = weakref.ref(block)  # type: ignore[attr-defined]
+        y, _ = tg.moe_ffn_forward_grouped(data, block.gate, block.experts, block.top_k)
+        return y if y is not None else torch.empty(0)
+
+    t_naive = bench(run_naive, iters=args.iters, warmup=args.warmup)
+    tflops_naive = flops_total / ((t_naive / 1000.0) * 1e12)
+    print(
+        f"naive\t{t_naive:.2f} ms\t{tokens / (t_naive / 1000.0):.1f} tok/s\t{tflops_naive:.2f} TFLOP/s"
+    )
+
+    with torch.no_grad():
+        y_ref = run_naive()
+
+    if tg is None or not tg.available():
+        print("torch_grouped\tN/A (unavailable)")
+        return
+
+    y_grouped = run_grouped()
+    if y_grouped.numel() == 0:
+        print("torch_grouped\tN/A (op not callable)")
+        return
+
+    t_grouped = bench(run_grouped, iters=args.iters, warmup=args.warmup)
+    tflops_grouped = flops_total / ((t_grouped / 1000.0) * 1e12)
+    speedup = t_naive / t_grouped
+    print(
+        f"torch_grouped\t{t_grouped:.2f} ms\t{tokens / (t_grouped / 1000.0):.1f} tok/s\t"
+        f"{tflops_grouped:.2f} TFLOP/s\t{speedup:.2f}×"
+    )
+
+    diff = (y_ref.float() - y_grouped.float()).abs()
+    print(
+        "torch_grouped_check: "
+        f"max_abs={diff.max().item():.3e} mean_abs={diff.mean().item():.3e} "
+        f"rel_l2={(diff.pow(2).sum() / (y_ref.float().pow(2).sum() + 1e-12)).sqrt().item():.3e}"
+    )
+
+    if args.profile:
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CUDA], record_shapes=True
+        ) as prof:
+            run_naive()
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CUDA], record_shapes=True
+        ) as prof:
+            run_grouped()
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+
+
+if __name__ == "__main__":
+    main()

scripts/bench_moe_sweep.py

@@ -0,0 +1,311 @@
+#!/usr/bin/env python
+"""Sweep grouped_mm vs naive performance for Qwen2 MoE block."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import sys
+import time
+import weakref
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List
+
+import torch
+import torch._dynamo as dynamo
+
+try:
+    from axolotl.kernels.moe import torch_grouped as tg
+except Exception:  # pragma: no cover
+    tg = None
+
+
+def _parse_list(arg: str) -> List[int]:
+    return [int(v) for v in arg.split(",") if v]
+
+
+def _bench(run, *, iters: int, warmup: int, device: torch.device) -> float:
+    for _ in range(warmup):
+        run()
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+    times: List[float] = []
+    for _ in range(iters):
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        start = time.perf_counter()
+        run()
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        times.append((time.perf_counter() - start) * 1000.0)
+    return sum(times) / len(times)
+
+
+def _estimate_flops(tokens: int, hidden: int, inter: int, top_k: int) -> float:
+    return 6.0 * tokens * top_k * hidden * inter
+
+
+def _load_block(
+    hidden: int,
+    inter: int,
+    experts: int,
+    top_k: int,
+    *,
+    device: torch.device,
+    dtype: torch.dtype,
+):
+    project_root = Path(__file__).resolve().parents[2]
+    transformers_src = project_root / "transformers" / "src"
+    if transformers_src.exists() and str(transformers_src) not in sys.path:
+        sys.path.append(str(transformers_src))
+
+    from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+    from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
+
+    cfg = Qwen2MoeConfig(
+        hidden_size=hidden,
+        moe_intermediate_size=inter,
+        shared_expert_intermediate_size=inter,
+        num_experts=experts,
+        num_experts_per_tok=top_k,
+        norm_topk_prob=True,
+        qkv_bias=True,
+    )
+
+    block = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+    block_grouped = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+    block_grouped.load_state_dict(block.state_dict())
+    return block, block_grouped
+
+
+@dataclass
+class Result:
+    bsz: int
+    seq: int
+    hidden: int
+    inter: int
+    experts: int
+    top_k: int
+    dtype: str
+    naive_ms: float
+    grouped_ms: float
+    speedup: float
+    naive_tflops: float
+    grouped_tflops: float
+    max_abs: float
+    mean_abs: float
+    rel_l2: float
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="Grouped MoE sweep")
+    p.add_argument("--batch-sizes", default="4,8,16")
+    p.add_argument("--seq-lens", default="512,1024,2048")
+    p.add_argument("--hidden", default="2048,4096")
+    p.add_argument("--inter", default="5632,8192,14336")
+    p.add_argument("--experts", default="8,16,32")
+    p.add_argument("--top-k", default="1,2,4")
+    p.add_argument("--dtype", choices=["bf16", "fp16", "fp32"], default="bf16")
+    p.add_argument("--iters", type=int, default=25)
+    p.add_argument("--warmup", type=int, default=5)
+    p.add_argument("--csv", type=Path, default=None)
+    p.add_argument("--compile", action="store_true")
+    args = p.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    dtype = {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[args.dtype]
+
+    if tg is None or not tg.available():
+        print("torch_grouped unavailable; sweep aborted")
+        return
+
+    bs_list = _parse_list(args.batch_sizes)
+    seq_list = _parse_list(args.seq_lens)
+    hidden_list = _parse_list(args.hidden)
+    inter_list = _parse_list(args.inter)
+    expert_list = _parse_list(args.experts)
+    topk_list = _parse_list(args.top_k)
+
+    results: List[Result] = []
+
+    print(
+        "bsz\tseq\thidden\tinter\texperts\ttop_k\tnaive(ms)\tgrouped(ms)\tspeedup\t"
+        "naive TF/s\tgrouped TF/s\tmax_abs\tmean_abs\trel_l2"
+    )
+
+    for bsz in bs_list:
+        for seq in seq_list:
+            tokens = bsz * seq
+            for hidden in hidden_list:
+                for inter in inter_list:
+                    for experts in expert_list:
+                        for top_k in topk_list:
+                            torch.manual_seed(0)
+                            if device.type == "cuda":
+                                torch.cuda.manual_seed(0)
+
+                            block_naive, block_grouped = _load_block(
+                                hidden,
+                                inter,
+                                experts,
+                                top_k,
+                                device=device,
+                                dtype=dtype,
+                            )
+
+                            x = torch.randn(
+                                bsz, seq, hidden, device=device, dtype=dtype
+                            )
+
+                            compiled_impl = None
+                            if args.compile:
+                                dynamo.config.capture_scalar_outputs = True
+                                dynamo.config.allow_unspec_int_on_nn_module = True
+                                try:
+                                    block_naive = torch.compile(block_naive)  # type: ignore[arg-type]
+                                except Exception as exc:
+                                    print(
+                                        f"torch.compile naive failed ({exc}); using eager"
+                                    )
+                                else:
+
+                                    def grouped_forward(inp, *, block=block_grouped):
+                                        block.experts._ax_parent_block_ref = (
+                                            weakref.ref(block)
+                                        )  # type: ignore[attr-defined]
+                                        y, _ = tg.moe_ffn_forward_grouped(
+                                            inp,
+                                            block.gate,
+                                            block.experts,
+                                            block.top_k,
+                                        )
+                                        return y
+
+                                    try:
+                                        compiled_impl = torch.compile(grouped_forward)  # type: ignore[arg-type]
+                                    except Exception as exc:
+                                        print(
+                                            f"torch.compile grouped failed ({exc}); using eager"
+                                        )
+                                        compiled_impl = None
+
+                            def run_naive(block=block_naive, data=x):
+                                y, _ = block(data)
+                                return y
+
+                            def run_grouped(
+                                block=block_grouped, data=x, impl=compiled_impl
+                            ):
+                                if impl is not None:
+                                    return impl(data)
+                                block.experts._ax_parent_block_ref = weakref.ref(block)  # type: ignore[attr-defined]
+                                y, _ = tg.moe_ffn_forward_grouped(
+                                    data,
+                                    block.gate,
+                                    block.experts,
+                                    block.top_k,
+                                )
+                                return y
+
+                            naive_ms = _bench(
+                                run_naive,
+                                iters=args.iters,
+                                warmup=args.warmup,
+                                device=device,
+                            )
+                            y_naive = run_naive()
+
+                            grouped_ms = _bench(
+                                run_grouped,
+                                iters=args.iters,
+                                warmup=args.warmup,
+                                device=device,
+                            )
+                            y_grouped = run_grouped()
+
+                            diff = (y_naive.float() - y_grouped.float()).abs()
+                            res = Result(
+                                bsz,
+                                seq,
+                                hidden,
+                                inter,
+                                experts,
+                                top_k,
+                                args.dtype,
+                                naive_ms,
+                                grouped_ms,
+                                naive_ms / grouped_ms,
+                                _estimate_flops(tokens, hidden, inter, top_k)
+                                / ((naive_ms / 1000.0) * 1e12),
+                                _estimate_flops(tokens, hidden, inter, top_k)
+                                / ((grouped_ms / 1000.0) * 1e12),
+                                diff.max().item(),
+                                diff.mean().item(),
+                                (
+                                    (
+                                        diff.pow(2).sum()
+                                        / (y_naive.float().pow(2).sum() + 1e-12)
+                                    )
+                                    .sqrt()
+                                    .item()
+                                ),
+                            )
+                            results.append(res)
+                            print(
+                                f"{bsz}\t{seq}\t{hidden}\t{inter}\t{experts}\t{top_k}\t{res.naive_ms:.2f}\t"
+                                f"{res.grouped_ms:.2f}\t{res.speedup:.2f}\t{res.naive_tflops:.2f}\t"
+                                f"{res.grouped_tflops:.2f}\t{res.max_abs:.2e}\t{res.mean_abs:.2e}\t{res.rel_l2:.2e}"
+                            )
+
+    if args.csv:
+        fieldnames = [
+            "bsz",
+            "seq",
+            "hidden",
+            "inter",
+            "experts",
+            "top_k",
+            "dtype",
+            "naive_ms",
+            "grouped_ms",
+            "speedup",
+            "naive_tflops",
+            "grouped_tflops",
+            "max_abs",
+            "mean_abs",
+            "rel_l2",
+        ]
+        with args.csv.open("w", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=fieldnames)
+            writer.writeheader()
+            for r in results:
+                writer.writerow(
+                    {
+                        "bsz": r.bsz,
+                        "seq": r.seq,
+                        "hidden": r.hidden,
+                        "inter": r.inter,
+                        "experts": r.experts,
+                        "top_k": r.top_k,
+                        "dtype": r.dtype,
+                        "naive_ms": f"{r.naive_ms:.4f}",
+                        "grouped_ms": f"{r.grouped_ms:.4f}",
+                        "speedup": f"{r.speedup:.4f}",
+                        "naive_tflops": f"{r.naive_tflops:.4f}",
+                        "grouped_tflops": f"{r.grouped_tflops:.4f}",
+                        "max_abs": f"{r.max_abs:.6e}",
+                        "mean_abs": f"{r.mean_abs:.6e}",
+                        "rel_l2": f"{r.rel_l2:.6e}",
+                    }
+                )
+
+
+if __name__ == "__main__":
+    import weakref
+
+    main()

scripts/bench_torchtitan_moe.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python
+"""Benchmark Torchtitan MoE grouped vs naive expert execution."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import torch
+
+# Ensure torchtitan is importable when running from the axolotl tree
+_PROJECT_ROOT = Path(__file__).resolve().parents[2]
+_TITAN_PATH = _PROJECT_ROOT / "torchtitan"
+if str(_TITAN_PATH) not in sys.path:
+    sys.path.append(str(_TITAN_PATH))
+
+from torchtitan.models.moe import MoE, MoEArgs
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Torchtitan MoE microbenchmark")
+    p.add_argument("--bsz", type=int, default=8)
+    p.add_argument("--seq", type=int, default=1024)
+    p.add_argument("--hidden", type=int, default=4096)
+    p.add_argument("--inter", type=int, default=14336)
+    p.add_argument("--experts", type=int, default=8)
+    p.add_argument("--top_k", type=int, default=2)
+    p.add_argument("--dtype", choices=["bf16", "fp16", "fp32"], default="bf16")
+    p.add_argument("--iters", type=int, default=50)
+    p.add_argument("--warmup", type=int, default=10)
+    p.add_argument("--init-std", type=float, default=0.02)
+    p.add_argument(
+        "--score-before",
+        action="store_true",
+        help="Apply routing scores before expert computation (default: after)",
+    )
+    p.add_argument(
+        "--score-func",
+        choices=["softmax", "sigmoid"],
+        default="softmax",
+    )
+    p.add_argument(
+        "--route-norm",
+        action="store_true",
+        help="Enable Torchtitan router normalization when using sigmoid scores.",
+    )
+    return p.parse_args()
+
+
+def _map_dtype(arg: str) -> torch.dtype:
+    return {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[arg]
+
+
+def _estimate_moe_flops(tokens: int, hidden: int, inter: int, top_k: int) -> float:
+    # Two up projections + one down projection per expert/token combination.
+    return 6.0 * tokens * top_k * hidden * inter
+
+
+def _prepare_module(
+    moe: MoE,
+    *,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> MoE:
+    moe = moe.to(device=device)
+    for param in moe.parameters():
+        param.data = param.data.to(dtype)
+        if param.grad is not None:
+            param.grad = None
+
+    buffers = dict(moe.named_buffers())
+    for name, buf in buffers.items():
+        if name == "tokens_per_expert":
+            moe._buffers[name] = torch.zeros_like(
+                buf, dtype=torch.float32, device=device
+            )
+        elif name == "expert_bias" and buf is not None:
+            moe._buffers[name] = torch.zeros_like(
+                buf, dtype=torch.float32, device=device
+            )
+        else:
+            moe._buffers[name] = buf.to(device=device, dtype=dtype)
+    moe.eval()
+    return moe
+
+
+@torch.inference_mode()
+def _forward_fn(module: MoE, x: torch.Tensor) -> torch.Tensor:
+    return module(x)
+
+
+def _bench(fn, *, iters: int, warmup: int, sync: bool = True) -> float:
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    for _ in range(warmup):
+        fn()
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+    times = []
+    for _ in range(iters):
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+        start = time.perf_counter()
+        fn()
+        if sync and device.type == "cuda":
+            torch.cuda.synchronize()
+        times.append((time.perf_counter() - start) * 1000.0)
+    return sum(times) / len(times)
+
+
+def main() -> None:
+    args = _parse_args()
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    dtype = _map_dtype(args.dtype)
+
+    torch.manual_seed(0)
+    if device.type == "cuda":
+        torch.cuda.manual_seed(0)
+
+    moe_args_grouped = MoEArgs(
+        num_experts=args.experts,
+        num_shared_experts=0,
+        score_func=args.score_func,
+        route_norm=args.route_norm,
+        top_k=args.top_k,
+        use_grouped_mm=True,
+        score_before_experts=args.score_before,
+        load_balance_coeff=None,
+    )
+    moe_grouped = MoE(moe_args_grouped, dim=args.hidden, hidden_dim=args.inter)
+    moe_grouped.init_weights(args.init_std, buffer_device=device)
+
+    moe_args_naive = MoEArgs(
+        num_experts=args.experts,
+        num_shared_experts=0,
+        score_func=args.score_func,
+        route_norm=args.route_norm,
+        top_k=args.top_k,
+        use_grouped_mm=False,
+        score_before_experts=args.score_before,
+        load_balance_coeff=None,
+    )
+    moe_naive = MoE(moe_args_naive, dim=args.hidden, hidden_dim=args.inter)
+    moe_naive.load_state_dict(moe_grouped.state_dict(), strict=True)
+
+    moe_grouped = _prepare_module(moe_grouped, device=device, dtype=dtype)
+    moe_naive = _prepare_module(moe_naive, device=device, dtype=dtype)
+
+    x = torch.randn(args.bsz, args.seq, args.hidden, device=device, dtype=dtype)
+
+    tokens = args.bsz * args.seq
+    print(
+        f"Device={device} dtype={dtype} tokens={tokens} hidden={args.hidden} "
+        f"inter={args.inter} experts={args.experts} top_k={args.top_k}"
+    )
+
+    def run_naive():
+        return _forward_fn(moe_naive, x)
+
+    def run_grouped():
+        return _forward_fn(moe_grouped, x)
+
+    if hasattr(moe_naive, "tokens_per_expert"):
+        moe_naive.tokens_per_expert.zero_()
+    if hasattr(moe_grouped, "tokens_per_expert"):
+        moe_grouped.tokens_per_expert.zero_()
+
+    t_naive = _bench(run_naive, iters=args.iters, warmup=args.warmup)
+    flops = _estimate_moe_flops(tokens, args.hidden, args.inter, args.top_k)
+    tflops_naive = flops / ((t_naive / 1000.0) * 1e12)
+    print(
+        f"naive\t{t_naive:.2f} ms\t{tokens / (t_naive / 1000.0):.1f} tok/s\t"
+        f"{tflops_naive:.2f} TFLOP/s"
+    )
+
+    y_naive = run_naive()
+
+    if hasattr(moe_grouped, "tokens_per_expert"):
+        moe_grouped.tokens_per_expert.zero_()
+
+    t_grouped = _bench(run_grouped, iters=args.iters, warmup=args.warmup)
+    tflops_grouped = flops / ((t_grouped / 1000.0) * 1e12)
+    speedup = t_naive / t_grouped if t_grouped > 0 else float("nan")
+    print(
+        f"grouped\t{t_grouped:.2f} ms\t{tokens / (t_grouped / 1000.0):.1f} tok/s\t"
+        f"{tflops_grouped:.2f} TFLOP/s\t{speedup:.2f}×"
+    )
+
+    y_grouped = run_grouped()
+    diff = (y_naive.float() - y_grouped.float()).abs()
+    max_abs = diff.max().item()
+    mean_abs = diff.mean().item()
+    rel_l2 = (diff.pow(2).sum() / (y_naive.float().pow(2).sum() + 1e-12)).sqrt().item()
+    print(
+        f"grouped_check: max_abs={max_abs:.3e} mean_abs={mean_abs:.3e} rel_l2={rel_l2:.3e}"
+    )
+
+
+if __name__ == "__main__":
+    main()

scripts/bench_torchtitan_moe_sweep.py

@@ -0,0 +1,328 @@
+#!/usr/bin/env python
+"""Sweep Torchtitan MoE grouped vs naive configurations and report performance."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, List
+
+import torch
+
+_PROJECT_ROOT = Path(__file__).resolve().parents[2]
+_TITAN_PATH = _PROJECT_ROOT / "torchtitan"
+if str(_TITAN_PATH) not in sys.path:
+    sys.path.append(str(_TITAN_PATH))
+
+from torchtitan.models.moe import MoE, MoEArgs
+
+
+def _parse_int_list(value: str) -> List[int]:
+    return [int(v) for v in value.split(",") if v]
+
+
+def _parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Torchtitan MoE grouped vs naive sweep")
+    p.add_argument(
+        "--batch-sizes", default="4,8,16", help="Comma separated batch sizes"
+    )
+    p.add_argument(
+        "--seq-lens", default="1024,2048", help="Comma separated sequence lengths"
+    )
+    p.add_argument(
+        "--experts", default="8,16,32,64", help="Comma separated expert counts"
+    )
+    p.add_argument("--top-ks", default="1,2,4", help="Comma separated top_k choices")
+    p.add_argument("--hidden", type=int, default=4096)
+    p.add_argument("--inter", type=int, default=14336)
+    p.add_argument("--dtype", choices=["bf16", "fp16", "fp32"], default="bf16")
+    p.add_argument("--iters", type=int, default=25)
+    p.add_argument("--warmup", type=int, default=5)
+    p.add_argument("--init-std", type=float, default=0.02)
+    p.add_argument("--score-before", action="store_true")
+    p.add_argument("--score-func", choices=["softmax", "sigmoid"], default="softmax")
+    p.add_argument("--route-norm", action="store_true")
+    p.add_argument("--csv", type=Path, default=None, help="Optional CSV output path")
+    return p.parse_args()
+
+
+def _map_dtype(arg: str) -> torch.dtype:
+    return {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[arg]
+
+
+def _estimate_flops(tokens: int, hidden: int, inter: int, top_k: int) -> float:
+    return 6.0 * tokens * top_k * hidden * inter
+
+
+def _prepare_module(module: MoE, *, device: torch.device, dtype: torch.dtype) -> MoE:
+    module = module.to(device=device)
+    for param in module.parameters():
+        param.data = param.data.to(dtype)
+        if param.grad is not None:
+            param.grad = None
+    for name, buf in module.named_buffers():
+        if name == "tokens_per_expert":
+            module._buffers[name] = torch.zeros_like(
+                buf, dtype=torch.float32, device=device
+            )
+        elif name == "expert_bias" and buf is not None:
+            module._buffers[name] = torch.zeros_like(
+                buf, dtype=torch.float32, device=device
+            )
+        else:
+            module._buffers[name] = buf.to(device=device, dtype=dtype)
+    module.eval()
+    return module
+
+
+@torch.inference_mode()
+def _forward(module: MoE, x: torch.Tensor) -> torch.Tensor:
+    return module(x)
+
+
+def _bench(callable_, *, iters: int, warmup: int, device: torch.device) -> float:
+    for _ in range(warmup):
+        callable_()
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+    timings: List[float] = []
+    for _ in range(iters):
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        start = time.perf_counter()
+        callable_()
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        timings.append((time.perf_counter() - start) * 1000.0)
+    return sum(timings) / len(timings)
+
+
+@dataclass
+class SweepResult:
+    bsz: int
+    seq: int
+    experts: int
+    top_k: int
+    dtype: str
+    naive_ms: float
+    grouped_ms: float
+    speedup: float
+    naive_tflops: float
+    grouped_tflops: float
+    max_abs: float
+    mean_abs: float
+    rel_l2: float
+
+
+def _run_case(
+    *,
+    bsz: int,
+    seq: int,
+    experts: int,
+    top_k: int,
+    hidden: int,
+    inter: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    iters: int,
+    warmup: int,
+    init_std: float,
+    score_before: bool,
+    score_func: str,
+    route_norm: bool,
+) -> SweepResult:
+    torch.manual_seed(0)
+    if device.type == "cuda":
+        torch.cuda.manual_seed(0)
+
+    moe_args_grouped = MoEArgs(
+        num_experts=experts,
+        num_shared_experts=0,
+        score_func=score_func,
+        route_norm=route_norm,
+        top_k=top_k,
+        use_grouped_mm=True,
+        score_before_experts=score_before,
+        load_balance_coeff=None,
+    )
+    moe_grouped = MoE(moe_args_grouped, dim=hidden, hidden_dim=inter)
+    moe_grouped.init_weights(init_std, buffer_device=device)
+
+    moe_args_naive = MoEArgs(
+        num_experts=experts,
+        num_shared_experts=0,
+        score_func=score_func,
+        route_norm=route_norm,
+        top_k=top_k,
+        use_grouped_mm=False,
+        score_before_experts=score_before,
+        load_balance_coeff=None,
+    )
+    moe_naive = MoE(moe_args_naive, dim=hidden, hidden_dim=inter)
+    moe_naive.load_state_dict(moe_grouped.state_dict(), strict=True)
+
+    moe_grouped = _prepare_module(moe_grouped, device=device, dtype=dtype)
+    moe_naive = _prepare_module(moe_naive, device=device, dtype=dtype)
+
+    x = torch.randn(bsz, seq, hidden, device=device, dtype=dtype)
+
+    def run_naive():
+        if hasattr(moe_naive, "tokens_per_expert"):
+            moe_naive.tokens_per_expert.zero_()
+        return _forward(moe_naive, x)
+
+    def run_grouped():
+        if hasattr(moe_grouped, "tokens_per_expert"):
+            moe_grouped.tokens_per_expert.zero_()
+        return _forward(moe_grouped, x)
+
+    naive_ms = _bench(run_naive, iters=iters, warmup=warmup, device=device)
+    y_naive = run_naive()
+
+    grouped_ms = _bench(run_grouped, iters=iters, warmup=warmup, device=device)
+    y_grouped = run_grouped()
+
+    diff = (y_naive.float() - y_grouped.float()).abs()
+    max_abs = diff.max().item()
+    mean_abs = diff.mean().item()
+    rel_l2 = (diff.pow(2).sum() / (y_naive.float().pow(2).sum() + 1e-12)).sqrt().item()
+
+    tokens = bsz * seq
+    flops = _estimate_flops(tokens, hidden, inter, top_k)
+    naive_tflops = flops / ((naive_ms / 1000.0) * 1e12)
+    grouped_tflops = flops / ((grouped_ms / 1000.0) * 1e12)
+    speedup = naive_ms / grouped_ms if grouped_ms > 0 else float("nan")
+
+    return SweepResult(
+        bsz=bsz,
+        seq=seq,
+        experts=experts,
+        top_k=top_k,
+        dtype=str(dtype),
+        naive_ms=naive_ms,
+        grouped_ms=grouped_ms,
+        speedup=speedup,
+        naive_tflops=naive_tflops,
+        grouped_tflops=grouped_tflops,
+        max_abs=max_abs,
+        mean_abs=mean_abs,
+        rel_l2=rel_l2,
+    )
+
+
+def _print_header(
+    hidden: int, inter: int, dtype: torch.dtype, device: torch.device
+) -> None:
+    print(f"Device={device} dtype={dtype} hidden={hidden} inter={inter}")
+    print(
+        "bsz\tseq\texperts\ttop_k\tnaive(ms)\tgrouped(ms)\tspeedup\t"
+        "naive TF/s\tgrouped TF/s\tmax_abs\tmean_abs\trel_l2"
+    )
+
+
+def _print_result(res: SweepResult) -> None:
+    print(
+        f"{res.bsz}\t{res.seq}\t{res.experts}\t{res.top_k}\t"
+        f"{res.naive_ms:.2f}\t{res.grouped_ms:.2f}\t{res.speedup:.2f}\t"
+        f"{res.naive_tflops:.2f}\t{res.grouped_tflops:.2f}\t"
+        f"{res.max_abs:.2e}\t{res.mean_abs:.2e}\t{res.rel_l2:.2e}"
+    )
+
+
+def _write_csv(path: Path, results: Iterable[SweepResult]) -> None:
+    fieldnames = [
+        "batch_size",
+        "seq_len",
+        "experts",
+        "top_k",
+        "dtype",
+        "naive_ms",
+        "grouped_ms",
+        "speedup",
+        "naive_tflops",
+        "grouped_tflops",
+        "max_abs",
+        "mean_abs",
+        "rel_l2",
+    ]
+    with path.open("w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        for r in results:
+            writer.writerow(
+                {
+                    "batch_size": r.bsz,
+                    "seq_len": r.seq,
+                    "experts": r.experts,
+                    "top_k": r.top_k,
+                    "dtype": r.dtype,
+                    "naive_ms": f"{r.naive_ms:.4f}",
+                    "grouped_ms": f"{r.grouped_ms:.4f}",
+                    "speedup": f"{r.speedup:.4f}",
+                    "naive_tflops": f"{r.naive_tflops:.4f}",
+                    "grouped_tflops": f"{r.grouped_tflops:.4f}",
+                    "max_abs": f"{r.max_abs:.6e}",
+                    "mean_abs": f"{r.mean_abs:.6e}",
+                    "rel_l2": f"{r.rel_l2:.6e}",
+                }
+            )
+
+
+def main() -> None:
+    args = _parse_args()
+    dtype = _map_dtype(args.dtype)
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+    batch_sizes = _parse_int_list(args.batch_sizes)
+    seq_lens = _parse_int_list(args.seq_lens)
+    experts_list = _parse_int_list(args.experts)
+    top_ks = _parse_int_list(args.top_ks)
+
+    results: List[SweepResult] = []
+    _print_header(args.hidden, args.inter, dtype, device)
+
+    for bsz in batch_sizes:
+        for seq in seq_lens:
+            for experts in experts_list:
+                for top_k in top_ks:
+                    try:
+                        res = _run_case(
+                            bsz=bsz,
+                            seq=seq,
+                            experts=experts,
+                            top_k=top_k,
+                            hidden=args.hidden,
+                            inter=args.inter,
+                            dtype=dtype,
+                            device=device,
+                            iters=args.iters,
+                            warmup=args.warmup,
+                            init_std=args.init_std,
+                            score_before=args.score_before,
+                            score_func=args.score_func,
+                            route_norm=args.route_norm,
+                        )
+                    except RuntimeError as err:
+                        print(
+                            f"{bsz}\t{seq}\t{experts}\t{top_k}\tERROR: {err}",
+                            file=sys.stderr,
+                        )
+                        continue
+                    results.append(res)
+                    _print_result(res)
+
+    if args.csv and results:
+        _write_csv(args.csv, results)
+        print(f"Wrote {len(results)} rows to {args.csv}")
+
+
+if __name__ == "__main__":
+    main()

scripts/chat_datasets.py

@@ -27,7 +27,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not field_messages:
         raise ValueError(
-            f'No conversation field found in dataset: {", ".join(feature_keys)}'
+            f"No conversation field found in dataset: {', '.join(feature_keys)}"
         )
     ds_cfg["field_messages"] = field_messages
 
@@ -40,7 +40,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not message_property_mappings["role"]:
         raise ValueError(
-            f'No role field found in messages: {", ".join(message_fields)}'
+            f"No role field found in messages: {', '.join(message_fields)}"
         )
 
     for key in ["content", "text", "value"]:
@@ -49,7 +49,7 @@ def parse_dataset(dataset=None, split="train"):
             break
     if not message_property_mappings["content"]:
         raise ValueError(
-            f'No content field found in messages: {", ".join(message_fields)}'
+            f"No content field found in messages: {', '.join(message_fields)}"
         )
     ds_cfg["message_property_mappings"] = message_property_mappings
 

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@0ee9ee8"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@c6a32c5"'
 )

scripts/debug_qwen2_experts.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+"""Inspect Qwen2 MoE expert implementations for grouped-mm debugging."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import torch
+
+ROOT = Path(__file__).resolve().parents[2]
+sys.path.extend(
+    [
+        str(ROOT / "transformers" / "src"),
+        str(ROOT / "src"),
+    ]
+)
+
+from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
+from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
+
+from axolotl.kernels.moe.torch_grouped import _iter_expert_impls
+
+
+def main() -> None:
+    cfg = Qwen2MoeConfig(
+        hidden_size=4096,
+        moe_intermediate_size=14336,
+        shared_expert_intermediate_size=14336,
+        num_experts=32,
+        num_experts_per_tok=4,
+    )
+
+    block = Qwen2MoeSparseMoeBlock(cfg).to("cuda", dtype=torch.bfloat16)
+    experts = block.experts
+    experts._ax_parent_block = block
+
+    impls = _iter_expert_impls(experts)
+    print(f"impl count: {len(impls)}")
+    for idx, impl in enumerate(impls[:8]):
+        has_gate = hasattr(impl, "gate_proj")
+        has_up = hasattr(impl, "up_proj")
+        print(
+            f"impl[{idx}] type={impl.__class__.__name__} has_gate={has_gate} has_up={has_up}"
+        )
+        if has_gate:
+            print(f"  gate shape {tuple(impl.gate_proj.weight.shape)}")
+            print(f"  up shape   {tuple(impl.up_proj.weight.shape)}")
+            print(f"  down shape {tuple(impl.down_proj.weight.shape)}")
+
+
+if __name__ == "__main__":
+    main()

scripts/probe_torch_grouped_ops.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+"""
+Probe PyTorch for grouped GEMM operator names and namespaces.
+Run: python scripts/probe_torch_grouped_ops.py
+"""
+
+import sys
+
+
+def main():
+    try:
+        import torch
+    except Exception as e:
+        print("Failed to import torch:", e)
+        sys.exit(1)
+
+    print("torch version:", torch.__version__)
+    namespaces = [n for n in dir(torch.ops) if not n.startswith("_")]
+    print("ops namespaces:", namespaces)
+
+    found_any = False
+    for ns in namespaces:
+        obj = getattr(torch.ops, ns, None)
+        ops = []
+        if obj is not None:
+            try:
+                ops = dir(obj)
+            except Exception as e:
+                print(f"warning: failed to list ops for namespace {ns}: {e}")
+        cands = [
+            o
+            for o in ops
+            if ("group" in o.lower())
+            or ("mm_grouped" in o.lower())
+            or ("matmul_grouped" in o.lower())
+            or ("grouped" in o.lower())
+        ]
+        if cands:
+            found_any = True
+            print(f"namespace {ns} candidates:", cands)
+
+    if not found_any:
+        print("No grouped GEMM candidates found. PyTorch >= 2.8 is recommended.")
+
+
+if __name__ == "__main__":
+    main()

scripts/unsloth_install.py

@@ -1,11 +1,10 @@
 # noqa
-# pylint: skip-file
 import sys
 
 try:
     import torch
-except ImportError:
-    raise ImportError("Install torch via `pip install torch`")
+except ImportError as error:
+    raise ImportError("Install torch via `pip install torch`") from error
 from packaging.version import Version as V
 
 use_uv = "--uv" in sys.argv[1:]

setup.py

@@ -64,7 +64,9 @@ def parse_requirements(extras_require_map):
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 7):
+            if (major, minor) >= (2, 8):
+                pass
+            elif (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:
                     _install_requires.append("xformers==0.0.30")
@@ -125,7 +127,7 @@ extras_require = {
         "yunchang==0.6.0",
     ],
     "deepspeed": [
-        "deepspeed==0.17.2",
+        "deepspeed==0.17.5",
         "deepspeed-kernels",
     ],
     "mamba-ssm": [
@@ -160,6 +162,7 @@ extras_require = {
     "llmcompressor": [
         "llmcompressor==0.5.1",
     ],
+    "fbgemm-gpu": ["fbgemm-gpu-genai>=1.2.0"],
 }
 install_requires, dependency_links, extras_require_build = parse_requirements(
     extras_require

src/axolotl/cli/__init__.py

@@ -4,5 +4,7 @@ import os
 
 from axolotl.logging_config import configure_logging
 
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
+
 configure_logging()

src/axolotl/cli/args.py

@@ -17,9 +17,9 @@ class PreprocessCliArgs:
         default=False,
         metadata={
             "help": (
-                "[DEPRECATED] No longer supported. For streaming datasets, use "
-                "'axolotl train' and set 'streaming: true' in your YAML config, or "
-                "pass --streaming instead in the CLI."
+                "Deprecated in v0.13.0, will be removed in v0.14.0. For streaming "
+                "datasets, use 'axolotl train' and set 'streaming: true' in your YAML "
+                "config, or pass --streaming instead in the CLI."
             )
         },
     )
@@ -115,6 +115,7 @@ class QuantizeCliArgs:
     quantize_embedding: Optional[bool] = field(default=None)
     group_size: Optional[int] = field(default=None)
     output_dir: Optional[str] = field(default=None)
+    hub_model_id: Optional[str] = field(default=None)
 
 
 @dataclass

src/axolotl/cli/art.py

@@ -22,7 +22,7 @@ HAS_PRINTED_LOGO = False
 def print_axolotl_text_art():
     """Prints axolotl ASCII art."""
 
-    global HAS_PRINTED_LOGO  # pylint: disable=global-statement
+    global HAS_PRINTED_LOGO
     if HAS_PRINTED_LOGO:
         return
     if is_main_process():

src/axolotl/cli/cloud/__init__.py

@@ -7,6 +7,8 @@ from typing import Literal
 
 import yaml
 
+from axolotl.cli.cloud.base import Cloud
+from axolotl.cli.cloud.baseten import BasetenCloud
 from axolotl.cli.cloud.modal_ import ModalCloud
 from axolotl.utils.dict import DictDefault
 
@@ -38,8 +40,15 @@ def do_cli_train(
     cwd=None,
     **kwargs,
 ) -> None:
-    cloud_cfg = load_cloud_cfg(cloud_config)
-    cloud = ModalCloud(cloud_cfg)
+    cloud_cfg: DictDefault = load_cloud_cfg(cloud_config)
+    provider = cloud_cfg.provider or "modal"
+    cloud: Cloud | None
+    if provider == "modal":
+        cloud = ModalCloud(cloud_cfg)
+    elif provider == "baseten":
+        cloud = BasetenCloud(cloud_cfg.to_dict())
+    else:
+        raise ValueError(f"Unsupported cloud provider: {provider}")
     with open(config, "r", encoding="utf-8") as file:
         config_yaml = file.read()
     local_dirs = {}

src/axolotl/cli/cloud/baseten/__init__.py

@@ -0,0 +1,48 @@
+"""Baseten Cloud CLI"""
+
+import shutil
+import subprocess  # nosec B404
+import tempfile
+from os.path import dirname
+from typing import Literal
+
+import yaml
+
+from axolotl.cli.cloud.base import Cloud
+
+
+class BasetenCloud(Cloud):
+    """Baseten Cloud Axolotl CLI"""
+
+    def __init__(self, config: dict):
+        self.config = config
+
+    def preprocess(self, config_yaml: str, *args, **kwargs) -> None:
+        raise NotImplementedError(
+            "Separate preprocess function for Baseten is not "
+            "implemented and will happen during hte train step."
+        )
+
+    def train(
+        self,
+        config_yaml: str,
+        launcher: Literal["accelerate", "torchrun", "python"] = "accelerate",
+        launcher_args: list[str] | None = None,
+        local_dirs: dict[str, str] | None = None,  # pylint: disable=unused-argument
+        **kwargs,
+    ):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            config = self.config.copy()
+            config["launcher"] = launcher
+            config["launcher_args"] = launcher_args
+            with open(tmp_dir + "/cloud.yaml", "w", encoding="utf-8") as cloud_fout:
+                yaml.dump(config, cloud_fout)
+            with open(tmp_dir + "/train.yaml", "w", encoding="utf-8") as config_fout:
+                config_fout.write(config_yaml)
+            shutil.copyfile(dirname(__file__) + "/template/run.sh", tmp_dir + "/run.sh")
+            shutil.copyfile(
+                dirname(__file__) + "/template/train_sft.py", tmp_dir + "/train_sft.py"
+            )
+            subprocess.run(  # nosec B603 B607
+                ["truss", "train", "push", "train_sft.py"], cwd=tmp_dir, check=False
+            )

src/axolotl/cli/cloud/baseten/template/run.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+set -eux
+
+export NCCL_SOCKET_IFNAME="^docker0,lo"
+export NCCL_IB_DISABLE=0
+export NCCL_TIMEOUT=1800000
+
+axolotl preprocess train.yaml
+axolotl train train.yaml --launcher ${AXOLOTL_LAUNCHER} ${AXOLOTL_LAUNCHER_ARGS}

src/axolotl/cli/cloud/baseten/template/train_sft.py

@@ -0,0 +1,71 @@
+"""
+Baseten Training Script for Axolotl
+"""
+
+# pylint: skip-file
+import yaml
+from truss.base import truss_config
+
+# Import necessary classes from the Baseten Training SDK
+from truss_train import definitions
+
+cloud_config = yaml.safe_load(open("cloud.yaml", "r"))
+gpu = cloud_config.get("gpu", "h100")
+gpu_count = int(cloud_config.get("gpu_count", 1))
+node_count = int(cloud_config.get("node_count", 1))
+project_name = cloud_config.get("project_name", "axolotl-project") or "axolotl-project"
+secrets = cloud_config.get("secrets", [])
+launcher = cloud_config.get("launcher", "accelerate")
+launcher_args = cloud_config.get("launcher_args", [])
+script_name = "run.sh"
+
+launcher_args_str = ""
+if launcher_args:
+    launcher_args_str = "-- " + " ".join(launcher_args)
+
+# 1. Define a base image for your training job
+# must use torch 2.7.0 for vllm
+BASE_IMAGE = "axolotlai/axolotl:main-py3.11-cu126-2.7.1"
+
+# 2. Define the Runtime Environment for the Training Job
+# This includes start commands and environment variables.a
+# Secrets from the baseten workspace like API keys are referenced using
+# `SecretReference`.
+
+env_vars = {
+    "AXOLOTL_LAUNCHER": launcher,
+    "AXOLOTL_LAUNCHER_ARGS": launcher_args_str,
+}
+for secret_name in secrets:
+    env_vars[secret_name] = definitions.SecretReference(name=secret_name)
+
+training_runtime = definitions.Runtime(
+    start_commands=[  # Example: list of commands to run your training script
+        f"/bin/sh -c 'chmod +x ./{script_name} && ./{script_name}'"
+    ],
+    environment_variables=env_vars,
+)
+
+# 3. Define the Compute Resources for the Training Job
+training_compute = definitions.Compute(
+    node_count=node_count,
+    accelerator=truss_config.AcceleratorSpec(
+        accelerator=truss_config.Accelerator.H100,
+        count=gpu_count,
+    ),
+)
+
+# 4. Define the Training Job
+# This brings together the image, compute, and runtime configurations.
+my_training_job = definitions.TrainingJob(
+    image=definitions.Image(base_image=BASE_IMAGE),
+    compute=training_compute,
+    runtime=training_runtime,
+)
+
+
+# This config will be pushed using the Truss CLI.
+# The association of the job to the project happens at the time of push.
+first_project_with_job = definitions.TrainingProject(
+    name=project_name, job=my_training_job
+)

src/axolotl/cli/cloud/modal_.py

@@ -41,7 +41,7 @@ def run_cmd(cmd: str, run_folder: str, volumes=None):
     if exit_code := subprocess.call(  # nosec B603
         cmd.split(), cwd=run_folder, env=new_env
     ):
-        exit(exit_code)  # pylint: disable=consider-using-sys-exit
+        exit(exit_code)
 
     # Commit writes to volume.
     if volumes:
@@ -130,7 +130,6 @@ class ModalCloud(Cloud):
         res = []
         if self.config.secrets:
             for key in self.config.get("secrets", []):
-                # pylint: disable=duplicate-code
                 if isinstance(key, str):
                     if val := os.environ.get(key, ""):
                         res.append(modal.Secret.from_dict({key: val}))
@@ -177,8 +176,8 @@ class ModalCloud(Cloud):
             with self.app.run(detach=True):
                 modal_fn.remote(
                     config_yaml,
-                    volumes={k: v[0] for k, v in self.volumes.items()},
                     *args,
+                    volumes={k: v[0] for k, v in self.volumes.items()},
                     **kwargs,
                 )
 
@@ -187,7 +186,7 @@ class ModalCloud(Cloud):
             return int(self.config.timeout)
         return 60 * 60 * 24  # 24 hours
 
-    def get_train_gpu(self):  # pylint: disable=too-many-return-statements
+    def get_train_gpu(self):
         count = self.config.gpu_count or 1
         family = self.config.gpu.lower() or "l40s"
 
@@ -277,7 +276,7 @@ def _train(
     launcher: Literal["accelerate", "torchrun", "python"] = "accelerate",
     launcher_args: list[str] | None = None,
     volumes=None,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ):
     Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:

src/axolotl/cli/config.py

@@ -23,7 +23,8 @@ from axolotl.utils.config import (
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
+from axolotl.utils.tee import prepare_debug_log
+from axolotl.utils.trainer import prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 LOG = get_logger(__name__)
@@ -210,7 +211,7 @@ def load_cfg(
     try:
         device_props = torch.cuda.get_device_properties("cuda")
         gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
-    except:  # pylint: disable=bare-except # noqa: E722
+    except:
         gpu_version = None
 
     prepare_plugins(cfg)
@@ -227,8 +228,11 @@ def load_cfg(
         },
     )
 
+    # NOTE(djsaunde): We start outputting to output_dir/debug.log at this point since we
+    # have to wait for cfg.output to be resolved. We could call this earlier if we write
+    # to a temporary file, and then move it later.
+    prepare_debug_log(cfg)
     prepare_optim_env(cfg)
-    prepare_opinionated_env(cfg)
     normalize_config(cfg)
     normalize_cfg_datasets(cfg)
     setup_wandb_env_vars(cfg)
@@ -241,7 +245,6 @@ def load_cfg(
         for k, v in cfg.items()
         if v is not None
     }
-
     LOG.info(
         "config:\n%s",
         json.dumps(cfg_to_log, indent=2, default=str, sort_keys=True),

src/axolotl/cli/evaluate.py

@@ -28,7 +28,7 @@ def do_evaluate(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
         cfg: Dictionary mapping `axolotl` config keys to values.
         cli_args: CLI arguments.
     """
-    # pylint: disable=duplicate-code
+
     check_accelerate_default_config()
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()
@@ -49,7 +49,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, **kwargs)
     parser = HfArgumentParser(TrainerCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/inference.py

@@ -14,10 +14,12 @@ from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 from axolotl.cli.args import InferenceCliArgs
 from axolotl.cli.config import load_cfg
 from axolotl.cli.utils import load_model_and_tokenizer
-from axolotl.utils.chat_templates import (
-    get_chat_template,
-    get_chat_template_from_config,
+from axolotl.cli.utils.diffusion import (
+    diffusion_inference,
+    launch_diffusion_gradio_ui,
 )
+from axolotl.integrations.base import PluginManager
+from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.logging import get_logger
 
@@ -32,10 +34,11 @@ def get_multi_line_input() -> str:
         Possibly multi-line, possibly empty stdin input as a string.
     """
     print("Give me an instruction (Ctrl + D to submit): ")
+    print("=" * 80)
 
     instruction = ""
     for line in sys.stdin:
-        instruction += line  # pylint: disable=consider-using-join
+        instruction += line
 
     return instruction
 
@@ -46,9 +49,9 @@ def do_inference(
     cli_args: InferenceCliArgs,
 ):
     """
-    Runs inference on the command line in a loop. User input is accepted, a chat template
-    is (optionally) applied, and the model specified in the `axolotl` config is used to
-    generate completions according to a default generation config.
+    Runs inference on the command line in a loop. User input is accepted, a chat
+    template is (optionally) applied, and the model specified in the `axolotl` config is
+    used to generate completions according to a default generation config.
 
     Args:
         cfg: Dictionary mapping `axolotl` config keys to values.
@@ -64,17 +67,31 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
-    elif cfg.datasets[0].type == "chat_template":
+        chat_template_str = get_chat_template_from_config(
+            cfg, ds_cfg=None, tokenizer=tokenizer
+        )
+    elif cfg.datasets and cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
         )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
+    # Detect diffusion mode
+    plugin_manager = PluginManager.get_instance()
+    is_diffusion = any(
+        plugin.__class__.__name__ == "DiffusionPlugin"
+        for plugin in plugin_manager.plugins.values()
+    )
+
+    if is_diffusion:
+        print("=" * 80)
+        print("Commands:")
+        print(":complete N -> completion mode with N tokens (default 64)")
+        print(":mask R     -> random masking with ratio R (0.0–1.0)")
+
     while True:
         print("=" * 80)
-        # support for multiline inputs
         instruction = get_multi_line_input()
         if not instruction:
             return
@@ -104,9 +121,19 @@ def do_inference(
         else:
             batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
-        print("=" * 40)
+        print("=" * 80)
         model.eval()
         with torch.no_grad():
+            if is_diffusion:
+                diffusion_inference(
+                    model=model,
+                    tokenizer=tokenizer,
+                    cfg=cfg,
+                    prompt=prompt,
+                    chat_template_str=chat_template_str,
+                )
+                continue
+
             generation_config = GenerationConfig(
                 repetition_penalty=1.1,
                 max_new_tokens=1024,
@@ -129,7 +156,7 @@ def do_inference(
                 generation_config=generation_config,
                 streamer=streamer,
             )
-        print("=" * 40)
+        print("=" * 80)
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
@@ -159,15 +186,37 @@ def do_inference_gradio(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
+        chat_template_str = get_chat_template_from_config(
+            cfg, ds_cfg=None, tokenizer=tokenizer
+        )
+    elif cfg.datasets and cfg.datasets[0].type == "chat_template":
+        chat_template_str = get_chat_template_from_config(
+            cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
+        )
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
+    # Detect diffusion mode
+    plugin_manager = PluginManager.get_instance()
+    is_diffusion = any(
+        plugin.__class__.__name__ == "DiffusionPlugin"
+        for plugin in plugin_manager.plugins.values()
+    )
+
+    if is_diffusion:
+        launch_diffusion_gradio_ui(
+            model=model,
+            tokenizer=tokenizer,
+            cfg=cfg,
+            prompter_module=prompter_module,
+            chat_template_str=chat_template_str,
+        )
+        return
+
     def generate(instruction):
         if not instruction:
             return
         if prompter_module:
-            # pylint: disable=stop-iteration-return
             prompt: str = next(
                 prompter_module().build_prompt(instruction=instruction.strip("\n"))
             )
@@ -252,7 +301,7 @@ def do_cli(
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, inference=True, rl=None, **kwargs)
     parsed_cfg.sample_packing = False
     parser = transformers.HfArgumentParser(InferenceCliArgs)

src/axolotl/cli/main.py

@@ -1,7 +1,5 @@
 """Click CLI definitions for various axolotl commands."""
 
-# pylint: disable=redefined-outer-name
-
 import os
 import subprocess  # nosec B404
 from typing import Literal, Optional
@@ -28,7 +26,7 @@ from axolotl.cli.utils import (
     launch_training,
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
-from axolotl.utils import patch_optimized_env
+from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.config import AxolotlInputConfig
 
@@ -46,7 +44,7 @@ def cli():
     """Axolotl CLI - Train and fine-tune large language models"""
     print_axolotl_text_art()
     load_dotenv()
-    patch_optimized_env()
+    set_pytorch_cuda_alloc_conf()
 
 
 @cli.command()

src/axolotl/cli/merge_lora.py

@@ -43,7 +43,10 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
             safe_serialization=safe_serialization,
             progressbar=True,
         )
-        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        tokenizer.save_pretrained(
+            str(Path(cfg.output_dir) / "merged"),
+            save_jinja_files=cfg.tokenizer_save_jinja_files,
+        )
 
         if processor:
             processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -32,7 +32,7 @@ LOG = get_logger(__name__)
 class BFloat16CastPlanner(_EmptyStateDictLoadPlanner):
     """A custom planner to cast tensors to bfloat16 on the fly during loading."""
 
-    def commit_tensor(self, read_item, tensor):  # pylint: disable=unused-argument
+    def commit_tensor(self, read_item, tensor):
         tensor.copy_(tensor.to(torch.bfloat16))
 
 
@@ -59,10 +59,10 @@ def _distributed_checkpoint_to_merged_weights(
     state_dict: Dict = {}
     save_path_ = Path(save_path)
     save_path_.mkdir(exist_ok=True)
-    dist_cp_format_utils._load_state_dict(  # pylint: disable=protected-access
+    dist_cp_format_utils._load_state_dict(
         state_dict,
         storage_reader=dist_cp.FileSystemReader(checkpoint_dir),
-        planner=BFloat16CastPlanner(),  # pylint: disable=protected-access
+        planner=BFloat16CastPlanner(),
         no_dist=True,
     )
 
@@ -191,7 +191,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     parsed_cfg = load_cfg(config, **kwargs)
 
     fsdp_dir = Path(parsed_cfg.output_dir) / "pytorch_model_fsdp_0"

src/axolotl/cli/preprocess.py

@@ -83,7 +83,7 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
                     AutoModelForCausalLM.from_pretrained(
                         model_name, trust_remote_code=True
                     )
-                except Exception as exc:  # pylint: disable=broad-exception-caught,unused-variable  # nosec B110  # noqa F841
+                except Exception:  # nosec B110
                     pass
                 # fmt: on
 
@@ -105,7 +105,7 @@ def do_cli(
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
+
     os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
     is_preprocess = kwargs.pop("is_preprocess", True)
     parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)

src/axolotl/cli/quantize.py

@@ -5,12 +5,17 @@ CLI to post-training quantize a model using torchao
 from pathlib import Path
 from typing import Union
 
-from transformers import AutoModelForCausalLM
+from transformers import AutoConfig, AutoModelForCausalLM, TorchAoConfig
 
 from axolotl.cli.config import load_cfg
 from axolotl.loaders import load_tokenizer
 from axolotl.utils.logging import get_logger
-from axolotl.utils.quantization import TorchIntDType, quantize_model_for_ptq
+from axolotl.utils.quantization import (
+    TorchAOQuantDType,
+    get_quantization_config,
+    quantization_config_to_str,
+    quantize_model,
+)
 
 LOG = get_logger(__name__)
 
@@ -43,13 +48,13 @@ def do_quantize(
             "No quantization configuration found. Please specify either qat or quantization in your config file."
         )
 
-    model_path = cli_args.get("model_path") or cfg.output_dir
+    model_path = cli_args.get("base_model") or cfg.output_dir
     if weight_dtype := cli_args.get("weight_dtype"):
-        weight_dtype = TorchIntDType[weight_dtype]
+        weight_dtype = TorchAOQuantDType.from_string(weight_dtype)
     else:
         weight_dtype = quantize_cfg.weight_dtype
     if activation_dtype := cli_args.get("activation_dtype"):
-        activation_dtype = TorchIntDType[activation_dtype]
+        activation_dtype = TorchAOQuantDType.from_string(activation_dtype)
     else:
         activation_dtype = quantize_cfg.activation_dtype
     group_size = cli_args.get("group_size") or quantize_cfg.group_size
@@ -57,10 +62,15 @@ def do_quantize(
         cli_args.get("quantize_embedding") or quantize_cfg.quantize_embedding
     )
     output_dir = cli_args.get("output_dir") or cfg.output_dir
+    hub_model_id = cli_args.get("hub_model_id") or cfg.hub_model_id
 
-    LOG.info(f"Loading model from {model_path}...")
+    LOG.info(f"Loading model from {model_path}.")
     tokenizer = load_tokenizer(cfg)
-    model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
+    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
+    )
 
     LOG.info(
         f"Quantizing model with configuration: \n"
@@ -70,11 +80,21 @@ def do_quantize(
         f"\tquantize_embedding: {quantize_embedding}"
     )
 
-    quantize_model_for_ptq(
+    quantize_model(
         model, weight_dtype, group_size, activation_dtype, quantize_embedding
     )
 
-    LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}...")
+    quantization_config = get_quantization_config(
+        weight_dtype, activation_dtype, group_size
+    )
+
+    ao_config = TorchAoConfig(
+        quant_type=quantization_config,
+        include_input_output_embeddings=quantize_embedding,
+    )
+    model.config.quantization_config = ao_config
+
+    LOG.info(f"Saving quantized model to: {str(Path(output_dir) / 'quantized')}.")
     model.save_pretrained(
         str(Path(output_dir) / "quantized"),
         safe_serialization=False,
@@ -84,5 +104,16 @@ def do_quantize(
         str(Path(output_dir) / "quantized"),
         safe_serialization=False,
         progressbar=True,
+        save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
-    LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}...")
+
+    if hub_model_id:
+        hub_model_id = (
+            hub_model_id.rstrip("-")
+            + f"-{quantization_config_to_str[type(quantization_config)]}"
+        )
+        model.push_to_hub(hub_model_id, safe_serialization=False)
+        tokenizer.push_to_hub(hub_model_id)
+        LOG.info(f"Quantized model pushed to: {hub_model_id}.")
+
+    LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}.")

src/axolotl/cli/train.py

@@ -17,6 +17,7 @@ from axolotl.integrations.base import PluginManager
 from axolotl.train import train
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.trainer import prepare_optim_env
 
 
 def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
@@ -59,7 +60,6 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         config: Path to `axolotl` config YAML file.
         kwargs: Additional keyword arguments to override config file values.
     """
-    # pylint: disable=duplicate-code
     parsed_cfg = load_cfg(config, **kwargs)
     parser = HfArgumentParser(TrainerCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(
@@ -92,6 +92,7 @@ def ray_train_func(kwargs: dict):
     # cast `cfg` back to DictDefault (ray tune deepcopy has issues with DictDefault so needed it to be dict)
     # also renormalize the config now that TorchTrainer has spawned distributed workers
     cfg = DictDefault(kwargs["cfg"])
+    prepare_optim_env(cfg)
     normalize_config(cfg)
 
     # now that we are on the worker node, we can check `is_torch_bf16_gpu_available` to resolve dtype

src/axolotl/cli/utils/args.py

@@ -65,7 +65,7 @@ def add_options_from_dataclass(config_class: Type[Any]) -> Callable:
         for field in reversed(dataclasses.fields(config_class)):
             field_type = _strip_optional_type(field.type)
 
-            if field_type == bool:
+            if field_type is bool:
                 field_name = field.name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"
                 function = click.option(
@@ -103,7 +103,7 @@ def add_options_from_config(config_class: Type[BaseModel]) -> Callable:
         for name, field in reversed(config_class.model_fields.items()):
             field_type = _strip_optional_type(field.annotation)
 
-            if field_type == bool:
+            if field_type is bool:
                 field_name = name.replace("_", "-")
                 option_name = f"--{field_name}/--no-{field_name}"
                 function = click.option(

src/axolotl/cli/utils/diffusion.py

@@ -0,0 +1,374 @@
+"""Helpers for diffusion-mode inference in CLI and Gradio."""
+
+from __future__ import annotations
+
+import gradio as gr
+from colorama import Fore, Style
+
+from axolotl.integrations.diffusion import generate, resolve_mask_token_id
+from axolotl.utils.dict import DictDefault
+
+
+def diffusion_inference(
+    model,
+    tokenizer,
+    cfg,
+    prompt: str,
+    chat_template_str: str | None = None,
+):
+    """Diffusion inference helper method."""
+    mode = "random"
+    completion_tokens = 0
+    target_mask_ratio = None
+    mode, completion_tokens, target_mask_ratio, cleaned = _parse_commands(prompt)
+
+    if cleaned:
+        prompt = cleaned
+
+    info = run_diffusion(
+        model=model,
+        tokenizer=tokenizer,
+        cfg=cfg,
+        prompt=prompt,
+        chat_template_str=chat_template_str,
+        mode=mode,
+        target_mask_ratio=target_mask_ratio,
+        completion_tokens=completion_tokens,
+    )
+    masked_text = info["masked_text"]
+    mask_ratio = info["mask_ratio"]
+    generated_ids = info["generated_ids"]
+    masked_positions = info["masked_positions"]
+    orig_ids = info["orig_ids"]
+
+    # Display with masked preview and colored diff
+    if masked_text is not None and mask_ratio is not None:
+        print(f"Masked ({mask_ratio:.1%}):\n{masked_text}\n")
+    if generated_ids is not None:
+        # Compute per-token style
+        styles: list[str] = []
+        for i, tid in enumerate(generated_ids):
+            if i in masked_positions:
+                if i < len(orig_ids) and tid == orig_ids[i]:
+                    styles.append("green")  # correct fill
+                elif i < len(orig_ids):
+                    styles.append("red")  # incorrect fill
+                else:
+                    styles.append("normal")  # appended
+            else:
+                same = i < len(orig_ids) and tid == orig_ids[i]
+                styles.append("dim" if same else "normal")
+
+        # Group contiguous spans by style
+        styled_spans: list[tuple[str, int, int]] = []
+        if generated_ids:
+            current_style = styles[0]
+            start = 0
+            for i in range(1, len(generated_ids)):
+                s = styles[i]
+                if s != current_style:
+                    styled_spans.append((current_style, start, i))
+                    current_style, start = s, i
+            styled_spans.append((current_style, start, len(generated_ids)))
+
+        out_parts = []
+        for style_name, a, b in styled_spans:
+            chunk_text = tokenizer.decode(generated_ids[a:b], skip_special_tokens=False)
+            if style_name == "green":
+                out_parts.append(Fore.GREEN + chunk_text + Style.RESET_ALL)
+            elif style_name == "red":
+                out_parts.append(Fore.RED + chunk_text + Style.RESET_ALL)
+            else:
+                if style_name == "dim":
+                    out_parts.append(Style.DIM + chunk_text + Style.RESET_ALL)
+                else:
+                    out_parts.append(chunk_text)
+        print("Generated:\n" + "".join(out_parts))
+    else:
+        print("Generated:\n(no output)")
+
+
+def _parse_commands(text: str):
+    """
+    Parse leading diffusion commands.
+
+    Supported at start of input (can be chained):
+      :complete N  -> completion mode with N tokens (default 64)
+      :mask R      -> random masking with ratio R in [0, 1]
+    """
+    tokens = text.strip().split()
+    i = 0
+    mode = "random"
+    completion_tokens = 0
+    target_mask_ratio = None
+    consumed = 0
+    while i < len(tokens) and tokens[i].startswith(":"):
+        cmd = tokens[i]
+        i += 1
+        consumed = i
+        if cmd == ":complete":
+            mode = "completion"
+            if i < len(tokens):
+                try:
+                    completion_tokens = int(tokens[i])
+                    i += 1
+                    consumed = i
+                except Exception:
+                    completion_tokens = 64
+            else:
+                completion_tokens = 64
+        elif cmd == ":mask":
+            mode = "random"
+            if i < len(tokens):
+                try:
+                    target_mask_ratio = float(tokens[i])
+                    i += 1
+                    consumed = i
+                except Exception:
+                    target_mask_ratio = None
+        else:
+            i -= 1
+            consumed = i
+            break
+
+    cleaned = " ".join(tokens[consumed:])
+
+    return mode, completion_tokens, target_mask_ratio, cleaned
+
+
+def run_diffusion(
+    *,
+    model,
+    tokenizer,
+    cfg: DictDefault,
+    prompt: str,
+    chat_template_str: str | None,
+    mode: str = "random",
+    target_mask_ratio: float | None = None,
+    completion_tokens: int = 0,
+):
+    """Run a single diffusion generation and return a structured result dict."""
+    if chat_template_str:
+        batch = tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            return_tensors="pt",
+            add_special_tokens=True,
+            add_generation_prompt=True,
+            chat_template=chat_template_str,
+            tokenize=True,
+            return_dict=True,
+        )
+    else:
+        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+    mask_token_id = resolve_mask_token_id(tokenizer, cfg, allow_add=False)
+
+    seq = batch["input_ids"].to(cfg.device)
+    gen_mode = "completion" if mode == "completion" else "random"
+    comp_tokens = int(completion_tokens) if gen_mode == "completion" else 0
+
+    result = generate(
+        model,
+        tokenizer,
+        original_sequence=seq[:1],
+        num_diffusion_steps=cfg.diffusion.num_diffusion_steps,
+        temperature=cfg.diffusion.generation_temperature,
+        mask_token_id=int(mask_token_id),
+        mode=gen_mode,  # type: ignore[arg-type]
+        completion_tokens=comp_tokens,
+        target_mask_ratio=target_mask_ratio,
+    )
+
+    masked_text = result.get("masked") if isinstance(result, dict) else None
+    mask_ratio = result.get("mask_ratio") if isinstance(result, dict) else None
+    generated_ids = result.get("generated_ids") if isinstance(result, dict) else None
+    masked_positions = (
+        set(result.get("masked_positions") or []) if isinstance(result, dict) else set()
+    )
+    orig_ids = seq[0].detach().cpu().tolist()
+
+    return {
+        "masked_text": masked_text,
+        "mask_ratio": mask_ratio,
+        "generated_ids": generated_ids,
+        "masked_positions": masked_positions,
+        "orig_ids": orig_ids,
+    }
+
+
+def render_html(
+    *,
+    generated_ids: list[int] | None,
+    orig_ids: list[int],
+    masked_positions: set[int],
+    tokenizer,
+) -> str:
+    """Render HTML visualizing diffusion outputs."""
+    if not generated_ids:
+        return "<pre>Generated:\n(no output)</pre>"
+
+    def _style_for(i: int, tid: int) -> str:
+        if i in masked_positions:
+            if i < len(orig_ids) and tid == orig_ids[i]:
+                return "green"
+            if i < len(orig_ids):
+                return "red"
+            return "normal"
+        same = i < len(orig_ids) and tid == orig_ids[i]
+        return "dim" if same else "normal"
+
+    # Group contiguous spans by style to reduce HTML size
+    spans: list[tuple[str, int, int]] = []
+    if generated_ids:
+        cur = _style_for(0, generated_ids[0])
+        start = 0
+        for i in range(1, len(generated_ids)):
+            s = _style_for(i, generated_ids[i])
+            if s != cur:
+                spans.append((cur, start, i))
+                cur, start = s, i
+        spans.append((cur, start, len(generated_ids)))
+
+    html_parts = []
+    for style_name, a, b in spans:
+        txt = tokenizer.decode(generated_ids[a:b], skip_special_tokens=False)
+        if style_name == "green":
+            html_parts.append(f'<span style="color:#2e7d32">{txt}</span>')
+        elif style_name == "red":
+            html_parts.append(f'<span style="color:#c62828">{txt}</span>')
+        elif style_name == "dim":
+            html_parts.append(f'<span style="opacity:0.6">{txt}</span>')
+        else:
+            html_parts.append(txt)
+
+    legend = (
+        '<div style="font-size:0.9em;margin-bottom:4px">'
+        '<span style="color:#2e7d32">correct</span>, '
+        '<span style="color:#c62828">incorrect</span>, '
+        '<span style="opacity:0.6">unchanged</span>'
+        "</div>"
+    )
+
+    return (
+        legend
+        + '<pre style="white-space:pre-wrap">Generated:\n'
+        + "".join(html_parts)
+        + "</pre>"
+    )
+
+
+def launch_diffusion_gradio_ui(
+    *,
+    model,
+    tokenizer,
+    cfg: DictDefault,
+    prompter_module=None,
+    chat_template_str: str | None = None,
+):
+    """Build and launch a simple Gradio UI for diffusion inference."""
+    with gr.Blocks(
+        title=cfg.get("gradio_title", "Axolotl Diffusion Interface")
+    ) as demo:
+        gr.Markdown(
+            """
+            ## Axolotl Diffusion Inference
+            - Mode "Random" masks tokens at a target ratio and fills them.
+            - Mode "Completion" appends N masked tokens at the end and fills them.
+            """
+        )
+
+        with gr.Row():
+            mode = gr.Radio(
+                choices=["random", "completion"],
+                value="random",
+                label="Mode",
+            )
+            mask_ratio = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.4,
+                label="Mask ratio (random mode)",
+                interactive=True,
+            )
+            completion_tokens = gr.Number(
+                value=64,
+                precision=0,
+                label="Completion tokens (completion mode)",
+                interactive=True,
+                visible=False,
+            )
+
+        instruction = gr.Textbox(label="Instruction", lines=6)
+        run_btn = gr.Button("Generate")
+
+        masked_preview = gr.Textbox(label="Masked preview", lines=6)
+        html_out = gr.HTML(label="Generated")
+
+        def _toggle_controls(selected_mode: str):
+            return (
+                gr.update(visible=(selected_mode == "random")),
+                gr.update(visible=(selected_mode == "completion")),
+            )
+
+        mode.change(
+            _toggle_controls,
+            inputs=[mode],
+            outputs=[mask_ratio, completion_tokens],
+        )
+
+        def _gen(instruction_text: str, selected_mode: str, mratio: float, ctoks: int):
+            if not instruction_text:
+                return "", "<pre>Generated:\n(no output)</pre>"
+
+            if prompter_module:
+                prompt: str = next(
+                    prompter_module().build_prompt(
+                        instruction=instruction_text.strip("\n")
+                    )
+                )
+            else:
+                prompt = instruction_text.strip()
+
+            info = run_diffusion(
+                model=model,
+                tokenizer=tokenizer,
+                cfg=cfg,
+                prompt=prompt,
+                chat_template_str=chat_template_str,
+                mode=selected_mode,
+                target_mask_ratio=mratio if selected_mode == "random" else None,
+                completion_tokens=int(ctoks) if selected_mode == "completion" else 0,
+            )
+
+            masked_text = info.get("masked_text")
+            mask_ratio_val = info.get("mask_ratio")
+            generated_ids = info.get("generated_ids")
+            masked_positions = info.get("masked_positions") or set()
+            orig_ids = info.get("orig_ids") or []
+
+            preview = (
+                f"Masked ({mask_ratio_val:.1%}):\n{masked_text}"
+                if masked_text is not None and mask_ratio_val is not None
+                else ""
+            )
+            html = render_html(
+                generated_ids=generated_ids,
+                orig_ids=orig_ids,
+                masked_positions=masked_positions,
+                tokenizer=tokenizer,
+            )
+            return preview, html
+
+        run_btn.click(
+            _gen,
+            inputs=[instruction, mode, mask_ratio, completion_tokens],
+            outputs=[masked_preview, html_out],
+        )
+
+        demo.queue().launch(
+            show_api=False,
+            share=cfg.get("gradio_share", True),
+            server_name=cfg.get("gradio_server_name", "127.0.0.1"),
+            server_port=cfg.get("gradio_server_port", None),
+        )

src/axolotl/cli/utils/sweeps.py

@@ -49,7 +49,10 @@ def generate_sweep_configs(
                 new_config = {}
                 # new_config = deepcopy(base_config)
                 # Combine regular parameters with paired parameters
-                full_combo = {**dict(zip(param_names, reg_combo)), **paired_set}
+                full_combo = {
+                    **dict(zip(param_names, reg_combo, strict=False)),
+                    **paired_set,
+                }
                 for param_name, param_value in full_combo.items():
                     new_config[param_name] = param_value
                 print(new_config)
@@ -58,7 +61,7 @@ def generate_sweep_configs(
             # If no paired values, just use regular combinations
             # new_config = deepcopy(base_config)
             new_config = {}
-            for param_name, param_value in zip(param_names, reg_combo):
+            for param_name, param_value in zip(param_names, reg_combo, strict=False):
                 new_config[param_name] = param_value
             print(new_config)
             all_combinations.append(new_config)

src/axolotl/cli/utils/train.py

@@ -95,7 +95,6 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str,
         permutation_id = f"sweep{idx:04d}"
         permutation["output_dir"] = str(permutation_dir / permutation_id)
 
-        # pylint: disable=consider-using-with
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",
             suffix=".yaml",

src/axolotl/cli/vllm_serve.py

@@ -39,7 +39,7 @@ def do_vllm_serve(
     model = cfg.base_model
 
     serve_module = cli_args.get("serve_module", "trl.scripts.vllm_serve")
-    vllm_serve_main = getattr(__import__(serve_module, fromlist=["main"]), "main")
+    vllm_serve_main = __import__(serve_module, fromlist=["main"]).main
     tensor_parallel_size = 1
     data_parallel_size = 1
 
@@ -68,7 +68,6 @@ def do_vllm_serve(
         cli_args.get("enable_reasoning") or cfg.vllm.enable_reasoning or False
     )
 
-    # pylint: disable=unexpected-keyword-arg
     vllm_script_args = AxolotlScriptArguments(
         model=model,
         tensor_parallel_size=tensor_parallel_size,

src/axolotl/common/datasets.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 
 from datasets import Dataset
 
-import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
+import axolotl.monkeypatch.data.batch_dataset_fetcher  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
 from axolotl.utils.data import prepare_datasets, prepare_preference_datasets

src/axolotl/convert.py

@@ -67,9 +67,7 @@ class JsonToJsonlConverter:
         self.json_parser = json_parser
         self.jsonl_serializer = jsonl_serializer
 
-    def convert(
-        self, input_file_path, output_file_path
-    ):  # pylint: disable=unused-argument
+    def convert(self, input_file_path, output_file_path):
         content = self.file_reader.read(input_file_path)
         data = self.json_parser.parse(content)
         # data = [r for r in data if r["conversations"]]  # vicuna cleaned has rows with empty conversations

src/axolotl/core/attention/flex_block_mask.py

@@ -84,9 +84,7 @@ def create_causal_mask(
     batch_size, dtype = input_embeds.shape[0], input_embeds.dtype
     if attention_mask is not None:
 
-        def causal_doc_mask_mod(
-            batch_idx, head_idx, q_idx, kv_idx
-        ):  # pylint: disable=unused-argument
+        def causal_doc_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
             """
             Defines the logic of a block causal mask by combining both a standard causal mask
             and a block diagonal document mask.
@@ -103,9 +101,7 @@ def create_causal_mask(
         mask_factory_function = causal_doc_mask_mod
     else:
         mask_factory_function = causal_mask_function
-    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[
-        config._attn_implementation  # pylint: disable=protected-access
-    ]
+    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[config._attn_implementation]
 
     # Do not allow skip if we are compiling (this is to match BC)
     allow_is_causal_skip = (

src/axolotl/core/builders/base.py

@@ -24,9 +24,7 @@ from pathlib import Path
 from typing import Any
 
 import torch
-from transformers import (
-    TrainerCallback,
-)
+from transformers import TrainerCallback
 from transformers.trainer_pt_utils import AcceleratorConfig
 
 from axolotl.integrations.base import PluginManager
@@ -44,7 +42,7 @@ from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 LOG = logging.getLogger(__name__)
 
 with suppress(ImportError):
-    import torch._dynamo  # pylint: disable=ungrouped-imports
+    import torch._dynamo
 
 
 class TrainerBuilderBase(abc.ABC):
@@ -260,14 +258,14 @@ class TrainerBuilderBase(abc.ABC):
                 adam_kwargs["eps"] = training_args_kwargs.get("adam_epsilon")
 
             if self.cfg.optimizer == "muon":
-                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
+                from axolotl.contribs.mit.muon import (
                     MuonOptimizerFactory,
                 )
 
                 optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "dion":
-                from axolotl.contribs.mit.dion import (  # pylint: disable=no-name-in-module
+                from axolotl.contribs.mit.dion import (
                     DionOptimizerFactory,
                 )
 
@@ -414,12 +412,8 @@ class TrainerBuilderBase(abc.ABC):
 
     def _configure_torch_compile(self, training_args_kwargs: dict):
         if self.cfg.torch_compile and getattr(torch, "_dynamo", None):
-            torch._dynamo.config.suppress_errors = (  # pylint: disable=protected-access
-                True
-            )
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
+            torch._dynamo.config.suppress_errors = True
+            torch._dynamo.config.accumulated_cache_size_limit = 256
             training_args_kwargs["torch_compile"] = self.cfg.torch_compile
             if self.cfg.torch_compile_backend:
                 training_args_kwargs["torch_compile_backend"] = (
@@ -441,7 +435,7 @@ class TrainerBuilderBase(abc.ABC):
             # don't use the HF gradient checkpointing, manually wrap
             training_args_kwargs["gradient_checkpointing"] = False
             training_args_kwargs["activation_offloading"] = True
-        elif self.cfg.gradient_checkpointing:
+        elif self.cfg.gradient_checkpointing is not None:
             training_args_kwargs["gradient_checkpointing"] = (
                 self.cfg.gradient_checkpointing
             )
@@ -516,6 +510,7 @@ class TrainerBuilderBase(abc.ABC):
                 self.cfg.eval_batch_size
             )
 
+        training_args_kwargs["include_tkps"] = self.cfg.include_tkps
         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
 

src/axolotl/core/builders/causal.py

@@ -10,6 +10,7 @@ import transformers
 from transformers import (
     DataCollatorWithFlattening,
     EarlyStoppingCallback,
+    Trainer,
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
@@ -35,6 +36,7 @@ from axolotl.utils.callbacks import (
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
 from axolotl.utils.callbacks.qat import QATCallback
+from axolotl.utils.callbacks.tokens_per_second import TokensPerSecondCallback
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
@@ -74,6 +76,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.qat:
             callbacks.append(QATCallback(self.cfg.qat))
 
+        if self.cfg.include_tkps:
+            callbacks.append(
+                TokensPerSecondCallback(
+                    self.cfg.tensor_parallel_size, self.cfg.context_parallel_size
+                )
+            )
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -340,20 +348,22 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
+            if self.cfg.center_rewards_coefficient is not None:
+                training_arguments_kwargs["center_rewards_coefficient"] = (
+                    self.cfg.center_rewards_coefficient
+                )
         elif self.cfg.process_reward_model:
             training_args_cls = AxolotlPRMConfig
         else:
             training_args_cls = AxolotlTrainingArguments
-        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+        training_args = training_args_cls(
             **training_arguments_kwargs,
         )
         training_args = self.hook_post_create_training_args(training_args)
 
         # unset run_name so wandb sets up experiment names
         if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
+            training_args.run_name = None
 
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
@@ -385,10 +395,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 **data_collator_kwargs,
             )
         sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters:
+        if "processing_class" in sig.parameters or issubclass(trainer_cls, Trainer):
             trainer_kwargs["processing_class"] = self.tokenizer
         elif "tokenizer" in sig.parameters:
             trainer_kwargs["tokenizer"] = self.tokenizer
+
         if (
             trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
             and self.cfg.datasets is not None
@@ -406,6 +417,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
+        # if the trainer has the `axolotl_cfg` property, set it
+        if hasattr(trainer, "axolotl_cfg"):
+            trainer.axolotl_cfg = self.cfg
         for callback in self.get_post_trainer_create_callbacks(trainer):
             trainer.add_callback(callback)
 

src/axolotl/core/builders/rl.py

@@ -168,16 +168,14 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if plugin_training_args:
                 training_args_kwargs.update(plugin_training_args)
 
-        training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
+        training_args = training_args_cls(
             logging_first_step=True,
             **training_args_kwargs,
         )
 
         # unset run_name so wandb sets up experiment names
         if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
+            training_args.run_name = None
 
         return training_args, trainer_kwargs
 

src/axolotl/core/chat/format/chatml.py

@@ -10,7 +10,7 @@ from .shared import wrap_tools
 
 def format_message(
     message: Messages,
-    message_index: Optional[int] = None,  # pylint: disable=unused-argument
+    message_index: Optional[int] = None,
 ) -> Messages:
     if message.is_chat_formatted:
         return message

src/axolotl/core/chat/messages.py

@@ -15,11 +15,11 @@ class MessageRoles(str, Enum):
     Message roles for the system, user, assistant, and tools
     """
 
-    system = "system"  # pylint: disable=invalid-name
-    user = "user"  # pylint: disable=invalid-name
-    assistant = "assistant"  # pylint: disable=invalid-name
-    tool = "tool"  # pylint: disable=invalid-name
-    ipython = (  # pylint: disable=invalid-name
+    system = "system"
+    user = "user"
+    assistant = "assistant"
+    tool = "tool"
+    ipython = (
         # for responses from builtin tools
         "ipython"
     )
@@ -30,12 +30,12 @@ class MessageContentTypes(str, Enum):
     Message content types for text, image, audio, tool calls, and tool responses
     """
 
-    special_token = "special_token"  # pylint: disable=invalid-name  # nosec B105
-    text = "text"  # pylint: disable=invalid-name
-    image = "image"  # pylint: disable=invalid-name
-    audio = "audio"  # pylint: disable=invalid-name
-    tool_call = "tool_call"  # pylint: disable=invalid-name  # to differentiate regular responses from tool calls from the assistant
-    tool_response = "tool_response"  # pylint: disable=invalid-name
+    special_token = "special_token"  # nosec B105
+    text = "text"
+    image = "image"
+    audio = "audio"
+    tool_call = "tool_call"
+    tool_response = "tool_response"
 
 
 class SpecialToken(str, Enum):
@@ -43,8 +43,8 @@ class SpecialToken(str, Enum):
     Special tokens for beginning of string and end of string
     """
 
-    bos_token = "bos_token"  # pylint: disable=invalid-name  # nosec B105
-    eos_token = "eos_token"  # pylint: disable=invalid-name  # nosec B105
+    bos_token = "bos_token"  # nosec B105
+    eos_token = "eos_token"  # nosec B105
 
 
 class ToolCallFunction(BaseModel):
@@ -73,7 +73,7 @@ class ToolCallContents(BaseModel):
 
     name: str
     arguments: dict[str, Union[str, int]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
+    id: Optional[str] = None
 
     def __str__(self) -> str:
         data = {"name": self.name, "arguments": self.arguments}
@@ -89,7 +89,7 @@ class ToolResponseContents(BaseModel):
 
     name: str
     content: Union[str, dict[str, Union[str, int, float]]]
-    id: Optional[str] = None  # pylint: disable=invalid-name
+    id: Optional[str] = None
 
     def __str__(self) -> str:
         data = {"name": self.name, "content": self.content}

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -1,23 +1,17 @@
 """
-This module contains a function that builds a transform that takes a row from the dataset and converts it to a Chat.
+This module contains a function that builds a transform that takes a row from the
+dataset and converts it to a Chat.
 """
 
-from typing import Any, Mapping, Union
+from typing import Any, Mapping
 
 
-def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
+def chat_message_transform_builder(
     train_on_inputs=False,
     conversations_field: str = "conversations",
-    message_field_role: Union[str, list[str]] = ["role", "from"],  # commonly "role"
-    message_field_content: Union[str, list[str]] = [
-        "value",
-        "text",
-        "content",
-    ],  # commonly "content"
-    message_field_training: Union[str, list[str]] = [
-        "train",
-        "weight",
-    ],  # commonly "weight"
+    message_field_role: str | list[str] | None = None,  # commonly "role"
+    message_field_content: str | list[str] | None = None,  # commonly "content"
+    message_field_training: str | list[str] | None = None,  # commonly "weight"
 ):
     """Builds a transform that takes a row from the dataset and converts it to a Chat
 
@@ -39,6 +33,12 @@ def chat_message_transform_builder(  # pylint: disable=dangerous-default-value
             A function that takes a list of conversations and returns a list of messages.
     """
 
+    if message_field_training is None:
+        message_field_training = ["train", "weight"]
+    if message_field_content is None:
+        message_field_content = ["value", "text", "content"]
+    if message_field_role is None:
+        message_field_role = ["role", "from"]
     message_field_role = (
         [message_field_role]
         if isinstance(message_field_role, str)

src/axolotl/core/trainers/__init__.py

@@ -1,6 +1,5 @@
 """Init for axolotl.core.trainers"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .base import AxolotlTrainer

src/axolotl/core/trainers/base.py

@@ -1,7 +1,5 @@
 """Module for customized trainers"""
 
-# pylint: disable=too-many-lines
-
 from __future__ import annotations
 
 import os
@@ -44,12 +42,20 @@ from axolotl.core.trainers.utils import (
 )
 from axolotl.utils import get_not_null
 from axolotl.utils.bench import get_gpu_memory_usage
+from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger(__name__)
 
+REDUCTION_FNS = {
+    "mean": torch.mean,
+    "min": torch.min,
+    "max": torch.max,
+    "sum": torch.sum,
+}
+
 
 class AxolotlTrainer(
     PackingMixin,
@@ -65,6 +71,15 @@ class AxolotlTrainer(
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
+    _axolotl_cfg: DictDefault | None = None
+
+    @property
+    def axolotl_cfg(self):
+        return self._axolotl_cfg
+
+    @axolotl_cfg.setter
+    def axolotl_cfg(self, cfg):
+        self._axolotl_cfg = cfg
 
     def __init__(
         self,
@@ -80,9 +95,10 @@ class AxolotlTrainer(
         self._signature_columns = None  # workaround for pylint
 
         super().__init__(*_args, **kwargs)
-
         self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        self._stored_metrics = defaultdict(
+            lambda: defaultdict(lambda: {"values": [], "reduction": "mean"})
+        )
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
@@ -285,9 +301,9 @@ class AxolotlTrainer(
         # fmt: off
         if dataloader_key is not None and self.args.dataloader_persistent_workers:
             if hasattr(self, "_eval_dataloaders"):
-                self._eval_dataloaders[dataloader_key] = dataloader  # type: ignore  # pylint: disable=access-member-before-definition
+                self._eval_dataloaders[dataloader_key] = dataloader  # type: ignore
             else:
-                self._eval_dataloaders = {dataloader_key: dataloader}  # pylint: disable=attribute-defined-outside-init
+                self._eval_dataloaders = {dataloader_key: dataloader}
         # fmt: on
 
         return self.accelerator.prepare(dataloader)
@@ -329,6 +345,17 @@ class AxolotlTrainer(
         #     outputs = model(**inputs)
         #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
         #     return (loss, outputs) if return_outputs else loss
+
+        # track number of tokens for tokens per second calculation
+        if self.args.include_tkps:
+            inputs_key = "labels" if "labels" in inputs else "input_ids"
+            if hasattr(self.state, "num_tokens"):
+                self.state.num_tokens = (
+                    self.state.num_tokens + (inputs[inputs_key] != -100).sum().cpu()
+                )
+            else:
+                self.state.num_tokens = (inputs[inputs_key] != -100).sum().cpu()
+
         if self.args.orpo_alpha:
             return self.orpo_compute_loss(
                 model,
@@ -344,6 +371,11 @@ class AxolotlTrainer(
             num_items_in_batch=num_items_in_batch,
         )
 
+    @override
+    def evaluate(self, *args, **kwargs):
+        LOG.info("Running evaluation step...")
+        return super().evaluate(*args, **kwargs)
+
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}
@@ -443,7 +475,7 @@ class AxolotlTrainer(
         model,
         inputs,
         return_outputs=False,
-        num_items_in_batch=None,  # pylint: disable=unused-argument
+        num_items_in_batch=None,
     ):
         concat_inputs = AxolotlTrainer.orpo_concatenate_inputs(
             inputs,
@@ -524,15 +556,10 @@ class AxolotlTrainer(
         accelerator_config = self.args.accelerator_config.to_dict()
         use_configured_state = accelerator_config.get("use_configured_state", False)
         if not use_configured_state:
-            AcceleratorState._reset_state(  # pylint: disable=protected-access
-                reset_partial_state=True
-            )
+            AcceleratorState._reset_state(reset_partial_state=True)
 
         super().create_accelerator_and_postprocess()
 
-        # now we need to put parallelism_config back on the PartialState since we rely on that info in other places
-        # PartialState().parallelism_config = self.accelerator.state.parallelism_config
-
         if self.is_fsdp_enabled:
             if (
                 "limit_all_gathers" in self.args.fsdp_config
@@ -540,7 +567,6 @@ class AxolotlTrainer(
             ):
                 self.accelerator.state.fsdp_plugin.limit_all_gathers = True
 
-    # pylint: disable=unused-argument
     def additional_accelerator_args(
         self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
     ) -> dict[str, Any]:
@@ -573,29 +599,61 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-        # Add averaged stored metrics to logs
-        for key, metrics in self._stored_metrics[train_eval].items():
-            logs[key] = torch.tensor(metrics).mean().item()
+
+        for key, metric_data in self._stored_metrics[train_eval].items():
+            values = torch.tensor(metric_data["values"])  # type: ignore[arg-type]
+            reduction_type = metric_data["reduction"]
+
+            fn = REDUCTION_FNS.get(reduction_type)
+            if fn is None:
+                raise NotImplementedError(
+                    "Metric reduction must be one of [mean, min, max, sum]"
+                )
+            logs[key] = round(fn(values).item(), 4)
 
         if is_main_process():
             # Add memory usage
             try:
                 active, allocated, reserved = get_gpu_memory_usage()
-                logs["memory/max_mem_active(gib)"] = round(active, 2)
-                logs["memory/max_mem_allocated(gib)"] = round(allocated, 2)
-                logs["memory/device_mem_reserved(gib)"] = round(reserved, 2)
+                logs["memory/max_active (GiB)"] = round(active, 2)
+                logs["memory/max_allocated (GiB)"] = round(allocated, 2)
+                logs["memory/device_reserved (GiB)"] = round(reserved, 2)
             except (ValueError, TypeError, FileNotFoundError):
                 pass
 
+        if self.args.include_tkps and train_eval == "train":
+            # each rank will log its own tokens per second
+            # for logging_steps > 1 we obtain a moving average of this metric
+            logs["tokens_per_second_per_gpu"] = round(
+                self.state.last_tokens_per_second.item() / self.args.logging_steps, 2
+            )
+
         del self._stored_metrics[train_eval]
 
         return super().log(logs, start_time)
 
     def store_metrics(
-        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
+        self,
+        metrics: dict[str, float] | dict[str, tuple[int | float, str]],
+        train_eval: Literal["train", "eval"] = "train",
+        reduction: Literal["mean", "min", "max", "sum"] = "mean",
     ) -> None:
+        """
+        Store metrics with specified reduction type.
+
+        Args:
+            metrics: Dictionary of metric names to values, or metric names to (value,
+                reduction_type) tuples.
+            train_eval: Whether this is for training or evaluation.
+        """
         for key, value in metrics.items():
-            self._stored_metrics[train_eval][key].append(value)
+            if isinstance(value, tuple):
+                value, _reduction = value  # type: ignore[assignment]
+            else:
+                value, _reduction = value, reduction
+
+            self._stored_metrics[train_eval][key]["values"].append(value)
+            self._stored_metrics[train_eval][key]["reduction"] = _reduction
 
     def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey
@@ -662,6 +720,11 @@ class AxolotlTrainer(
                 LOG.info(
                     "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
                 )
-                self.data_collator.tokenizer.save_pretrained(output_dir)
+                save_jinja_files = True
+                if self.axolotl_cfg:
+                    save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
+                self.data_collator.tokenizer.save_pretrained(
+                    output_dir, save_jinja_files=save_jinja_files
+                )
             # Good practice: save your training arguments together with the trained model
             torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

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

@@ -101,11 +101,11 @@ class AxolotlDPOTrainer(
     ) -> dict[str, torch.Tensor]:
         if self.args.dpo_norm_loss:
             # fmt: off
-            loss_type: str = self.loss_type  # type: ignore[has-type]  # pylint: disable=access-member-before-definition
+            loss_type: str = self.loss_type  # type: ignore[has-type]
             # fmt: on
             # concatenated_forward handles avg token logprob for ipo case already
-            self.loss_type = "ipo"  # pylint: disable=attribute-defined-outside-init
+            self.loss_type = "ipo"
             res = super().concatenated_forward(model, batch, is_ref_model=is_ref_model)
-            self.loss_type = loss_type  # pylint: disable=attribute-defined-outside-init
+            self.loss_type = loss_type
             return res
         return super().concatenated_forward(model, batch, is_ref_model=is_ref_model)

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

@@ -128,9 +128,7 @@ class GRPOStrategy:
         return grpo_args_kwargs
 
     @classmethod
-    def set_trainer_args(
-        cls, cfg: DictDefault
-    ) -> list[Any]:  # pylint: disable=unused-argument
+    def set_trainer_args(cls, cfg: DictDefault) -> list[Any]:
         trainer_args = []
         if cfg.trl and cfg.trl.reward_funcs:
             reward_funcs = []
@@ -151,7 +149,7 @@ class GRPOStrategy:
         return trainer_kwargs
 
     @classmethod
-    def get_collator(cls, *args, **kwargs):  # pylint: disable=unused-argument
+    def get_collator(cls, *args, **kwargs):
         # No data collation is needed in GRPO, handled by trl's trainer __init__
         return None
 

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

@@ -1,7 +1,5 @@
 """Axolotl GRPO trainers (with and without sequence parallelism handling)"""
 
-# pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
-
 import warnings
 from functools import partial
 from typing import Any
@@ -52,7 +50,6 @@ from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, Optimizer
 from axolotl.monkeypatch.ring_attn import get_ring_attn_group
 
 if is_peft_available():
-    # pylint: disable=unused-import
     from peft import PeftConfig
 
 
@@ -253,7 +250,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
     def get_train_dataloader(self) -> DataLoader:
         """Get dataloader for training"""
         train_dataset = self.train_dataset
-        # pylint: disable=access-member-before-definition
+
         data_collator = self.data_collator  # type: ignore
 
         # Handle dataset preprocessing
@@ -266,7 +263,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                     train_dataset, description="training"
                 )
         else:
-            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
+            self.data_collator = self._get_collator_with_removed_columns(
                 data_collator,
                 description="training",
             )
@@ -308,10 +305,10 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
         # Generate completions using either vLLM or regular generation
         if self.args.use_vllm:
             # First, have main process load weights if needed
-            # pylint: disable=access-member-before-definition
+
             if self.state.global_step != self._last_loaded_step:  # type: ignore[has-type]
                 self._move_model_to_vllm()
-                # pylint: disable=attribute-defined-outside-init
+
                 self._last_loaded_step = self.state.global_step
 
             # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
@@ -333,8 +330,9 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                         # Extract prompts from this SP group, accounting for num_generations duplicates
                         # We only need prompts from one rank in each SP group
                         group_prompts = all_prompts_text[
-                            group_leader_rank
-                            * len(prompts_text) : (group_leader_rank + 1)
+                            group_leader_rank * len(prompts_text) : (
+                                group_leader_rank + 1
+                            )
                             * len(prompts_text) : self.num_generations
                         ]
 
@@ -485,7 +483,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
         )
         if is_conversational(inputs[0]):
             completions = []
-            for prompt, completion in zip(prompts, completions_text):
+            for prompt, completion in zip(prompts, completions_text, strict=False):
                 bootstrap = (
                     prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
                 )
@@ -503,6 +501,7 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                 self.reward_funcs,
                 self.reward_processing_classes,
                 self.reward_func_names,
+                strict=False,
             )
         ):
             with profiling_context(self, reward_func_name):
@@ -511,14 +510,17 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
                 ):  # Module instead of PretrainedModel for compat with compiled models
                     if is_conversational(inputs[0]):
                         messages = [
-                            {"messages": p + c} for p, c in zip(prompts, completions)
+                            {"messages": p + c}
+                            for p, c in zip(prompts, completions, strict=False)
                         ]
                         texts = [
                             apply_chat_template(x, reward_processing_class)["text"]
                             for x in messages
                         ]
                     else:
-                        texts = [p + c for p, c in zip(prompts, completions)]
+                        texts = [
+                            p + c for p, c in zip(prompts, completions, strict=False)
+                        ]
                     reward_inputs = reward_processing_class(
                         text=texts,
                         return_tensors="pt",
@@ -564,7 +566,8 @@ class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
             row_reward_kwargs["completion"] = completions[nan_row_idx]
             warnings.warn(
                 f"All reward functions returned None for the following kwargs: {row_reward_kwargs}. "
-                "Please ensure that at least one reward function returns a valid reward."
+                "Please ensure that at least one reward function returns a valid reward.",
+                stacklevel=2,
             )
 
         # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the

src/axolotl/core/trainers/mamba.py

@@ -5,7 +5,6 @@ import torch
 from axolotl.core.trainers.base import AxolotlTrainer
 
 
-# pylint: disable=too-many-ancestors
 class AxolotlMambaTrainer(AxolotlTrainer):
     """Mamba specific trainer to handle loss calculation"""
 
@@ -15,8 +14,8 @@ class AxolotlMambaTrainer(AxolotlTrainer):
         self,
         model,
         inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-        num_items_in_batch=None,  # pylint: disable=unused-argument
+        return_outputs=False,
+        num_items_in_batch=None,
     ):
         input_ids = inputs.pop("input_ids")
         lm_logits = model(input_ids).logits

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

@@ -1,6 +1,5 @@
 """Init for axolotl.core.trainers.mixins"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .activation_checkpointing import ActivationOffloadingMixin

src/axolotl/core/trainers/mixins/activation_checkpointing.py

@@ -92,7 +92,7 @@ def get_lora_act_offloading_ctx_manager(
         `contextlib.ContextDecorator`:
             Activation offloading context manager for the model.
     """
-    # pylint: disable=unnecessary-dunder-call
+
     activations_handling_ctx = OffloadActivations(
         use_pin_memory=use_pin_memory,
         use_streams=use_streams,

src/axolotl/core/trainers/mixins/distributed_parallel.py

@@ -26,7 +26,6 @@ class DistributedParallelMixin(Trainer):
             self.accelerator.distributed_type == "FSDP"
             and self.accelerator.state.fsdp_plugin is None
         ):
-            # pylint: disable=protected-access
             # handle Context Parallelism without FSDP
             self.accelerator.state.distributed_type = "MULTI_GPU"
             self.accelerator.state._shared_state["distributed_type"] = "MULTI_GPU"

src/axolotl/core/trainers/mixins/optimizer.py

@@ -70,11 +70,11 @@ class OptimizerMixin(Trainer):
                 }
             )
         if params["embeddings"]:
-            lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+            lr = optimizer_kwargs["lr"]
             if self.args.embedding_lr_scale:
-                lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+                lr *= self.args.embedding_lr_scale
             elif self.args.embedding_lr:
-                lr = self.args.embedding_lr  # pylint: disable=invalid-name
+                lr = self.args.embedding_lr
             optimizer_grouped_parameters.append(
                 {
                     "params": list(params["embeddings"].values()),
@@ -143,7 +143,7 @@ class OptimizerMixin(Trainer):
                 loraplus_lr_embedding = getattr(
                     self.args, "loraplus_lr_embedding", 1e-6
                 )
-                self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer = create_loraplus_optimizer(
                     opt_model,
                     optimizer_cls,
                     loraplus_lr_ratio=loraplus_lr_ratio,
@@ -185,17 +185,15 @@ class OptimizerMixin(Trainer):
                                 p.data_ptr(): p.numel() for p in module.parameters()
                             }.values()
                         )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        LOG.info(f"skipped {module}: {skipped / 2**20}M params")
                         manager.register_module_override(
                             module, "weight", {"optim_bits": 32}
                         )
                         LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-                LOG.info(f"skipped: {skipped/2**20}M params")
+                LOG.info(f"skipped: {skipped / 2**20}M params")
 
         if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
+            self.optimizer = smp.DistributedOptimizer(self.optimizer)
 
         return self.optimizer
 

src/axolotl/core/trainers/mixins/scheduler.py

@@ -46,7 +46,7 @@ class SchedulerMixin(Trainer):
         )
 
         # fmt: off
-        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+        if self.lr_scheduler is None:  # type: ignore
             # fmt: on
             plugin_manager = PluginManager.get_instance()
             lr_scheduler: LRScheduler | None = plugin_manager.create_lr_scheduler(
@@ -90,7 +90,7 @@ class SchedulerMixin(Trainer):
                     LOG.warning(
                         "Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
 
-                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -98,7 +98,7 @@ class SchedulerMixin(Trainer):
             elif self.args.cosine_min_lr_ratio and self.args.cosine_constant_lr_ratio and use_cosine_min_lr:
                 assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
                 assert 0 <= self.args.cosine_constant_lr_ratio <= 1.0, "cosine_constant_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(  # pylint: disable=attribute-defined-outside-init
+                self.lr_scheduler = get_cosine_schedule_with_warmup_decay_constant(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -107,7 +107,7 @@ class SchedulerMixin(Trainer):
                 )
             elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
                 assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
+                self.lr_scheduler = get_cosine_schedule_with_min_lr(
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
@@ -133,7 +133,7 @@ class SchedulerMixin(Trainer):
             )
             if not self.lr_scheduler:
                 super().create_scheduler(num_training_steps, optimizer)
-            self.lr_scheduler = JaggedLRRestartScheduler(  # pylint: disable=attribute-defined-outside-init
+            self.lr_scheduler = JaggedLRRestartScheduler(
                 optimizer,
                 self.lr_scheduler,
                 self.args.jagged_restart_steps,

src/axolotl/core/training_args_base.py

@@ -14,7 +14,6 @@ class AxolotlTrainingMixins:
     Mixin class for the Axolotl training args.
     """
 
-    # pylint: disable=duplicate-code
     model_type: Optional[str] = field(
         default=None, metadata={"help": "HF model configuration model_type."}
     )
@@ -50,6 +49,12 @@ class AxolotlTrainingMixins:
         default=False,
         metadata={"help": "Use real batches for efficient training."},
     )
+    include_tkps: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to include tokens per second in the training metrics."
+        },
+    )
     eval_sample_packing: Optional[bool] = field(
         default=None,
         metadata={"help": "Use sample packing for efficient evals."},

src/axolotl/datasets.py

@@ -6,8 +6,6 @@ concept of middlewares to wrap each dataset. We'll use the collators later on to
 datasets.
 """
 
-from typing import Any
-
 from datasets import Dataset, IterableDataset
 
 from axolotl.utils.logging import get_logger
@@ -27,7 +25,7 @@ class TokenizedPromptDataset(Dataset):
         keep_in_memory: Whether to keep the tokenized dataset in memory.
     """
 
-    def __init__(  # pylint: disable=super-init-not-called
+    def __init__(
         self,
         prompt_tokenizer: PromptTokenizingStrategy,
         dataset: Dataset,
@@ -43,13 +41,10 @@ class TokenizedPromptDataset(Dataset):
             **kwargs,
         )
 
-    def process(self, dataset: Dataset | IterableDataset) -> Dataset | IterableDataset:
-        """Apply filtering and tokenization."""
-        features = None
-        if not isinstance(dataset, IterableDataset):
-            features = dataset.features.keys()
+    def process(self, dataset):
+        features = dataset.features.keys()
 
-        map_kwargs: dict[str, Any] = {}
+        map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
             map_kwargs["batch_size"] = 1_000
@@ -58,28 +53,18 @@ class TokenizedPromptDataset(Dataset):
             hasattr(self.prompt_tokenizer, "filter_rows")
             and self.prompt_tokenizer.filter_rows
         ):
-            filter_kwargs: dict[str, Any] = {"desc": "Strategy Filtering Rows"}
-            if not isinstance(dataset, IterableDataset):
-                filter_kwargs["num_proc"] = self.process_count
-
             dataset = dataset.filter(
                 self.prompt_tokenizer.filter_rows,
-                **filter_kwargs,
+                num_proc=self.process_count,
+                desc="Strategy Filtering Rows",
             )
 
-        map_kwargs = {
-            **map_kwargs,
-            "desc": "Tokenizing Prompts",
-        }
-
-        # Only add remove_columns for regular datasets
-        if not isinstance(dataset, IterableDataset):
-            map_kwargs["remove_columns"] = features
-            map_kwargs["num_proc"] = self.process_count
-            map_kwargs["keep_in_memory"] = self.keep_in_memory
-
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
+            num_proc=self.process_count,
+            remove_columns=features,
+            keep_in_memory=self.keep_in_memory,
+            desc="Tokenizing Prompts",
             **map_kwargs,
         )
 
@@ -93,16 +78,10 @@ def wrap_dataset_for_tokenized_prompt(
         map_kwargs = {}
         if prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-
-        # Map the dataset and remove original columns
-        # For IterableDataset, features might be None until first iteration
-        remove_columns = None
-        if dataset.features is not None:
-            remove_columns = list(dataset.features.keys())
-
+        features = list(dataset.features.keys())
         return dataset.map(
             prompt_tokenizer.tokenize_prompt,
-            remove_columns=remove_columns,
+            remove_columns=features,
             **map_kwargs,
         )
     return TokenizedPromptDataset(prompt_tokenizer, dataset, **kwargs)