broken

* black, isort, flake8 -> ruff

* remove unused

* add back needed import

* fix

.bandit

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

.coderabbit.yaml

@@ -12,5 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
+    auto_incremental_review: true
 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/CONTRIBUTING.md

@@ -57,6 +57,13 @@ We welcome ideas for improvements and new features. To suggest an enhancement, o
 5. Push your branch to your fork on GitHub.
 6. Open a new pull request against the `main` branch of the axolotl repository. Include a clear and concise description of your changes, referencing any related issues.
 
+#### Skipping CI Checks
+
+You can skip certain CI checks by including specific keywords in your commit messages:
+
+- `[skip ci]` or `skip ci` - Skips all CI checks for that commit
+- `[skip-e2e]` or `skip-e2e` - Skips only end-to-end tests while running other CI checks. You may also include this in the title of your PR to disable end-to-end tests for the entire PR.
+
 ## Style Guidelines
 
 ### Code Style

.github/workflows/main.yml

@@ -98,6 +98,12 @@ jobs:
             python_version: "3.11"
             pytorch: 2.7.1
             axolotl_extras:
+            is_latest:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
             is_latest: true
           - cuda: 128
             cuda_version: 12.8.1
@@ -151,6 +157,18 @@ jobs:
             python_version: "3.11"
             pytorch: 2.6.0
             axolotl_extras:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras:
+            is_latest:
+          - cuda: 126
+            cuda_version: 12.6.3
+            python_version: "3.11"
+            pytorch: 2.7.1
+            axolotl_extras: vllm
+            is_latest: true
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/tests.yml

@@ -105,7 +105,8 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/patched/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/ --cov=axolotl --cov-append --cov-report=xml
 
@@ -179,21 +180,52 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest -v --durations=10 tests/patched/
+          pytest -v --durations=10 -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ --ignore=tests/monkeypatch/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v --durations=10 tests/monkeypatch/ --cov=axolotl --cov-append --cov-report=xml
           pytest -v --durations=10 tests/cli/
 
       - name: cleanup pip cache
         run: |
           find "$(pip cache dir)/http-v2" -type f -mtime +14 -exec rm {} \;
 
+  gate-skip-e2e:
+    needs: [pre-commit, pytest, pytest-sdist]
+    runs-on: ubuntu-latest
+    outputs:
+      skip: ${{ steps.compute.outputs.skip }}
+    steps:
+      - uses: actions/github-script@v7
+        id: compute
+        with:
+          script: |
+            const token = /\[skip-e2e\]/i;
+            let msg = '';
+            if (context.eventName === 'push') {
+              msg = context.payload.head_commit?.message || '';
+            } else if (context.eventName === 'pull_request') {
+              const { owner, repo } = context.repo;
+              const prNumber = context.payload.pull_request.number;
+              const commits = await github.paginate(
+                github.rest.pulls.listCommits,
+                { owner, repo, pull_number: prNumber, per_page: 100 }
+              );
+              msg = commits.at(-1)?.commit?.message || '';
+            }
+            const title = context.payload.pull_request?.title || '';
+            const body  = context.payload.pull_request?.body  || '';
+            const skip = token.test(msg) || token.test(title) || token.test(body);
+            core.setOutput('skip', String(skip));
+
   docker-e2e-tests-1st:
     # Run this job first as a gate for running the remainder of the test matrix
-    if: ${{ ! contains(github.event.commits[0].message, '[skip e2e]') && github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: >
+      github.repository_owner == 'axolotl-ai-cloud' &&
+      (github.event_name != 'pull_request' || !github.event.pull_request.draft) &&
+      needs.gate-skip-e2e.outputs.skip != 'true'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
-    needs: [pre-commit, pytest, pytest-sdist]
+    needs: [pre-commit, pytest, pytest-sdist, gate-skip-e2e]
 
     strategy:
       fail-fast: false
@@ -239,13 +271,16 @@ jobs:
           modal run cicd.e2e_tests
 
   docker-e2e-tests:
-    if: ${{ github.repository_owner == 'axolotl-ai-cloud' && !github.event.pull_request.draft }}
+    if: >
+      github.repository_owner == 'axolotl-ai-cloud' &&
+      (github.event_name != 'pull_request' || !github.event.pull_request.draft) &&
+      needs.gate-skip-e2e.outputs.skip != 'true'
     # this job needs to be run on self-hosted GPU runners...
     runs-on: [self-hosted, modal]
     timeout-minutes: 120
     # Only run the remainder of the matrix if the first e2e check passed;
     # this is to save on wasted compute costs for known failures that get caught in the first run
-    needs: [pre-commit, pytest, docker-e2e-tests-1st]
+    needs: [pre-commit, pytest, gate-skip-e2e, docker-e2e-tests-1st]
 
     strategy:
       fail-fast: false

.isort.cfg

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

.pre-commit-config.yaml

@@ -3,29 +3,19 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   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: v0.12.9
     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: v3.3.7
-    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

@@ -0,0 +1,10 @@
+cff-version: 1.2.0
+type: software
+title: "Axolotl: Post-Training for AI Models"
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "Axolotl maintainers and contributors"
+repository-code: "https://github.com/axolotl-ai-cloud/axolotl"
+url: "https://axolotl.ai/"
+license: Apache-2.0
+date-released: "2023-05-30"

README.md

@@ -149,6 +149,20 @@ Contributions are welcome! Please see our [Contributing Guide](https://github.co
 
 Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)
 
+## 📝 Citing Axolotl
+
+If you use Axolotl in your research or projects, please cite it as follows:
+
+```bibtex
+@software{axolotl,
+  title = {Axolotl: Post-Training for AI Models},
+  author = {{Axolotl maintainers and contributors}},
+  url = {https://github.com/axolotl-ai-cloud/axolotl},
+  license = {Apache-2.0},
+  year = {2023}
+}
+```
+
 ## 📜 License
 
 This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.

TODO.md

@@ -1,10 +0,0 @@
-# todo list
-
-- [] Validation of parameters for combinations that won't work
-
-
-
-## things that are known not to work
-
-- FSDP offload and gradient_checkpointing - https://github.com/pytorch/pytorch/issues/82203
-- adamw_bnb_8bit doesn't play well with FSDP offload

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
@@ -70,4 +68,4 @@ 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)

docker/Dockerfile-base

@@ -37,7 +37,7 @@ WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
     python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
-    python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
+    CAUSAL_CONV1D_FORCE_CXX11_ABI=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE python3 -m pip install --no-cache-dir causal_conv1d==1.5.2 && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" && \
     python3 -m pip cache purge
 

docs/multimodal.qmd

@@ -13,10 +13,13 @@ format:
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
+- [Voxtral](#sec-voxtral)
 - [Gemma-3](#sec-gemma-3)
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
+- [SmolVLM2](#sec-smolvlm2)
+- [LFM2-VL](#sec-lfm2-vl)
 
 ## Usage
 
@@ -31,7 +34,7 @@ skip_prepare_dataset: true
 remove_unused_columns: false  # leave columns in place as they are needed to handle image embeddings during training
 sample_packing: false  # not yet supported with multimodal
 
-chat_template:  # see in next section
+chat_template:  # see in next section if specified
 
 # example dataset
 datasets:
@@ -97,6 +100,16 @@ base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 chat_template: mistral_v7_tekken
 ```
 
+### Voxtral {#sec-voxtral}
+
+::: {.callout-tip}
+Please make sure to install audio lib via `pip3 install librosa==0.11.0 'mistral_common[audio]==1.8.3'`
+:::
+
+```yaml
+base_model: mistralai/Voxtral-Mini-3B-2507
+```
+
 ### Gemma-3 {#sec-gemma-3}
 
 ::: {.callout-tip}
@@ -143,6 +156,26 @@ base_model: Qwen/Qwen2.5-VL-7B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
+### SmolVLM2 {#sec-smolvlm2}
+
+::: {.callout-tip}
+Please make sure to install `num2words` via `pip3 install num2words==0.5.14`
+:::
+
+```yaml
+base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
+```
+
+### LFM2-VL {#sec-lfm2-vl}
+
+::: {.callout-warning}
+Please uninstall `causal-conv1d` via `pip3 uninstall -y causal-conv1d`
+:::
+
+```yaml
+base_model: LiquidAI/LFM2-VL-450M
+```
+
 ## Dataset Format
 
 For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.
@@ -181,6 +214,20 @@ You may need to install `librosa` via `pip3 install librosa==0.11.0`.
 
 :::
 
+### Video
+
+::: {.callout-warning}
+
+This is not well tested at the moment. We welcome contributors!
+
+:::
+
+For video loading, you can use the following keys within `content` alongside `"type": "video"`:
+
+- `"path": "/path/to/video.mp4"`
+- `"url": "https://example.com/video.mp4"`
+- `"video": np.ndarray | list[PIL.Image.Image] | torch.Tensor` (or list of the aforementioned)
+
 ### Example
 
 Here is an example of a multi-modal dataset:

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."
             )

examples/LiquidAI/README.md

@@ -0,0 +1,58 @@
+# Finetune Liquid Foundation Models 2 (LFM2) with Axolotl
+
+[Liquid Foundation Models 2 (LFM2)](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) are a family of small, open-weight models from [Liquid AI](https://www.liquid.ai/) focused on quality, speed, and memory efficiency. Liquid AI released text-only [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) and text+vision [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa) models.
+
+LFM2 features a new hybrid Liquid architecture with multiplicative gates, short-range convolutions, and grouped query attention, enabling fast training and inference.
+
+This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
+
+## Getting Started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    ```
+
+2.  Run one of the finetuning examples below.
+
+    **LFM2**
+    ```bash
+    # FFT SFT (1x48GB @ 25GiB)
+    axolotl train examples/LiquidAI/lfm2-350m-fft.yaml
+    ```
+
+    **LFM2-VL**
+    ```bash
+    # LoRA SFT (1x48GB @ 2.7GiB)
+    axolotl train examples/LiquidAI/lfm2-vl-lora.yaml
+    ```
+
+### TIPS
+
+- **Installation Error**: If you encounter `ImportError: ... undefined symbol ...` or `ModuleNotFoundError: No module named 'causal_conv1d_cuda'`, the `causal-conv1d` package may have been installed incorrectly. Try uninstalling it:
+  ```bash
+  pip uninstall -y causal-conv1d
+  ```
+
+- **Dataset Loading**: Read more on how to load your own dataset in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
+- **Dataset Formats**:
+  - For LFM2 models, the dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+  - For LFM2-VL models, Axolotl follows the multi-content Messages format. See our [Multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format) for details.
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+
+## Related Resources
+
+- [LFM2 Blog](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models)
+- [LFM2-VL Blog](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

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

@@ -2,7 +2,6 @@ base_model: LiquidAI/LFM2-350M
 
 chunked_cross_entropy: true
 
-chat_template: tokenizer_default
 eot_tokens:
   - "<|im_end|>"
 datasets:

examples/LiquidAI/lfm2-vl-lora.yaml

@@ -0,0 +1,58 @@
+base_model: LiquidAI/LFM2-VL-450M
+trust_remote_code: true
+model_type: AutoModelForImageTextToText
+processor_type: AutoProcessor
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/gpt-oss/README.md

@@ -33,13 +33,64 @@ Note: Memory usage taken from `device_mem_reserved(gib)` from logs.
 
 ### Training 120B
 
-On 8xH100s
+On 8xH100s, make sure you have ~3TB of free disk space. With each checkpoint clocking in at ~720GB, along with the base
+model, and final model output, you may need at least 3TB of free disk space to keep at least 2 checkpoints.
 
 ```bash
 # FFT SFT with offloading (8x80GB @ ~49GiB/GPU)
 axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
 ```
 
+To simplify fine-tuning across 2 nodes × 8x H100 (80GB) GPUs, we've partnered with [Baseten](https://baseten.co) to showcase multi-node
+training of the 120B model using Baseten Truss. You can read more about this recipe on
+[Baseten's blog](https://www.baseten.co/blog/how-to-fine-tune-gpt-oss-120b-with-baseten-and-axolotl/). The recipe can
+be found on their
+[GitHub](https://github.com/basetenlabs/ml-cookbook/tree/main/examples/oss-gpt-120b-axolotl/training).
+
+ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
+See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
+
+```bash
+sed -i 's/FSDPGptOssForCausalLM/GptOssForCausalLM/g' ./outputs/gpt-oss-out/config.json
+```
+
+When using SHARDED_STATE_DICT with FSDP, the final checkpoint should automatically merge the sharded weights to your
+configured `output_dir`. However, if that step fails due to a disk space error, you can take an additional step to
+merge the sharded weights.  This step will automatically determine the last checkpoint directory and merge the sharded
+weights to `{output_dir}/merged`.
+
+```bash
+axolotl merge-sharded-fsdp-weights examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
+mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
+```
+
+
+### Inferencing your fine-tuned model
+
+#### vLLM
+
+GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
+for more information about using a special vllm-openai docker image for inferencing with vLLM.
+
+Optionally, vLLM can be installed from nightly:
+
+```bash
+pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+```
+and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
+```bash
+vllm serve ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-20b --host 0.0.0.0 --port 8888  --tensor-parallel-size 8
+```
+
+#### SGLang
+
+SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
+SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
+
+```bash
+python3 -m sglang.launch_server --model ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-120b --host 0.0.0.0 --port 8888 --tp 8
+```
+
 ### Tool use
 
 GPT-OSS has a comprehensive tool understanding. Axolotl supports tool calling datasets for Supervised Fine-tuning.

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -20,6 +20,7 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
+save_total_limit: 2  # the 120B model can use up to 720GB of disk space per checkpoint, so let's only keep the last 2
 
 sequence_len: 4096
 sample_packing: true
@@ -43,7 +44,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -41,7 +41,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -53,7 +53,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/lfm2/README.md

@@ -1,7 +0,0 @@
-# Liquid Foundation Models 2
-
-LFM2 support in transformers exists in the main branch, but is not yet included in the transformers release.
-
-```bash
-pip install --upgrade --no-deps --force-reinstall git+https://github.com/huggingface/transformers.git
-```

examples/smolvlm2/README.md

@@ -0,0 +1,49 @@
+# Finetune SmolVLM2 with Axolotl
+
+[SmolVLM2](https://huggingface.co/collections/HuggingFaceTB/smolvlm2-smallest-video-lm-ever-67ab6b5e84bf8aaa60cb17c7) are a family of lightweight, open-source multimodal models from HuggingFace designed to analyze and understand video, image, and text content.
+
+These models are built for efficiency, making them well-suited for on-device applications where computational resources are limited. Models are available in multiple sizes, including 2.2B, 500M, and 256M.
+
+This guide shows how to fine-tune SmolVLM2 models with Axolotl.
+
+## Getting Started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    ```
+
+2. Install an extra dependency:
+
+    ```bash
+    pip3 install num2words==0.5.14
+    ```
+
+3.  Run the finetuning example:
+
+    ```bash
+    # LoRA SFT (1x48GB @ 6.8GiB)
+    axolotl train examples/smolvlm2/smolvlm2-2B-lora.yaml
+    ```
+
+## TIPS
+
+- **Dataset Format**: For video finetuning, your dataset must be compatible with the multi-content Messages format. For more details, see our documentation on [Multimodal Formats](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+- **Dataset Loading**: Read more on how to prepare and load your own datasets in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+
+## Related Resources
+
+- [SmolVLM2 Blog](https://huggingface.co/blog/smolvlm2)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/smolvlm2/smolvlm2-2B-lora.yaml

@@ -0,0 +1,56 @@
+base_model: HuggingFaceTB/SmolVLM2-2.2B-Instruct
+trust_remote_code: true
+processor_type: AutoProcessor
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.text_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

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"]
+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

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 
 # START section of dependencies that don't install on Darwin/MacOS
-bitsandbytes==0.46.1
+bitsandbytes==0.47.0
 # triton 3.4.0 is not compatible with CCE
 triton>=3.0.0,<3.4.0
 mamba-ssm==1.2.0.post1
@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
-transformers==4.55.0
+peft>=0.17.0
+transformers==4.55.3
 tokenizers>=0.21.1
 accelerate==1.10.0
 datasets==4.0.0

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/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

@@ -118,9 +118,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.2"],
+    "flash-attn": ["flash-attn==2.8.3"],
     "ring-flash-attn": [
-        "flash-attn==2.8.2",
+        "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
         "yunchang==0.6.0",
     ],

src/axolotl/__init__.py

@@ -4,4 +4,4 @@ import pkgutil
 
 __path__ = pkgutil.extend_path(__path__, __name__)  # Make this a namespace package
 
-__version__ = "0.12.0"
+__version__ = "0.13.0.dev"

src/axolotl/cli/args.py

@@ -40,6 +40,12 @@ class VllmServeCliArgs:
         default=None,
         metadata={"help": "Number of tensor parallel workers to use."},
     )
+    data_parallel_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Number of data parallel workers to use for vLLM serving. This controls how many model replicas are used for parallel inference."
+        },
+    )
     host: Optional[str] = field(
         default=None,  # nosec B104
         metadata={"help": "Host address to run the server on."},

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/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:
@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.6.0"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"
@@ -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"
 
@@ -200,7 +199,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":
@@ -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

@@ -153,15 +153,14 @@ def prepare_plugins(cfg: DictDefault):
         plugin_manager = PluginManager.get_instance()
         for plugin_name in cfg["plugins"]:
             plugin_manager.register(plugin_name)
+        for plugin in plugin_manager.plugins.values():
+            plugin.register(cfg)
 
 
 def plugin_set_cfg(cfg: DictDefault):
     if cfg.get("plugins"):
         plugin_manager = PluginManager.get_instance()
         plugin_manager.cfg = cfg
-        # now that we have the finalized cfg, register the plugins individually
-        for plugin in plugin_manager.plugins.values():
-            plugin.register(cfg)
 
 
 def load_cfg(
@@ -211,7 +210,7 @@ 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)

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

@@ -35,7 +35,7 @@ def get_multi_line_input() -> str:
 
     instruction = ""
     for line in sys.stdin:
-        instruction += line  # pylint: disable=consider-using-join
+        instruction += line
 
     return instruction
 
@@ -64,7 +64,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(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=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer
@@ -167,7 +167,6 @@ def do_inference_gradio(
         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 +251,7 @@ 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
@@ -123,9 +121,10 @@ def train(
     _launcher = None if kwargs.get("use_ray") else launcher
 
     # Process each configuration
-    for cfg_file in generate_config_files(config, sweep):
+    for cfg_file, is_group in generate_config_files(config, sweep):
         try:
-            launch_training(cfg_file, _launcher, cloud, kwargs, launcher_args)
+            use_exec = is_group is not True
+            launch_training(cfg_file, _launcher, cloud, kwargs, launcher_args, use_exec)
         except subprocess.CalledProcessError as exc:
             LOG.error(f"Failed to train/fine-tune config '{cfg_file}': {exc}")
             if not sweep:

src/axolotl/cli/merge_sharded_fsdp_weights.py

@@ -10,6 +10,7 @@ import fire
 import torch
 import torch.distributed.checkpoint as dist_cp
 import torch.distributed.checkpoint.format_utils as dist_cp_format_utils
+from accelerate import PartialState
 from accelerate.utils import (
     SAFE_WEIGHTS_INDEX_NAME,
     SAFE_WEIGHTS_NAME,
@@ -23,6 +24,7 @@ from torch.distributed.checkpoint.format_utils import _EmptyStateDictLoadPlanner
 
 from axolotl.cli.config import load_cfg
 from axolotl.utils.logging import get_logger
+from axolotl.utils.train import determine_last_checkpoint
 
 LOG = get_logger(__name__)
 
@@ -30,7 +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))
 
 
@@ -57,10 +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,
     )
 
@@ -143,7 +145,6 @@ def merge_fsdp_weights(
         ValueError: If torch version < 2.3.0, or if `checkpoint_dir` does not exist.
     """
     checkpoint_dir_ = Path(checkpoint_dir)
-    from accelerate.state import PartialState
 
     if not is_torch_version(">=", "2.3.0"):
         raise ValueError("`merge_fsdp_weights` requires PyTorch >= 2.3.0`")
@@ -180,7 +181,6 @@ def merge_fsdp_weights(
         if remove_checkpoint_dir:
             LOG.info(f"Removing old checkpoint directory {checkpoint_dir_}")
             shutil.rmtree(checkpoint_dir_)
-    state.wait_for_everyone()
 
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
@@ -191,15 +191,36 @@ 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"
+    if not fsdp_dir.exists():
+        checkpoint_dir = determine_last_checkpoint(parsed_cfg, update=False)
+        if checkpoint_dir:
+            fsdp_dir = Path(checkpoint_dir) / "pytorch_model_fsdp_0"
+        if not fsdp_dir.exists():
+            raise ValueError(
+                f"Could not find FSDP checkpoint `pytorch_model_fsdp_0` in {checkpoint_dir}"
+            )
+
+    output_path = str(Path(parsed_cfg.output_dir) / "merged")
     merge_fsdp_weights(
         checkpoint_dir=str(fsdp_dir),
-        output_path=str(Path(parsed_cfg.output_dir) / "merged"),
+        output_path=output_path,
         safe_serialization=True,
     )
+    state = PartialState()
+    state.wait_for_everyone()
+    LOG.info(
+        f"FSDP SHARDED_STATE_DICT weights successfully merged to: {output_path}",
+        main_process_only=True,
+    )
+    LOG.info(
+        "Merged weights are only the safetensors and doesn't include the model configuration "
+        f"or tokenizer which may be found in {parsed_cfg.output_dir}.",
+        main_process_only=True,
+    )
 
 
 if __name__ == "__main__":

src/axolotl/cli/preprocess.py

@@ -73,7 +73,7 @@ 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
 
@@ -95,9 +95,10 @@ 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"
-    parsed_cfg = load_cfg(config, **kwargs)
+    is_preprocess = kwargs.pop("is_preprocess", True)
+    parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/train.py

@@ -59,7 +59,7 @@ 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)
     parser = HfArgumentParser(TrainerCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

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/sweeps.py

@@ -3,11 +3,12 @@
 import random
 from copy import deepcopy
 from itertools import product
+from typing import Any
 
 
 def generate_sweep_configs(
     base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
+) -> list[dict[str, Any]]:
     """
     Recursively generates all possible configurations by applying sweeps to the base config.
 
@@ -48,7 +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)
@@ -57,7 +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

@@ -2,7 +2,9 @@
 
 import os
 import subprocess  # nosec
+import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -64,10 +66,18 @@ def build_command(base_cmd: list[str], options: dict[str, Any]) -> list[str]:
     return cmd
 
 
-def generate_config_files(config: str, sweep: str | None) -> Iterator[str]:
-    """Generate list of configuration files to process."""
+def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str, bool]]:
+    """
+    Generate list of configuration files to process. Yields a tuple of the configuration file name and a boolean indicating
+    whether this is a group of configurations (i.e., a sweep).
+
+    Args:
+        config: Base configuration file
+        sweep: Sweep configuration file
+    """
+
     if not sweep:
-        yield config
+        yield config, False
         return
 
     # Load sweep and base configurations
@@ -78,8 +88,13 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[str]:
 
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
-    for permutation in permutations:
-        # pylint: disable=consider-using-with
+    is_group = len(permutations) > 1
+    base_output_dir = base_config.get("output_dir", "./model-out")
+    for idx, permutation in enumerate(permutations, start=1):
+        permutation_dir = Path(permutation.get("output_dir", base_output_dir))
+        permutation_id = f"sweep{idx:04d}"
+        permutation["output_dir"] = str(permutation_dir / permutation_id)
+
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",
             suffix=".yaml",
@@ -88,7 +103,7 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[str]:
         )
         yaml.dump(permutation, temp_file)
         temp_file.close()
-        yield temp_file.name
+        yield temp_file.name, is_group
 
 
 def launch_training(
@@ -97,6 +112,7 @@ def launch_training(
     cloud: str | None,
     kwargs: dict,
     launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training with the given configuration."""
     launcher_args = launcher_args or []
@@ -105,11 +121,14 @@ def launch_training(
         _launch_cloud_training(cloud, cfg_file, launcher, kwargs, launcher_args)
     elif launcher:
         if launcher == "accelerate":
-            _launch_accelerate_training(cfg_file, kwargs, launcher_args)
+            _launch_accelerate_training(cfg_file, kwargs, launcher_args, use_exec)
         elif launcher == "torchrun":
-            _launch_torchrun_training(cfg_file, kwargs, launcher_args)
+            _launch_torchrun_training(cfg_file, kwargs, launcher_args, use_exec)
         elif launcher == "python":
             _launch_python_training(cfg_file, kwargs)
+    elif launcher is None:
+        # handle ray train launch
+        _launch_python_training(cfg_file, kwargs)
 
 
 def _launch_cloud_training(
@@ -136,7 +155,10 @@ def _launch_cloud_training(
 
 
 def _launch_accelerate_training(
-    cfg_file: str, kwargs: dict, launcher_args: list[str] | None = None
+    cfg_file: str,
+    kwargs: dict,
+    launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training via accelerate launcher."""
     launcher_args = launcher_args or []
@@ -161,11 +183,20 @@ def _launch_accelerate_training(
         base_cmd.append(cfg_file)
 
     cmd = build_command(base_cmd, kwargs)
-    subprocess.run(cmd, check=True)  # nosec B603
+    if use_exec:
+        # make sure to flush stdout and stderr before replacing the process
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os.execvpe(cmd[0], cmd, os.environ)  # nosec B606
+    else:
+        subprocess.run(cmd, check=True)  # nosec B603
 
 
 def _launch_torchrun_training(
-    cfg_file: str, kwargs: dict, launcher_args: list[str] | None = None
+    cfg_file: str,
+    kwargs: dict,
+    launcher_args: list[str] | None = None,
+    use_exec: bool = False,
 ) -> None:
     """Execute training via torchrun launcher."""
     launcher_args = launcher_args or []
@@ -178,7 +209,13 @@ def _launch_torchrun_training(
         base_cmd.append(cfg_file)
 
     cmd = build_command(base_cmd, kwargs)
-    subprocess.run(cmd, check=True)  # nosec B603
+    if use_exec:
+        # make sure to flush stdout and stderr before replacing the process
+        sys.stdout.flush()
+        sys.stderr.flush()
+        os.execvpe(cmd[0], cmd, os.environ)  # nosec B606
+    else:
+        subprocess.run(cmd, check=True)  # nosec B603
 
 
 def _launch_python_training(cfg_file: str, kwargs: dict) -> None:

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

@@ -44,7 +44,7 @@ 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 +260,14 @@ 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 +414,8 @@ 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"] = (

src/axolotl/core/builders/causal.py

@@ -344,16 +344,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             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

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

@@ -1,11 +1,9 @@
 """Init for axolotl.core.trainers"""
 
-# pylint: disable=unused-import
 # flake8: noqa
 
 from .base import AxolotlTrainer
 from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOSequenceParallelTrainer, AxolotlGRPOTrainer
 from .mamba import AxolotlMambaTrainer
 from .trl import (
     AxolotlCPOTrainer,

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
@@ -285,9 +283,9 @@ 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)
@@ -443,7 +441,7 @@ 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,
@@ -524,9 +522,7 @@ 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()
 
@@ -540,7 +536,6 @@ 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]:

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."}
     )

src/axolotl/datasets.py

@@ -26,7 +26,7 @@ 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,
@@ -99,7 +99,7 @@ class ConstantLengthDataset(IterableDataset):
         seq_length: Length of token sequences to return.
     """
 
-    def __init__(  # pylint: disable=super-init-not-called
+    def __init__(
         self,
         tokenizer,
         datasets,

src/axolotl/evaluate.py

@@ -79,7 +79,7 @@ def evaluate(*, cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> Dict[str, f
     model, tokenizer, _, processor = setup_model_and_tokenizer(cfg)
 
     # Get datasets
-    # pylint: disable=duplicate-code
+
     train_dataset = dataset_meta.train_dataset
     eval_dataset = dataset_meta.eval_dataset
     total_num_steps = dataset_meta.total_num_steps

src/axolotl/integrations/base.py

@@ -76,8 +76,8 @@ class BasePlugin:
     def __init__(self):
         """Initializes the BasePlugin."""
 
-    def register(self, cfg: DictDefault):  # pylint: disable=unused-argument
-        """Registers the plugin with the given configuration.
+    def register(self, cfg: dict):
+        """Registers the plugin with the given configuration as an unparsed dict.
 
         Args:
             cfg: The configuration for the plugin.
@@ -104,14 +104,13 @@ class BasePlugin:
             dataset_meta: The metadata for the training dataset.
         """
 
-    def pre_model_load(self, cfg: DictDefault):  # pylint: disable=unused-argument
+    def pre_model_load(self, cfg: DictDefault):
         """Performs actions before the model is loaded.
 
         Args:
             cfg: The configuration for the plugin.
         """
 
-    # pylint: disable=unused-argument
     def post_model_build(self, cfg: DictDefault, model: PreTrainedModel):
         """Performs actions after the model is built/loaded, but before any adapters are applied.
 
@@ -119,7 +118,6 @@ class BasePlugin:
             cfg: The configuration for the plugin.
         """
 
-    # pylint: disable=unused-argument
     def pre_lora_load(self, cfg: DictDefault, model: PreTrainedModel):
         """Performs actions before LoRA weights are loaded.
 
@@ -128,7 +126,6 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
     def post_lora_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after LoRA weights are loaded.
 
@@ -137,7 +134,6 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
     def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after the model is loaded.
 
@@ -146,7 +142,6 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    # pylint: disable=unused-argument
     def get_trainer_cls(self, cfg: DictDefault) -> Trainer | None:
         """Returns a custom class for the trainer.
 
@@ -157,7 +152,6 @@ class BasePlugin:
             The first non-`None` trainer class returned by a plugin.
         """
 
-    # pylint: disable=unused-argument
     def post_trainer_create(self, cfg: DictDefault, trainer: Trainer):
         """Performs actions after the trainer is created.
 
@@ -166,7 +160,7 @@ class BasePlugin:
             trainer: The trainer object for training.
         """
 
-    def get_training_args(self, cfg: DictDefault):  # pylint: disable=unused-argument):
+    def get_training_args(self, cfg: DictDefault):
         """
         Returns custom training arguments to set on TrainingArgs.
 
@@ -177,9 +171,7 @@ class BasePlugin:
             object: dict containing the training arguments.
         """
 
-    def get_collator_cls_and_kwargs(
-        self, cfg: DictDefault, is_eval: bool = False
-    ):  # pylint: disable=unused-argument):
+    def get_collator_cls_and_kwargs(self, cfg: DictDefault, is_eval: bool = False):
         """
         Returns a custom class for the collator.
 
@@ -191,7 +183,6 @@ class BasePlugin:
             class: The class for the collator.
         """
 
-    # pylint: disable=unused-argument
     def create_optimizer(self, cfg: DictDefault, trainer: Trainer) -> Optimizer | None:
         """Creates and returns an optimizer for training.
 
@@ -203,7 +194,6 @@ class BasePlugin:
             The created optimizer.
         """
 
-    # pylint: disable=unused-argument
     def create_lr_scheduler(
         self,
         cfg: DictDefault,
@@ -223,7 +213,6 @@ class BasePlugin:
             The created learning rate scheduler.
         """
 
-    # pylint: disable=unused-argument
     def add_callbacks_pre_trainer(
         self, cfg: DictDefault, model: PreTrainedModel
     ) -> list[Callable]:
@@ -238,7 +227,6 @@ class BasePlugin:
         """
         return []
 
-    # pylint: disable=unused-argument
     def add_callbacks_post_trainer(
         self, cfg: DictDefault, trainer: Trainer
     ) -> list[Callable]:
@@ -254,7 +242,6 @@ class BasePlugin:
         """
         return []
 
-    # pylint: disable=unused-argument
     def post_train(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
         """Performs actions after training is complete.
 
@@ -263,7 +250,7 @@ class BasePlugin:
             model: The loaded model.
         """
 
-    def post_train_unload(self, cfg: DictDefault):  # pylint: disable=unused-argument
+    def post_train_unload(self, cfg: DictDefault):
         """Performs actions after training is complete and the model is unloaded.
 
         Args:
@@ -311,7 +298,7 @@ def load_plugin(plugin_name: str) -> BasePlugin:
     return plugin
 
 
-class PluginManager:  # pylint: disable=too-many-public-methods
+class PluginManager:
     """The `PluginManager` class is responsible for loading and managing plugins. It
     should be a singleton so it can be accessed from anywhere in the codebase.
 

src/axolotl/integrations/config.py

@@ -50,15 +50,9 @@ def merge_input_args():
         dynamic_input += f"class AxolotlInputConfig(AxolotlInputConfigBase, {', '.join(plugin_classes)}):\n    pass\n"
 
         namespace: Dict[Any, Any] = {}
-        exec(  # pylint: disable=exec-used  # nosec B102
-            dynamic_input, globals(), namespace
-        )
-        AxolotlInputConfig = namespace[  # pylint: disable=invalid-name
-            "AxolotlInputConfig"
-        ]
-        AxolotlConfigWCapabilities = namespace[  # pylint: disable=invalid-name
-            "AxolotlConfigWCapabilities"
-        ]
+        exec(dynamic_input, globals(), namespace)  # nosec B102
+        AxolotlInputConfig = namespace["AxolotlInputConfig"]
+        AxolotlConfigWCapabilities = namespace["AxolotlConfigWCapabilities"]
         return AxolotlConfigWCapabilities, AxolotlInputConfig
     return AxolotlConfigWCapabilitiesBase, AxolotlInputConfigBase
 
@@ -74,7 +68,7 @@ def merge_training_args() -> Type:
     Returns:
     tuple: A tuple containing the newly created classes, AxolotlTrainingMixins.
     """
-    # pylint: disable=duplicate-code
+
     from axolotl.core.training_args_base import (
         AxolotlTrainingMixins as AxolotlTrainingMixinsBase,
     )
@@ -93,11 +87,7 @@ def merge_training_args() -> Type:
 
         namespace: Dict[Any, Any] = {}
         local_vars = {"AxolotlTrainingMixinsBase": AxolotlTrainingMixinsBase}
-        exec(  # pylint: disable=exec-used  # nosec B102
-            dynamic_input, {**globals(), **local_vars}, namespace
-        )
-        AxolotlTrainingMixins = namespace[  # pylint: disable=invalid-name
-            "AxolotlTrainingMixins"
-        ]
+        exec(dynamic_input, {**globals(), **local_vars}, namespace)  # nosec B102
+        AxolotlTrainingMixins = namespace["AxolotlTrainingMixins"]
         return AxolotlTrainingMixins
     return AxolotlTrainingMixinsBase

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -18,6 +18,7 @@ Module for the Plugin for Cut Cross Entropy integration with Axolotl.
 Cut Cross Entropy is an optimized implementation of cross entropy loss
 from Apple's ML team.
 """
+
 import importlib
 from functools import partial
 
@@ -28,7 +29,7 @@ from axolotl.utils import get_pytorch_version
 from axolotl.utils.callbacks.models import get_causal_lm_model_cls_prefix
 from axolotl.utils.logging import get_logger
 
-from .args import CutCrossEntropyArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import CutCrossEntropyArgs as CutCrossEntropyArgs
 
 LOG = get_logger(__name__)
 
@@ -106,9 +107,7 @@ class CutCrossEntropyPlugin(BasePlugin):
         """
         from cut_cross_entropy.transformers.patch import PATCH_FNS
 
-        def patch_generic(
-            maybe_model, patch_options, model_type: str
-        ):  # pylint: disable=unused-argument
+        def patch_generic(maybe_model, patch_options, model_type: str):
             import cut_cross_entropy.transformers.llama
             from cut_cross_entropy.transformers.llama import cce_forward
 
@@ -121,12 +120,10 @@ class CutCrossEntropyPlugin(BasePlugin):
                 )
                 model_cls = getattr(module, f"{model_cls_prefix}ForCausalLM")
 
-                cut_cross_entropy.transformers.llama._PATCH_OPTS = (  # pylint: disable=protected-access
-                    patch_options
-                )
+                cut_cross_entropy.transformers.llama._PATCH_OPTS = patch_options
 
                 model_cls.forward = cce_forward
-            # pylint: disable=duplicate-code
+
             except (ImportError, AttributeError) as e:
                 raise RuntimeError(
                     f"Could not import ForCausalLM class for model_type: {model_type}. "

src/axolotl/integrations/cut_cross_entropy/args.py

@@ -15,6 +15,7 @@
 """
 Module for handling Cut Cross Entropy input arguments.
 """
+
 from typing import Optional
 
 from pydantic import BaseModel, model_validator

src/axolotl/integrations/grokfast/__init__.py

@@ -7,7 +7,7 @@ from transformers.trainer_callback import TrainerCallback
 from axolotl.utils.logging import get_logger
 
 from ..base import BasePlugin
-from .args import GrokfastArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import GrokfastArgs as GrokfastArgs
 from .optimizer import gradfilter_ema
 
 LOG = get_logger(__name__)
@@ -24,12 +24,10 @@ class GrokfastCallbackHandler(TrainerCallback):
         self.alpha = alpha
         self.lamb = lamb
 
-    def on_train_begin(self, *args_, **kwargs):  # pylint: disable=unused-argument
+    def on_train_begin(self, *args_, **kwargs):
         self.grads = None
 
-    def on_pre_optimizer_step(
-        self, args_, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
+    def on_pre_optimizer_step(self, args_, state, control, **kwargs):
         model = kwargs.pop("model")
         self.grads = gradfilter_ema(model, self.grads, alpha=self.alpha, lamb=self.lamb)
         return control

src/axolotl/integrations/grokfast/optimizer.py

@@ -1,7 +1,6 @@
 # Copyright: MIT License (c) 2024 Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee
 # Reference: https://github.com/ironjr/grokfast
 
-# pylint: skip-file
 from collections import deque
 from typing import Dict, Literal, Optional
 

src/axolotl/integrations/kd/__init__.py

@@ -15,6 +15,7 @@
 """
 Plugin init to add KD support to Axolotl.
 """
+
 from typing import Any
 
 from transformers import Trainer
@@ -22,7 +23,7 @@ from transformers import Trainer
 from axolotl.integrations.base import BasePlugin
 from axolotl.integrations.kd.callbacks import KDTemperatureSchedulerCallback
 
-from .args import KDArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import KDArgs as KDArgs
 
 
 class KDPlugin(BasePlugin):

src/axolotl/integrations/kd/args.py

@@ -15,6 +15,7 @@
 """
 Plugin args for KD support.
 """
+
 from dataclasses import dataclass
 from enum import Enum
 
@@ -26,8 +27,8 @@ class InferenceServerType(str, Enum):
     Online inferences server types to handle different request args
     """
 
-    vllm = "vllm"  # pylint: disable=invalid-name
-    sglang = "sglang"  # pylint: disable=invalid-name
+    vllm = "vllm"
+    sglang = "sglang"
 
 
 class KDArgs(BaseModel):

src/axolotl/integrations/kd/callbacks.py

@@ -19,9 +19,7 @@ class KDTemperatureSchedulerCallback(TrainerCallback):
 
         self.trainer = trainer
 
-    def on_step_end(
-        self, args, state, control, **kwargs
-    ):  # pylint: disable=unused-argument
+    def on_step_end(self, args, state, control, **kwargs):
         # cosine decay temperature over the max steps
 
         progress = state.global_step / state.max_steps

src/axolotl/integrations/kd/chat_template.py

@@ -15,6 +15,7 @@
 """
 Chat template prompt strategy loader with KD support
 """
+
 import logging
 from typing import Any, Dict
 
@@ -192,7 +193,6 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
         """
         Transform logprobs to target format for KD training
         """
-        # pylint: disable=duplicate-code
 
         logprobs = sample.pop(self.logprobs_field)
         target_seq_len = len(logprobs)
@@ -240,7 +240,7 @@ class ChatTemplateStrategyWithKDv2(ChatTemplateStrategyWithKD):
                 target_mask.append([1] * top_k)
 
         for token_pos_logprobs, pos_target_token_ids in zip(
-            logprobs, sample["target_token_ids"]
+            logprobs, sample["target_token_ids"], strict=False
         ):
             # Convert to a tensor for easier manipulation
             position_logprobs_tensor = torch.tensor(
@@ -299,7 +299,7 @@ class KDStrategyLoader(StrategyLoader):
     Load ChatTemplateStrategy with KD support using StrategyLoader.
     """
 
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
+    def _get_strategy_cls(self, cfg):
         return ChatTemplateStrategyWithKD
 
     def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
@@ -319,7 +319,7 @@ class KDStrategyLoaderV2(KDStrategyLoader):
     Load KD chat template datasets with pre-tokenized logprob data
     """
 
-    def _get_strategy_cls(self, cfg):  # pylint: disable=unused-argument
+    def _get_strategy_cls(self, cfg):
         return ChatTemplateStrategyWithKDv2
 
 

src/axolotl/integrations/kd/collator.py

@@ -37,7 +37,6 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
     target_logprobs. It also creates a teacher_mask to indicate which entries are valid.
     """
 
-    # pylint: disable=duplicate-code
     tokenizer: PreTrainedTokenizerBase
     model: Optional[Any] = None
     padding: Union[bool, str, PaddingStrategy] = True
@@ -72,7 +71,7 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
                         // self.pad_to_multiple_of
                     ) * self.pad_to_multiple_of
 
-                for f in features:  # pylint: disable=invalid-name
+                for f in features:
                     remainder = [pad_token_id] * (max_len - len(f[feature_name]))
                     if isinstance(f[feature_name], list):
                         f[feature_name] = (
@@ -101,7 +100,7 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
 
         if has_teacher_data:
             # Extract and remove from features
-            for f in features:  # pylint: disable=invalid-name
+            for f in features:
                 target_logprobs_list.append(f.pop("target_logprobs"))
                 target_token_ids_list.append(f.pop("target_token_ids"))
                 target_mask_list.append(f.pop("target_mask"))
@@ -117,24 +116,25 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
             padded_teacher_mask_list = []
 
             for t_logprobs, t_ids, t_mask in zip(
-                target_logprobs_list, target_token_ids_list, target_mask_list
+                target_logprobs_list,
+                target_token_ids_list,
+                target_mask_list,
+                strict=False,
             ):
                 t_logprobs_padded = []
                 t_ids_padded = []
                 t_mask_padded = []
 
-                for lp, ids, mask in zip(  # pylint: disable=invalid-name
-                    t_logprobs, t_ids, t_mask
-                ):
+                for lp, ids, mask in zip(t_logprobs, t_ids, t_mask, strict=False):
                     lp_len = len(lp)
                     if lp_len < max_k:
                         # Use -1e9 for padding logprobs and 0 for token_ids
                         pad_len = max_k - lp_len
-                        lp = lp + [-1e9] * pad_len  # pylint: disable=invalid-name
+                        lp = lp + [-1e9] * pad_len
                         ids = ids + [0] * pad_len
                         mask = mask + [0] * pad_len
                     else:
-                        lp = lp[:max_k]  # pylint: disable=invalid-name
+                        lp = lp[:max_k]
                         ids = ids[:max_k]
                         mask = mask[:max_k]
 
@@ -216,9 +216,7 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
         #    We want to produce a single "merged" feature dict for each sub-batch.
         out_features = [{} for _ in features]
 
-        for i, sub_features in enumerate(  # pylint: disable=too-many-nested-blocks
-            features
-        ):
+        for i, sub_features in enumerate(features):
             # sub_features is a list of dicts, each dict = one sequence’s features
             # We'll merge them into out_features[i].
             #
@@ -255,9 +253,7 @@ class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
                         if field_name in feat and isinstance(
                             feat[field_name], (list, torch.Tensor)
                         ):
-                            if isinstance(
-                                feat[field_name][0], (dict, str)
-                            ):  # pylint: disable=too-many-nested-blocks
+                            if isinstance(feat[field_name][0], (dict, str)):
                                 continue
                             arr = np.array(feat[field_name])
                             arrays.append(arr)

src/axolotl/integrations/kd/collator_online_teacher.py

@@ -144,7 +144,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 }
 
             for sequence_data, seq_input_ids, seq_labels in zip(
-                api_data, batch_input_ids, labels
+                api_data, batch_input_ids, labels, strict=False
             ):
                 current_target_logprobs = []
                 current_target_token_ids = []
@@ -165,7 +165,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 assert len(seq_input_ids) == len(input_top_logprobs)
 
                 for i, _, label in zip(
-                    range(len(seq_input_ids)), seq_input_ids, seq_labels
+                    range(len(seq_input_ids)), seq_input_ids, seq_labels, strict=False
                 ):
                     if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
                         # this is always the case for the first token.
@@ -202,7 +202,8 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
 
                         # pos_top_logprobs: list of logprobs, pos_token_ids: list of token_ids
                         pos_logprobs_raw, pos_token_ids, _ = [
-                            list(row) for row in zip(*pos_top_logprobs_data)
+                            list(row)
+                            for row in zip(*pos_top_logprobs_data, strict=False)
                         ]
 
                         # Ensure correct length (top_k)
@@ -317,7 +318,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                 }
 
             for sequence_data, seq_input_ids, seq_labels in zip(
-                choices, batch_input_ids, labels
+                choices, batch_input_ids, labels, strict=False
             ):
                 # seq_input_ids: List[int]
                 # seq_labels: List[int]
@@ -342,7 +343,9 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
 
                 seq_len = len(seq_input_ids)
 
-                for i, _, label in zip(range(seq_len), seq_input_ids, seq_labels):
+                for i, _, label in zip(
+                    range(seq_len), seq_input_ids, seq_labels, strict=False
+                ):
                     if i < len(input_top_logprobs) and input_top_logprobs[i] is None:
                         # this is always the case for the first token.
                         # there is never logprob data for the first token since that's a true input
@@ -424,7 +427,7 @@ class OnlineTeacherCollator(KDBatchSamplerDataCollatorForSeq2Seq):
                             list(range(self.kd_online_topk))
                         )
                         current_target_mask.append([0] * self.kd_online_topk)
-                for i in range(max(0, seq_len - len(current_target_logprobs))):
+                for _ in range(max(0, seq_len - len(current_target_logprobs))):
                     current_target_logprobs.append(
                         [-float("inf")] * self.kd_online_topk
                     )

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

@@ -197,7 +197,7 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
         compute_ce_loss: bool = True,
         normalize_topk: bool = True,
     ):
-        CHUNK_SIZE = chunk_size  # pylint: disable=invalid-name
+        CHUNK_SIZE = chunk_size
         grad_weight_acc = torch.zeros_like(student_lm_head_weight)
         grad_inputs_list = []
         grad_bias_acc = (
@@ -298,8 +298,8 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
             accumulate_chunk_grads_compiled = accumulate_chunk_grads
 
         # Use the same chunking logic as LigerFusedLinearDistillationBase.forward
-        B, N, D = student_input.shape  # pylint: disable=invalid-name
-        K = target_token_ids.shape[-1]  # pylint: disable=invalid-name
+        B, N, D = student_input.shape
+        K = target_token_ids.shape[-1]
 
         student_input_flat = student_input.reshape(-1, student_input.shape[-1])
         target_token_ids_flat = target_token_ids.reshape(-1, target_token_ids.shape[-1])

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

@@ -40,10 +40,9 @@ def kldiv_forward_llama_like(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,  # pylint: disable=unused-argument
+    logits_to_keep: Union[int, torch.Tensor] = 0,
     **kwargs: Unpack[TransformersKwargs],  # type: ignore[misc]
 ) -> CausalLMOutputWithPast:
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None

src/axolotl/integrations/kd/topk_logprob/forward_kl.py

@@ -15,6 +15,7 @@
 """
 loss for top_k KL divergence
 """
+
 import torch
 from torch import nn
 
@@ -117,7 +118,6 @@ class ChunkedTopKKDLoss(nn.Module):
         target_mask: torch.Tensor,  # [B, seq_len, K]
         num_items_in_batch: int = -1,  # optional batch size for normalization
     ) -> torch.Tensor:
-
         # 1. Split along the "token" dimension (dim=1).
         student_logits_chunks = student_logits.chunk(self.num_output_chunks, dim=1)
         token_ids_chunks = target_token_ids.chunk(self.num_output_chunks, dim=1)
@@ -131,7 +131,11 @@ class ChunkedTopKKDLoss(nn.Module):
 
         # 2. Loop over each chunk and compute a chunk-specific loss.
         for st_chunk, tid_chunk, lp_chunk, msk_chunk in zip(
-            student_logits_chunks, token_ids_chunks, logprobs_chunks, mask_chunks
+            student_logits_chunks,
+            token_ids_chunks,
+            logprobs_chunks,
+            mask_chunks,
+            strict=False,
         ):
             # We pass num_items_in_batch=-1 so that the kd_loss
             # will average over *this chunk's* valid tokens only.

src/axolotl/integrations/kd/trainer.py

@@ -21,7 +21,6 @@ from axolotl.core.trainers.base import AxolotlTrainer
 from .kernels.liger import LigerFusedLinearKLTopKLogprobLoss
 
 
-# pylint: disable=too-many-ancestors
 class AxolotlKDTrainer(AxolotlTrainer):
     """
     Custom trainer subclass for Knowledge Distillation (KD)

src/axolotl/integrations/liger/__init__.py

@@ -18,6 +18,7 @@ Module for the Plugin for LIGER integraton with Axolotl.
 Liger Kernel is the collection of Triton-native kernels for LLM Training.
 It is designed to be performant, correct, and light-weight.
 """
+
 from .args import LigerArgs
 from .plugin import LigerPlugin
 

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

@@ -41,7 +41,6 @@ def lce_forward(
             This is useful when using packed tensor format (single dimension for batch and sequence length).
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -181,7 +180,7 @@ def patch_lce_forward(
         model_cls = getattr(module, f"{model_cls_prefix}ForCausalLM")
 
         model_cls.forward = lce_forward
-    # pylint: disable=duplicate-code
+
     except (ImportError, AttributeError) as e:
         raise RuntimeError(
             f"Could not import ForCausalLM class for model_type: {model_type}. "

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

@@ -2,8 +2,6 @@
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
 
-# pylint: disable=duplicate-code
-
 from typing import List, Optional, Tuple, Union
 
 import torch

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

@@ -2,8 +2,6 @@
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
 
-# pylint: disable=duplicate-code
-
 from typing import Optional, Tuple, Union
 
 import torch

src/axolotl/integrations/liger/models/llama4.py

@@ -46,7 +46,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -78,9 +77,7 @@ def lce_forward(
     hidden_states = outputs[0]
 
     if hasattr(self.config, "pretraining_tp") and self.config.pretraining_tp > 1:
-        raise Exception(  # pylint: disable=broad-exception-raised
-            "Liger Kernel does not support pretraining_tp!!"
-        )
+        raise Exception("Liger Kernel does not support pretraining_tp!!")
 
     logits = None
     loss = None
@@ -128,7 +125,7 @@ def apply_liger_kernel_to_llama4(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
@@ -144,15 +141,15 @@ def apply_liger_kernel_to_llama4(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.llama4.modeling_llama4  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.llama4.modeling_llama4  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_llama4 = sys.modules["transformers.models.llama4.modeling_llama4"]
 
@@ -165,7 +162,7 @@ def apply_liger_kernel_to_llama4(
             # clone config to avoid modifying the original
             config = deepcopy(config)
             if intermediate_size:
-                setattr(config, "intermediate_size", intermediate_size)
+                config.intermediate_size = intermediate_size
             return LigerSwiGLUMLP(config, **kwargs)
 
         modeling_llama4.Llama4TextMLP = _liger_swiglu_mlp_wrapper

src/axolotl/integrations/liger/models/qwen3.py

@@ -43,7 +43,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -113,9 +112,8 @@ def apply_liger_kernel_to_qwen3(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
-    # pylint: disable=duplicate-code
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
 
@@ -130,15 +128,15 @@ def apply_liger_kernel_to_qwen3(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.qwen3.modeling_qwen3  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.qwen3.modeling_qwen3  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_qwen3 = sys.modules["transformers.models.qwen3.modeling_qwen3"]
 

src/axolotl/integrations/liger/models/qwen3_moe.py

@@ -45,7 +45,6 @@ def lce_forward(
     Returns:
     """
 
-    # pylint: disable=duplicate-code
     output_attentions = (
         output_attentions
         if output_attentions is not None
@@ -135,9 +134,8 @@ def apply_liger_kernel_to_qwen3_moe(
     rms_norm: bool = False,
     glu_activation: bool = False,
     layer_norm: bool = False,
-    **kwargs,  # pylint: disable=unused-argument
+    **kwargs,
 ) -> None:
-    # pylint: disable=duplicate-code
     """
     Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
 
@@ -152,15 +150,15 @@ def apply_liger_kernel_to_qwen3_moe(
         layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
     """
 
-    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401  # pylint: disable=unused-import
+    import transformers.models.qwen3_moe.modeling_qwen3_moe  # noqa: F401
     from liger_kernel.transformers.functional import liger_cross_entropy
     from liger_kernel.transformers.layer_norm import LigerLayerNorm
     from liger_kernel.transformers.rms_norm import LigerRMSNorm
     from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     modeling_qwen3_moe = sys.modules["transformers.models.qwen3_moe.modeling_qwen3_moe"]
 
@@ -174,7 +172,7 @@ def apply_liger_kernel_to_qwen3_moe(
             # clone config to avoid modifying the original
             config = deepcopy(config)
             if intermediate_size:
-                setattr(config, "intermediate_size", intermediate_size)
+                config.intermediate_size = intermediate_size
             return LigerSwiGLUMLP(config, **kwargs)
 
         modeling_qwen3_moe.Qwen3MoeMLP = _liger_swiglu_mlp_wrapper

src/axolotl/integrations/lm_eval/__init__.py

@@ -7,7 +7,7 @@ import subprocess  # nosec
 from axolotl.integrations.base import BasePlugin
 from axolotl.integrations.lm_eval.cli import build_lm_eval_command
 
-from .args import LMEvalArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import LMEvalArgs as LMEvalArgs
 
 
 class LMEvalPlugin(BasePlugin):
@@ -20,7 +20,6 @@ class LMEvalPlugin(BasePlugin):
 
     def post_train_unload(self, cfg):
         if cfg.lm_eval_post_train:
-            # pylint: disable=duplicate-code
             for lm_eval_args in build_lm_eval_command(
                 cfg.lm_eval_tasks,
                 bfloat16=cfg.bfloat16 or cfg.bf16,

src/axolotl/integrations/lm_eval/cli.py

@@ -99,7 +99,6 @@ def lm_eval(config: str, cloud: Optional[str] = None):
         with open(config, encoding="utf-8") as file:
             cfg: DictDefault = DictDefault(yaml.safe_load(file))
 
-        # pylint: disable=duplicate-code
         for lm_eval_args in build_lm_eval_command(
             cfg.lm_eval_tasks,
             bfloat16=cfg.bfloat16 or cfg.bf16,

src/axolotl/integrations/spectrum/__init__.py

@@ -23,7 +23,7 @@ import requests
 from axolotl.integrations.base import BasePlugin
 from axolotl.utils.logging import get_logger
 
-from .args import SpectrumArgs  # pylint: disable=unused-import. # noqa: F401
+from .args import SpectrumArgs as SpectrumArgs
 
 LOG = get_logger(__name__)
 
@@ -46,7 +46,7 @@ def _generate_unfrozen_params_yaml(snr_data, top_fraction=0.5):
         "^lm_head.weight$",
         "^model.embed_tokens.weight$",
     ]
-    for layer_type, layer_names in top_layers_by_type.items():
+    for _, layer_names in top_layers_by_type.items():
         for layer_name in layer_names:
             unfrozen_parameters.append(layer_name)
     return unfrozen_parameters
@@ -84,7 +84,7 @@ class SpectrumPlugin(BasePlugin):
                 snr_data = json.load(fin)
         except FileNotFoundError:
             pass
-        except Exception as exc:  # pylint: disable=broad-exception-caught
+        except Exception as exc:
             LOG.warning(f"Failed to read SNR data from {snr_path}: {exc}")
 
         if not snr_data:

src/axolotl/integrations/spectrum/args.py

@@ -15,6 +15,7 @@
 """
 Module for handling Spectrum input arguments.
 """
+
 from typing import Optional
 
 from pydantic import BaseModel, model_validator