Update .pre-commit-config.yaml

Co-authored-by: Wing Lian <wing.lian@gmail.com>

.github/workflows/precommit-autoupdate.yml

@@ -0,0 +1,49 @@
+name: Pre-commit auto-update
+
+on:
+  schedule:
+    - cron: '0 0 * * 0'  # Run weekly
+  workflow_dispatch:  # Manual kickoff
+
+jobs:
+  auto-update:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Update pre-commit hooks
+        id: update
+        run: |
+          pip install pre-commit
+          pre-commit autoupdate
+          if [[ -n $(git status --porcelain) ]]; then
+            echo "changes=true" >> $GITHUB_OUTPUT
+            git diff .pre-commit-config.yaml > pre-commit-update.diff
+          fi
+
+      - name: Create Pull Request
+        if: steps.update.outputs.changes == 'true'
+        uses: peter-evans/create-pull-request@v6
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          branch: update/pre-commit-hooks
+          delete-branch: true
+          title: "chore: update pre-commit hooks"
+          commit-message: "chore: update pre-commit hooks"
+          body: |
+            Automated PR to update pre-commit hooks to their latest versions.
+
+            <details>
+            <summary>Changes:</summary>
+
+            ```diff
+            ${{ steps.update.outputs.diff }}
+            ```
+            </details>

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -11,23 +11,23 @@ repos:
     -   id: no-commit-to-branch
         args: ['--branch', 'main']
 -   repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 25.1.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 6.0.1
     hooks:
       - id: isort
 -   repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.1.2
     hooks:
     - id: flake8
 -   repo: https://github.com/pylint-dev/pylint
-    rev: c8c96d20cde3552a79858c7456bb1483bf83d633
+    rev: v3.3.6
     hooks:
     - id: pylint
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.3.0
+    rev: v1.15.0
     hooks:
     - id: mypy
       additional_dependencies:
@@ -36,7 +36,7 @@ repos:
             'pydantic>=2.5.3',
         ]
 -   repo: https://github.com/PyCQA/bandit
-    rev: 1.7.5
+    rev: 1.8.3
     hooks:
     -   id: bandit
         args: [

cicd/multigpu.py

@@ -1,6 +1,7 @@
 """
- modal application to run axolotl gpu tests in Modal
- """
+modal application to run axolotl gpu tests in Modal
+"""
+
 # pylint: disable=duplicate-code
 
 import os

cicd/tests.py

@@ -1,4 +1,5 @@
 """Modal app to run axolotl GPU tests"""
+
 # pylint: disable=duplicate-code
 
 import os

scripts/chat_datasets.py

@@ -1,6 +1,7 @@
 """
 helper script to parse chat datasets into a usable yaml
 """
+
 import click
 import yaml
 from datasets import load_dataset

scripts/cutcrossentropy_install.py

@@ -1,4 +1,5 @@
 """Script to output the correct installation command for cut-cross-entropy."""
+
 import importlib.util
 import sys
 
@@ -17,12 +18,12 @@ if v < V("2.4.0"):
 
 cce_spec = importlib.util.find_spec("cut_cross_entropy")
 
-uninstall_prefix = ""
+UNINSTALL_PREFIX = ""
 if cce_spec:
     if not importlib.util.find_spec("cut_cross_entropy.transformers"):
-        uninstall_prefix = "pip uninstall -y cut-cross-entropy && "
+        UNINSTALL_PREFIX = "pip uninstall -y cut-cross-entropy && "
 
 print(
-    uninstall_prefix
+    UNINSTALL_PREFIX
     + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"'
 )

src/axolotl/cli/cloud/__init__.py

@@ -1,6 +1,7 @@
 """
 launch axolotl in supported cloud platforms
 """
+
 from pathlib import Path
 from typing import Union
 

src/axolotl/cli/cloud/base.py

@@ -1,6 +1,7 @@
 """
 base class for cloud platforms from cli
 """
+
 from abc import ABC, abstractmethod
 
 

src/axolotl/cli/cloud/modal_.py

@@ -1,6 +1,7 @@
 """
 Modal Cloud support from CLI
 """
+
 import copy
 import json
 import os

src/axolotl/cli/main.py

@@ -1,4 +1,5 @@
 """Click CLI definitions for various axolotl commands."""
+
 # pylint: disable=redefined-outer-name
 
 import logging

src/axolotl/cli/utils.py

@@ -5,7 +5,6 @@ import dataclasses
 import hashlib
 import json
 import logging
-import typing
 from functools import wraps
 from pathlib import Path
 from types import NoneType
@@ -24,7 +23,7 @@ configure_logging()
 LOG = logging.getLogger(__name__)
 
 
-def strip_optional_type(field_type: type | typing._SpecialForm | None):
+def strip_optional_type(field_type: type | str | None):
     """
     Extracts the non-`None` type from an `Optional` / `Union` type.
 

src/axolotl/convert.py

@@ -1,6 +1,5 @@
 """Module containing File Reader, File Writer, Json Parser, and Jsonl Serializer classes"""
 
-
 import json
 import sys
 

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

@@ -1,6 +1,7 @@
 """
 ChatML transformation functions for MessageContents
 """
+
 from typing import Optional
 
 from ..messages import MessageContents, Messages

src/axolotl/core/chat/format/llama3x.py

@@ -1,6 +1,7 @@
 """
 Llama 3.x chat formatting functions for MessageContents
 """
+
 from typing import Optional
 
 from ..messages import MessageContents, Messages

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

@@ -1,6 +1,7 @@
 """
 shared functions for format transforms
 """
+
 from axolotl.core.chat.messages import MessageContents, Messages
 
 

src/axolotl/core/chat/messages.py

@@ -1,6 +1,7 @@
 """
 internal message representations of chat messages
 """
+
 import json
 from enum import Enum
 from typing import Any, Callable, List, Optional, Union

src/axolotl/core/datasets/chat.py

@@ -1,6 +1,7 @@
 """
 chat dataset module
 """
+
 import os
 from typing import Callable, Optional, Union
 

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

@@ -1,6 +1,7 @@
 """
 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
 
 

src/axolotl/core/trainer_builder.py

@@ -332,9 +332,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs = {}
 
         if self.cfg.include_tokens_per_second is not None:
-            training_arguments_kwargs[
-                "include_tokens_per_second"
-            ] = self.cfg.include_tokens_per_second
+            training_arguments_kwargs["include_tokens_per_second"] = (
+                self.cfg.include_tokens_per_second
+            )
 
         if self.cfg.bf16 == "full":
             training_arguments_kwargs["bf16_full_eval"] = True
@@ -351,13 +351,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["seed"] = self.cfg.seed
 
         if self.cfg.gradient_checkpointing:
-            training_arguments_kwargs[
-                "gradient_checkpointing"
-            ] = self.cfg.gradient_checkpointing
+            training_arguments_kwargs["gradient_checkpointing"] = (
+                self.cfg.gradient_checkpointing
+            )
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_arguments_kwargs[
-                    "gradient_checkpointing_kwargs"
-                ] = self.cfg.gradient_checkpointing_kwargs
+                training_arguments_kwargs["gradient_checkpointing_kwargs"] = (
+                    self.cfg.gradient_checkpointing_kwargs
+                )
         if self.cfg.fsdp:
             training_arguments_kwargs["fsdp"] = self.cfg.fsdp
             if self.cfg.fsdp_config:
@@ -373,9 +373,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["deepspeed"] = self.cfg.deepspeed
 
         if self.cfg.lr_quadratic_warmup is not None:
-            training_arguments_kwargs[
-                "lr_quadratic_warmup"
-            ] = self.cfg.lr_quadratic_warmup
+            training_arguments_kwargs["lr_quadratic_warmup"] = (
+                self.cfg.lr_quadratic_warmup
+            )
 
         if self.cfg.adam_beta1:
             training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
@@ -399,28 +399,28 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_arguments_kwargs[
-                "dataloader_pin_memory"
-            ] = self.cfg.dataloader_pin_memory
+            training_arguments_kwargs["dataloader_pin_memory"] = (
+                self.cfg.dataloader_pin_memory
+            )
         if self.cfg.dataloader_num_workers is not None:
-            training_arguments_kwargs[
-                "dataloader_num_workers"
-            ] = self.cfg.dataloader_num_workers
+            training_arguments_kwargs["dataloader_num_workers"] = (
+                self.cfg.dataloader_num_workers
+            )
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_arguments_kwargs[
-                "dataloader_prefetch_factor"
-            ] = self.cfg.dataloader_prefetch_factor
+            training_arguments_kwargs["dataloader_prefetch_factor"] = (
+                self.cfg.dataloader_prefetch_factor
+            )
         if self.cfg.dataloader_drop_last is not None:
-            training_arguments_kwargs[
-                "dataloader_drop_last"
-            ] = self.cfg.dataloader_drop_last
+            training_arguments_kwargs["dataloader_drop_last"] = (
+                self.cfg.dataloader_drop_last
+            )
         elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
             training_arguments_kwargs["dataloader_drop_last"] = True
 
         if self.cfg.remove_unused_columns is not None:
-            training_arguments_kwargs[
-                "remove_unused_columns"
-            ] = self.cfg.remove_unused_columns
+            training_arguments_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
 
         if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
@@ -452,9 +452,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.do_causal_lm_eval:
             training_arguments_kwargs["do_causal_lm_eval"] = self.cfg.do_causal_lm_eval
         if self.cfg.metric_for_best_model:
-            training_arguments_kwargs[
-                "metric_for_best_model"
-            ] = self.cfg.metric_for_best_model
+            training_arguments_kwargs["metric_for_best_model"] = (
+                self.cfg.metric_for_best_model
+            )
         if self.cfg.greater_is_better:
             training_arguments_kwargs["greater_is_better"] = self.cfg.greater_is_better
 
@@ -467,13 +467,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 )
                 training_arguments_kwargs["torch_compile"] = self.cfg.torch_compile
                 if self.cfg.torch_compile_backend:
-                    training_arguments_kwargs[
-                        "torch_compile_backend"
-                    ] = self.cfg.torch_compile_backend
+                    training_arguments_kwargs["torch_compile_backend"] = (
+                        self.cfg.torch_compile_backend
+                    )
                 if self.cfg.torch_compile_mode:
-                    training_arguments_kwargs[
-                        "torch_compile_mode"
-                    ] = self.cfg.torch_compile_mode
+                    training_arguments_kwargs["torch_compile_mode"] = (
+                        self.cfg.torch_compile_mode
+                    )
 
         # DDP Config
         if self.cfg.ddp_timeout:
@@ -482,32 +482,32 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.ddp_bucket_cap_mb:
             training_arguments_kwargs["ddp_bucket_cap_mb"] = self.cfg.ddp_bucket_cap_mb
         if self.cfg.ddp_broadcast_buffers is not None:
-            training_arguments_kwargs[
-                "ddp_broadcast_buffers"
-            ] = self.cfg.ddp_broadcast_buffers
+            training_arguments_kwargs["ddp_broadcast_buffers"] = (
+                self.cfg.ddp_broadcast_buffers
+            )
 
         # these are all the "standard" kwargs that are def used
         training_arguments_kwargs["max_steps"] = (
             total_num_steps if self.cfg.max_steps else -1
         )
         training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
-        training_arguments_kwargs[
-            "per_device_train_batch_size"
-        ] = self.cfg.micro_batch_size
+        training_arguments_kwargs["per_device_train_batch_size"] = (
+            self.cfg.micro_batch_size
+        )
         if self.cfg.eval_batch_size:
-            training_arguments_kwargs[
-                "per_device_eval_batch_size"
-            ] = self.cfg.eval_batch_size
+            training_arguments_kwargs["per_device_eval_batch_size"] = (
+                self.cfg.eval_batch_size
+            )
         if self.cfg.auto_find_batch_size is not None:
-            training_arguments_kwargs[
-                "auto_find_batch_size"
-            ] = self.cfg.auto_find_batch_size
-        training_arguments_kwargs[
-            "gradient_accumulation_steps"
-        ] = self.cfg.gradient_accumulation_steps
-        training_arguments_kwargs[
-            "eval_accumulation_steps"
-        ] = self.cfg.gradient_accumulation_steps
+            training_arguments_kwargs["auto_find_batch_size"] = (
+                self.cfg.auto_find_batch_size
+            )
+        training_arguments_kwargs["gradient_accumulation_steps"] = (
+            self.cfg.gradient_accumulation_steps
+        )
+        training_arguments_kwargs["eval_accumulation_steps"] = (
+            self.cfg.gradient_accumulation_steps
+        )
         training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
         training_arguments_kwargs["learning_rate"] = self.cfg.learning_rate
         training_arguments_kwargs["output_dir"] = self.cfg.output_dir
@@ -554,9 +554,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
-            training_arguments_kwargs[
-                "alternate_lr_scheduler_type"
-            ] = self.cfg.lr_scheduler
+            training_arguments_kwargs["alternate_lr_scheduler_type"] = (
+                self.cfg.lr_scheduler
+            )
         else:
             training_arguments_kwargs["lr_scheduler_type"] = (
                 self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
@@ -565,9 +565,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
-        training_arguments_kwargs[
-            "cosine_constant_lr_ratio"
-        ] = self.cfg.cosine_constant_lr_ratio
+        training_arguments_kwargs["cosine_constant_lr_ratio"] = (
+            self.cfg.cosine_constant_lr_ratio
+        )
         training_arguments_kwargs["weight_decay"] = (
             self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
         )
@@ -580,40 +580,40 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.eval_sample_packing
         )
         if self.cfg.sample_packing_bin_size is not None:
-            training_arguments_kwargs[
-                "sample_packing_bin_size"
-            ] = self.cfg.sample_packing_bin_size
+            training_arguments_kwargs["sample_packing_bin_size"] = (
+                self.cfg.sample_packing_bin_size
+            )
         if self.cfg.sample_packing_group_size is not None:
-            training_arguments_kwargs[
-                "sample_packing_group_size"
-            ] = self.cfg.sample_packing_group_size
+            training_arguments_kwargs["sample_packing_group_size"] = (
+                self.cfg.sample_packing_group_size
+            )
         if self.cfg.sample_packing_eff_est:
-            training_arguments_kwargs[
-                "sample_packing_efficiency"
-            ] = self.cfg.sample_packing_eff_est
+            training_arguments_kwargs["sample_packing_efficiency"] = (
+                self.cfg.sample_packing_eff_est
+            )
 
         if self.cfg.relora_steps:
             training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
-            training_arguments_kwargs[
-                "relora_warmup_steps"
-            ] = self.cfg.relora_warmup_steps
+            training_arguments_kwargs["relora_warmup_steps"] = (
+                self.cfg.relora_warmup_steps
+            )
             if self.cfg.relora_anneal_steps:
-                training_arguments_kwargs[
-                    "relora_anneal_steps"
-                ] = self.cfg.relora_anneal_steps
+                training_arguments_kwargs["relora_anneal_steps"] = (
+                    self.cfg.relora_anneal_steps
+                )
             if self.cfg.relora_prune_ratio:
-                training_arguments_kwargs[
-                    "relora_prune_ratio"
-                ] = self.cfg.relora_prune_ratio
+                training_arguments_kwargs["relora_prune_ratio"] = (
+                    self.cfg.relora_prune_ratio
+                )
 
         if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
             training_arguments_kwargs["lisa_n_layers"] = self.cfg.lisa_n_layers
-            training_arguments_kwargs[
-                "lisa_step_interval"
-            ] = self.cfg.lisa_step_interval
-            training_arguments_kwargs[
-                "lisa_layers_attribute"
-            ] = self.cfg.lisa_layers_attribute
+            training_arguments_kwargs["lisa_step_interval"] = (
+                self.cfg.lisa_step_interval
+            )
+            training_arguments_kwargs["lisa_layers_attribute"] = (
+                self.cfg.lisa_layers_attribute
+            )
 
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
@@ -627,9 +627,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             )
 
         if self.cfg.neftune_noise_alpha is not None:
-            training_arguments_kwargs[
-                "neftune_noise_alpha"
-            ] = self.cfg.neftune_noise_alpha
+            training_arguments_kwargs["neftune_noise_alpha"] = (
+                self.cfg.neftune_noise_alpha
+            )
 
         trainer_kwargs = {}
 
@@ -731,23 +731,23 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 importlib.import_module("torchdistx")
 
         if self.cfg.optim_target_modules:
-            training_arguments_kwargs[
-                "optim_target_modules"
-            ] = self.cfg.optim_target_modules
+            training_arguments_kwargs["optim_target_modules"] = (
+                self.cfg.optim_target_modules
+            )
 
         training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
         training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
 
         training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs[
-            "loraplus_lr_embedding"
-        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["loraplus_lr_embedding"] = (
+            self.cfg.loraplus_lr_embedding
+        )
         training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
         if self.cfg.accelerator_config:
-            training_arguments_kwargs[
-                "accelerator_config"
-            ] = self.cfg.accelerator_config
+            training_arguments_kwargs["accelerator_config"] = (
+                self.cfg.accelerator_config
+            )
 
         if self.cfg.kd_ce_alpha is not None:
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
@@ -756,13 +756,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.kd_temperature is not None:
             training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
         if self.cfg.kd_zscore_base_temp is not None:
-            training_arguments_kwargs[
-                "kd_zscore_base_temp"
-            ] = self.cfg.kd_zscore_base_temp
+            training_arguments_kwargs["kd_zscore_base_temp"] = (
+                self.cfg.kd_zscore_base_temp
+            )
         if self.cfg.kd_top_k_before_softmax is not None:
-            training_arguments_kwargs[
-                "kd_top_k_before_softmax"
-            ] = self.cfg.kd_top_k_before_softmax
+            training_arguments_kwargs["kd_top_k_before_softmax"] = (
+                self.cfg.kd_top_k_before_softmax
+            )
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -972,32 +972,32 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
         if self.cfg.remove_unused_columns is not None:
-            training_args_kwargs[
-                "remove_unused_columns"
-            ] = self.cfg.remove_unused_columns
+            training_args_kwargs["remove_unused_columns"] = (
+                self.cfg.remove_unused_columns
+            )
         else:
             training_args_kwargs["remove_unused_columns"] = False
 
         if self.cfg.dataloader_pin_memory is not None:
-            training_args_kwargs[
-                "dataloader_pin_memory"
-            ] = self.cfg.dataloader_pin_memory
+            training_args_kwargs["dataloader_pin_memory"] = (
+                self.cfg.dataloader_pin_memory
+            )
         if self.cfg.dataloader_num_workers is not None:
-            training_args_kwargs[
-                "dataloader_num_workers"
-            ] = self.cfg.dataloader_num_workers
+            training_args_kwargs["dataloader_num_workers"] = (
+                self.cfg.dataloader_num_workers
+            )
         if self.cfg.dataloader_prefetch_factor is not None:
-            training_args_kwargs[
-                "dataloader_prefetch_factor"
-            ] = self.cfg.dataloader_prefetch_factor
+            training_args_kwargs["dataloader_prefetch_factor"] = (
+                self.cfg.dataloader_prefetch_factor
+            )
         if self.cfg.gradient_checkpointing:
-            training_args_kwargs[
-                "gradient_checkpointing"
-            ] = self.cfg.gradient_checkpointing
+            training_args_kwargs["gradient_checkpointing"] = (
+                self.cfg.gradient_checkpointing
+            )
             if self.cfg.gradient_checkpointing_kwargs is not None:
-                training_args_kwargs[
-                    "gradient_checkpointing_kwargs"
-                ] = self.cfg.gradient_checkpointing_kwargs
+                training_args_kwargs["gradient_checkpointing_kwargs"] = (
+                    self.cfg.gradient_checkpointing_kwargs
+                )
             else:
                 training_args_kwargs["gradient_checkpointing_kwargs"] = {
                     "use_reentrant": False
@@ -1071,9 +1071,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.dpo_use_weighting is not None:
                 training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
             if self.cfg.dpo_use_logits_to_keep is not None:
-                training_args_kwargs[
-                    "use_logits_to_keep"
-                ] = self.cfg.dpo_use_logits_to_keep
+                training_args_kwargs["use_logits_to_keep"] = (
+                    self.cfg.dpo_use_logits_to_keep
+                )
 
         for blocklist_key in blocklist_args_kwargs:
             if blocklist_key in training_args_kwargs:
@@ -1108,9 +1108,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.adapter and self.peft_config:
             dpo_trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
-            dpo_trainer_kwargs[
-                "precompute_ref_log_probs"
-            ] = self.cfg.precompute_ref_log_probs
+            dpo_trainer_kwargs["precompute_ref_log_probs"] = (
+                self.cfg.precompute_ref_log_probs
+            )
         if self.cfg.rl == "grpo":
             trainer_cls = GRPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model]

src/axolotl/core/trainers/base.py

@@ -462,9 +462,9 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 "pin_memory": self.args.dataloader_pin_memory,
             }
             if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
+                dataloader_params["prefetch_factor"] = (
+                    self.args.dataloader_prefetch_factor
+                )
 
             sampler = self._get_train_sampler()
             if isinstance(sampler, BatchSampler):
@@ -509,9 +509,9 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
                 "pin_memory": self.args.dataloader_pin_memory,
             }
             if self.args.dataloader_prefetch_factor:
-                dataloader_params[
-                    "prefetch_factor"
-                ] = self.args.dataloader_prefetch_factor
+                dataloader_params["prefetch_factor"] = (
+                    self.args.dataloader_prefetch_factor
+                )
 
             if isinstance(eval_sampler, BatchSampler):
                 dataloader_params["batch_sampler"] = eval_sampler

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

@@ -1,6 +1,7 @@
 """
 DPO Specific Strategy for training
 """
+
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
 
 

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

@@ -1,6 +1,7 @@
 """
 Axolotl specific DPO args
 """
+
 from dataclasses import dataclass
 
 from trl import DPOConfig

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

@@ -1,6 +1,7 @@
 """
 DPO trainer for axolotl
 """
+
 import gc
 from functools import wraps
 from typing import Any, Dict, Union

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

@@ -45,9 +45,9 @@ class GRPOStrategy:
             )
 
             if trl.vllm_gpu_memory_utilization:
-                grpo_args_kwargs[
-                    "vllm_gpu_memory_utilization"
-                ] = trl.vllm_gpu_memory_utilization
+                grpo_args_kwargs["vllm_gpu_memory_utilization"] = (
+                    trl.vllm_gpu_memory_utilization
+                )
 
             if trl.vllm_max_model_len:
                 grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
@@ -86,9 +86,9 @@ class GRPOStrategy:
     def set_trainer_kwargs(cls, cfg):
         trainer_kwargs = {}
         if cfg.trl and cfg.trl.reward_processing_classes:
-            trainer_kwargs[
-                "reward_processing_classes"
-            ] = cfg.trl.reward_processing_classes
+            trainer_kwargs["reward_processing_classes"] = (
+                cfg.trl.reward_processing_classes
+            )
         return trainer_kwargs
 
     @classmethod

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

@@ -1,6 +1,7 @@
 """
 Axolotl Specific Training Args
 """
+
 from dataclasses import dataclass
 
 from trl import GRPOConfig

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

@@ -1,6 +1,7 @@
 """
 Axolotl GRPO trainer
 """
+
 from accelerate.utils import is_peft_model
 from accelerate.utils.other import is_compiled_module
 from transformers import PreTrainedModel

src/axolotl/core/trainers/trl.py

@@ -1,6 +1,7 @@
 """
 module for TRL PPO training
 """
+
 import torch
 from tqdm import tqdm
 from trl import PPOTrainer

src/axolotl/core/training_args.py

@@ -1,6 +1,7 @@
 """
 extra axolotl specific training args
 """
+
 from dataclasses import dataclass, field
 from typing import Optional
 

src/axolotl/integrations/grokfast/__init__.py

@@ -1,6 +1,7 @@
 """
 Grokfast plugin for Axolotl
 """
+
 import logging
 
 from transformers.trainer_callback import TrainerCallback

src/axolotl/integrations/grokfast/args.py

@@ -1,6 +1,7 @@
 """
 config args for grokfast plugin
 """
+
 from typing import Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/kd/args.py

@@ -26,12 +26,12 @@ class KDArgs(BaseModel):
     """
 
     kd_trainer: Optional[bool] = None  # whether to use KD trainer
-    kd_ce_alpha: Optional[
-        float
-    ] = None  # loss coefficient for cross-entropy loss during KD
+    kd_ce_alpha: Optional[float] = (
+        None  # loss coefficient for cross-entropy loss during KD
+    )
     kd_alpha: Optional[float] = None  # loss coefficient for KD loss
     kd_temperature: Optional[float] = None  # temperature for sampling during KD
     kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling
-    kd_top_k_before_softmax: Optional[
-        bool
-    ] = None  # whether to sample top k before softmax during KD
+    kd_top_k_before_softmax: Optional[bool] = (
+        None  # whether to sample top k before softmax during KD
+    )

src/axolotl/integrations/liger/__init__.py

@@ -55,9 +55,9 @@ class LigerPlugin(BasePlugin):
             if "cross_entropy" in liger_fn_sig.parameters:
                 kwargs["cross_entropy"] = cfg.liger_cross_entropy
             if "fused_linear_cross_entropy" in liger_fn_sig.parameters:
-                kwargs[
-                    "fused_linear_cross_entropy"
-                ] = cfg.liger_fused_linear_cross_entropy
+                kwargs["fused_linear_cross_entropy"] = (
+                    cfg.liger_fused_linear_cross_entropy
+                )
             if "rms_norm" in liger_fn_sig.parameters:
                 kwargs["rms_norm"] = cfg.liger_rms_norm
             if "layer_norm" in liger_fn_sig.parameters:

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

@@ -1,6 +1,7 @@
 """
 DeepseekV2 model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code
 
 from typing import List, Optional, Tuple, Union

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

@@ -1,6 +1,7 @@
 """
 Jamba model with LigerFusedLinearCrossEntropyLoss
 """
+
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple, Union

src/axolotl/integrations/lm_eval/__init__.py

@@ -1,6 +1,7 @@
 """
 Module for the Plugin for LM Eval Harness
 """
+
 import subprocess  # nosec
 
 from axolotl.integrations.base import BasePlugin

src/axolotl/integrations/lm_eval/args.py

@@ -1,6 +1,7 @@
 """
 Module for handling lm eval harness input arguments.
 """
+
 from typing import List, Optional
 
 from pydantic import BaseModel

src/axolotl/integrations/lm_eval/cli.py

@@ -1,6 +1,7 @@
 """
 axolotl CLI for running lm_eval tasks
 """
+
 import subprocess  # nosec
 from collections import defaultdict
 from datetime import datetime

src/axolotl/kernels/geglu.py

@@ -5,6 +5,7 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 # pylint: disable=invalid-name,unnecessary-lambda-assignment,duplicate-code
 
 import torch

src/axolotl/kernels/lora.py

@@ -6,6 +6,7 @@ See "LoRA: Low-Rank Adaptation of Large Language Models"
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 # pylint: disable=invalid-name
 
 from typing import Callable

src/axolotl/kernels/quantize.py

@@ -1,4 +1,5 @@
 """Dequantization utilities for `bitsandbytes` integration."""
+
 # pylint: disable=invalid-name,global-statement
 
 import ctypes

src/axolotl/kernels/swiglu.py

@@ -5,6 +5,7 @@ See "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202).
 
 Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.
 """
+
 import torch
 import triton
 import triton.language as tl

src/axolotl/models/mamba/configuration_mamba.py

@@ -1,6 +1,7 @@
 """
 HF Transformers MambaConfig
 """
+
 from transformers import PretrainedConfig
 
 

src/axolotl/monkeypatch/attention/mllama.py

@@ -1,6 +1,7 @@
 """
 Monkeypatch for Vision Llama for FA2 support
 """
+
 # pylint: disable=duplicate-code
 
 from typing import Optional, Tuple
@@ -220,10 +221,10 @@ def patch_mllama():
         True
     )
     MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
-    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
-        "flash_attention_2"
-    ] = MllamaTextCrossFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES["flash_attention_2"] = (
+        MllamaTextCrossFlashAttention2
+    )
     # fallback to SDPA
-    MLLAMA_VISION_ATTENTION_CLASSES[
-        "flash_attention_2"
-    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]
+    MLLAMA_VISION_ATTENTION_CLASSES["flash_attention_2"] = (
+        MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]
+    )

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -1,4 +1,5 @@
 """monkey patches for the dataset fetcher to handle batches of packed indexes"""
+
 # pylint: disable=protected-access
 
 import torch

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -12,7 +12,9 @@ import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
 from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.llama.modeling_llama import LlamaAttention
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+)
 from transformers.models.llama.modeling_llama import (
     LlamaDecoderLayer as OriginalLlamaDecoderLayer,
 )
@@ -490,9 +492,11 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
+            query_padding_mask=(
+                attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None
+            ),
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -531,9 +535,11 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
+                query_padding_mask=(
+                    attention_mask[:, -query_states.size(1) :]
+                    if attention_mask is not None
+                    else None
+                ),
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -1,6 +1,7 @@
 """
 expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
 """
+
 from typing import Optional
 
 import torch

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -1,4 +1,5 @@
 """Flash attention monkey patch for mistral model"""
+
 # pylint: disable=duplicate-code
 
 import logging
@@ -21,7 +22,10 @@ from transformers.models.mistral.modeling_mistral import (
 from transformers.models.mistral.modeling_mistral import (
     MistralDecoderLayer as OriginalMistralDecoderLayer,
 )
-from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
+from transformers.models.mistral.modeling_mistral import (
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 
@@ -243,9 +247,11 @@ def flashattn_forward(
             # We have disabled _prepare_decoder_attention_mask in LlamaModel
             # the attention_mask should be the same as the key_padding_mask
             key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
+            query_padding_mask=(
+                attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None
+            ),
         )
         output_unpad = flash_attn_varlen_qkvpacked_func(
             qkv_unpad,
@@ -286,9 +292,11 @@ def flashattn_forward(
                 value_states,
                 kvpacked=True,
                 key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
+                query_padding_mask=(
+                    attention_mask[:, -query_states.size(1) :]
+                    if attention_mask is not None
+                    else None
+                ),
             )
             if q_unpad.dtype != kv_unpad.dtype:
                 kv_unpad = kv_unpad.to(q_unpad.dtype)

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -1,6 +1,7 @@
 """
 Patches to support multipack for mixtral
 """
+
 import torch
 
 

src/axolotl/monkeypatch/relora.py

@@ -1,4 +1,5 @@
 """Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune."""
+
 import glob
 import json
 import logging
@@ -411,7 +412,10 @@ def merge_and_save(
         if shard_path.endswith(".safetensors"):
             in_tensors = st.load_file(str(Path(model_src) / shard_path))
         else:
-            in_tensors = torch.load(Path(model_src) / shard_path)
+            in_tensors = torch.load(
+                Path(model_src) / shard_path,
+                weights_only=True,  # to prevent arbitrary code execution
+            )
             if "state_dict" in in_tensors:
                 in_tensors = in_tensors["state_dict"]
 

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -17,7 +17,7 @@
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
 # pylint: disable=duplicate-code
-""" PyTorch StableLM Epoch model. """
+"""PyTorch StableLM Epoch model."""
 import importlib
 import math
 from typing import Optional, Tuple, Union

src/axolotl/monkeypatch/trainer_fsdp_optim.py

@@ -1,6 +1,7 @@
 """
 fix for FSDP optimizer save in trainer w 4.47.0
 """
+
 import inspect
 import logging
 

src/axolotl/monkeypatch/utils.py

@@ -1,6 +1,7 @@
 """
 Shared utils for the monkeypatches
 """
+
 import re
 from typing import Optional, Tuple
 

src/axolotl/monkeypatch/xformers_/__init__.py

@@ -1,6 +1,7 @@
 """
 Fused MLP layer for incrementally improved training efficiency
 """
+
 import torch
 from transformers.models.llama.modeling_llama import LlamaMLP
 from xformers.ops import SwiGLU

src/axolotl/prompt_strategies/alpaca_w_system.py

@@ -1,6 +1,7 @@
 """
 Prompt strategies loader for alpaca instruction datasets with system prompts
 """
+
 from typing import Generator, Tuple, Union
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy

src/axolotl/prompt_strategies/completion.py

@@ -1,6 +1,7 @@
 """
 Basic completion text
 """
+
 from collections import defaultdict
 from typing import Any, Dict, Generator, Optional, Tuple
 

src/axolotl/prompt_strategies/context_qa.py

@@ -1,4 +1,5 @@
 """Module containing the classes for Context QA Prompt Tokenization Strategies"""
+
 from typing import Tuple
 
 from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy

src/axolotl/prompt_strategies/dpo/__init__.py

@@ -1,6 +1,7 @@
 """
 module for DPO style dataset transform strategies
 """
+
 from functools import partial
 
 from ..base import load as load_base

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -33,9 +33,9 @@ def default(
                 f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample[prompt_key]}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["chosen"] = f"{sample[chosen_key]}<|im_end|>"
         sample["rejected"] = f"{sample[rejected_key]}<|im_end|>"
         return sample
@@ -52,9 +52,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample[
-            "prompt"
-        ] = f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["prompt"] = (
+            f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+        )
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
         return sample
@@ -78,9 +78,9 @@ def icr(
                 f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['input']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -100,9 +100,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -120,9 +120,9 @@ def prompt_pairs(
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected']}<|im_end|>"
         return sample
@@ -142,9 +142,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
         return sample

src/axolotl/prompt_strategies/dpo/llama3.py

@@ -34,9 +34,9 @@ def default(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample[prompt_key]}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["chosen"] = f"{sample[chosen_key]}<|eot_id|>"
         sample["rejected"] = f"{sample[rejected_key]}<|eot_id|>"
         return sample
@@ -53,9 +53,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample[
-            "prompt"
-        ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['chosen'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["prompt"] = (
+            f"<|start_header_id|>user<|end_header_id|>\n\n{sample['chosen'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        )
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|eot_id|>"
         return sample
@@ -79,9 +79,9 @@ def icr(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -101,9 +101,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -121,9 +121,9 @@ def prompt_pairs(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["chosen"] = f"{sample['chosen']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected']}<|eot_id|>"
         return sample
@@ -143,9 +143,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["chosen"] = f"{sample['chosen'][1]['content']}<|eot_id|>"
         sample["rejected"] = f"{sample['rejected'][1]['content']}<|eot_id|>"
         return sample

src/axolotl/prompt_strategies/input_output.py

@@ -1,4 +1,5 @@
 """Module for plain input/output prompt pairs"""
+
 from typing import Generator, Tuple
 
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy

src/axolotl/prompt_strategies/jinja_template_analyzer.py

@@ -1,4 +1,5 @@
 """Module for inspect jinja templates for the variables they use"""
+
 from typing import Dict, Optional, Set, TypedDict, Union
 
 from jinja2 import Environment, meta, nodes

src/axolotl/prompt_strategies/kto/chatml.py

@@ -1,6 +1,7 @@
 """
 KTO strategies for chatml
 """
+
 # pylint: disable=duplicate-code
 
 
@@ -15,9 +16,9 @@ def argilla(
                 f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -33,9 +34,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample[
-            "prompt"
-        ] = f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["prompt"] = (
+            f"<|im_start|>user\n{sample['chosen'][0]['content']}<|im_end|>\n<|im_start|>assistant\n"
+        )
         sample["completion"] = f"{sample['completion'][1]['content']}<|im_end|>"
         return sample
 
@@ -55,9 +56,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -74,9 +75,9 @@ def prompt_pairs(
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 
@@ -96,9 +97,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            sample["prompt"] = (
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
         sample["completion"] = f"{sample['completion']}<|im_end|>"
         return sample
 

src/axolotl/prompt_strategies/kto/llama3.py

@@ -1,6 +1,7 @@
 """
 KTO strategies for llama-3 chat template
 """
+
 # pylint: disable=duplicate-code
 
 
@@ -15,9 +16,9 @@ def argilla(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['instruction']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -33,9 +34,9 @@ def argilla_chat(
     """
 
     def transform_fn(sample):
-        sample[
-            "prompt"
-        ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['completion'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        sample["prompt"] = (
+            f"<|start_header_id|>user<|end_header_id|>\n\n{sample['completion'][0]['content']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+        )
         sample["completion"] = f"{sample['completion'][1]['content']}<|eot_id|>"
         return sample
 
@@ -55,9 +56,9 @@ def intel(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['question']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -74,9 +75,9 @@ def prompt_pairs(
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 
@@ -96,9 +97,9 @@ def ultra(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-arg
                 f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
             )
         else:
-            sample[
-                "prompt"
-            ] = f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            sample["prompt"] = (
+                f"<|start_header_id|>user<|end_header_id|>\n\n{sample['prompt']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
         sample["completion"] = f"{sample['completion']}<|eot_id|>"
         return sample
 

src/axolotl/prompt_strategies/kto/user_defined.py

@@ -1,6 +1,7 @@
 """
 User-defined KTO strategies
 """
+
 # pylint: disable=duplicate-code
 
 

src/axolotl/prompt_strategies/messages/chat.py

@@ -1,6 +1,7 @@
 """
 Chat dataset wrapping strategy for new internal messages representations
 """
+
 from typing import Any, Callable, Dict, Optional
 
 from axolotl.core.datasets.chat import TokenizedChatDataset

src/axolotl/prompt_strategies/orcamini.py

@@ -9,6 +9,7 @@ this one specifies the system prompt with "### System:".
 
 Not suited/tested for multiple-turn conversations without further adjustments.
 """
+
 from typing import Generator, Union
 
 from axolotl.prompt_strategies.alpaca_w_system import OpenOrcaPromptTokenizingStrategy

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -1,4 +1,5 @@
 """chatml prompt tokenization strategy for ORPO"""
+
 from typing import Any, Dict, Generator, List, Optional, Tuple
 
 from pydantic import BaseModel

src/axolotl/prompt_strategies/pretrain.py

@@ -1,4 +1,5 @@
 """pretraining prompt strategies"""
+
 from typing import Generator
 
 from transformers import BatchEncoding

src/axolotl/train.py

@@ -406,9 +406,7 @@ def handle_untrained_tokens_fix(
         )
 
 
-def setup_model_and_trainer(
-    cfg: DictDefault, dataset_meta: TrainDatasetMeta
-) -> tuple[
+def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[
     HFRLTrainerBuilder | HFCausalTrainerBuilder,
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,

src/axolotl/utils/__init__.py

@@ -40,6 +40,6 @@ def set_pytorch_cuda_alloc_conf():
     torch_major, torch_minor = int(torch_version[0]), int(torch_version[1])
     if torch_major == 2 and torch_minor >= 2:
         if os.getenv("PYTORCH_CUDA_ALLOC_CONF") is None:
-            os.environ[
-                "PYTORCH_CUDA_ALLOC_CONF"
-            ] = "expandable_segments:True,roundup_power2_divisions:16"
+            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
+                "expandable_segments:True,roundup_power2_divisions:16"
+            )

src/axolotl/utils/bench.py

@@ -1,4 +1,5 @@
 """Benchmarking and measurement utilities"""
+
 import functools
 
 import torch

src/axolotl/utils/callbacks/__init__.py

@@ -343,9 +343,9 @@ def bench_eval_callback_factory(trainer, tokenizer):
                     bench_refs.extend(combined_bench_names[bench_name]["refs"])
                     bench_preds.extend(combined_bench_names[bench_name]["preds"])
                     if not pd.isna(bench_score):
-                        results[
-                            f"{bench_split}_bench_accuracy_{bench_name}"
-                        ] = bench_score
+                        results[f"{bench_split}_bench_accuracy_{bench_name}"] = (
+                            bench_score
+                        )
                         bench_scores.append(bench_score)
                     else:
                         results[f"{bench_split}_bench_accuracy_{bench_name}"] = 0.0

src/axolotl/utils/callbacks/mlflow_.py

@@ -1,4 +1,5 @@
 """MLFlow module for trainer callbacks"""
+
 import logging
 from shutil import copyfile
 from tempfile import NamedTemporaryFile

src/axolotl/utils/callbacks/perplexity.py

@@ -1,4 +1,5 @@
 """callback to calculate perplexity as an evaluation metric."""
+
 from typing import Dict, List, Optional
 
 import torch

src/axolotl/utils/callbacks/profiler.py

@@ -1,6 +1,7 @@
 """
 HF Trainer callback for creating pytorch profiling snapshots
 """
+
 from pathlib import Path
 from pickle import dump  # nosec B403
 

src/axolotl/utils/chat_templates.py

@@ -2,6 +2,7 @@
 This module provides functionality for selecting chat templates based on user choices.
 These templates are used for formatting messages in a conversation.
 """
+
 import logging
 from typing import TYPE_CHECKING, Any, Dict, Optional
 

src/axolotl/utils/collators/__init__.py

@@ -1,6 +1,7 @@
 """
 shared axolotl collators for multipack, mamba, multimodal
 """
+
 from .batching import (  # noqa: F401
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,

src/axolotl/utils/collators/core.py

@@ -1,4 +1,5 @@
 """
 basic shared collator constants
 """
+
 IGNORE_INDEX = -100

src/axolotl/utils/collators/mamba.py

@@ -1,6 +1,7 @@
 """
 collators for Mamba
 """
+
 from dataclasses import dataclass
 from typing import Dict, Sequence
 

src/axolotl/utils/config/__init__.py

@@ -18,7 +18,11 @@ from axolotl.utils.config.models.input.v0_4_1 import (
 from axolotl.utils.config.models.input.v0_4_1 import (
     AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import DPODataset, KTODataset, SFTDataset
+from axolotl.utils.config.models.input.v0_4_1 import (
+    DPODataset,
+    KTODataset,
+    SFTDataset,
+)
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
 

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -200,12 +200,12 @@ class SFTDataset(BaseModel):
     field_human: Optional[str] = None
     field_model: Optional[str] = None
     field_messages: Optional[str] = None
-    message_field_role: Optional[
-        str
-    ] = None  # deprecated, use message_property_mappings
-    message_field_content: Optional[
-        str
-    ] = None  # deprecated, use message_property_mappings
+    message_field_role: Optional[str] = (
+        None  # deprecated, use message_property_mappings
+    )
+    message_field_content: Optional[str] = (
+        None  # deprecated, use message_property_mappings
+    )
     message_property_mappings: Optional[Dict[str, str]] = None
     message_field_training: Optional[str] = None
     message_field_training_detail: Optional[str] = None
@@ -505,9 +505,9 @@ class HyperparametersConfig(BaseModel):
     embedding_lr: Optional[float] = None
     embedding_lr_scale: Optional[float] = None
     weight_decay: Optional[float] = 0.0
-    optimizer: Optional[
-        Union[OptimizerNames, CustomSupportedOptimizers]
-    ] = OptimizerNames.ADAMW_TORCH_FUSED
+    optimizer: Optional[Union[OptimizerNames, CustomSupportedOptimizers]] = (
+        OptimizerNames.ADAMW_TORCH_FUSED
+    )
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None,
         json_schema_extra={"description": "Optional arguments to supply to optimizer."},
@@ -699,9 +699,9 @@ class AxolotlInputConfig(
     reward_model: Optional[bool] = None
     process_reward_model: Optional[bool] = None
     num_labels: Optional[int] = None
-    dpo_use_weighting: Optional[
-        bool
-    ] = None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
+    dpo_use_weighting: Optional[bool] = (
+        None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
+    )
     dpo_use_logits_to_keep: Optional[bool] = None
 
     datasets: Optional[
@@ -780,9 +780,9 @@ class AxolotlInputConfig(
 
     # torch_dtype: Optional[torch.dtype]
 
-    gradient_checkpointing: Optional[
-        Union[Literal["unsloth", "offload"], bool]
-    ] = Field(default=False)
+    gradient_checkpointing: Optional[Union[Literal["unsloth", "offload"], bool]] = (
+        Field(default=False)
+    )
     gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
 
     unfrozen_parameters: Optional[List[str]] = None
@@ -894,9 +894,9 @@ class AxolotlInputConfig(
     kto_undesirable_weight: Optional[float] = None
     rl_beta: Optional[float] = None
 
-    max_memory: Optional[
-        Dict[Union[int, Literal["cpu", "disk"]], Union[int, str]]
-    ] = None
+    max_memory: Optional[Dict[Union[int, Literal["cpu", "disk"]], Union[int, str]]] = (
+        None
+    )
     gpu_memory_limit: Optional[Union[int, str]] = None
     low_cpu_mem_usage: Optional[bool] = None
 

src/axolotl/utils/config/models/internals/__init__.py

@@ -1,4 +1,5 @@
 """module for gpu capabilities"""
+
 from typing import Optional
 
 from pydantic import BaseModel, Field

src/axolotl/utils/data/__init__.py

@@ -1,6 +1,7 @@
 """
 Data processing modules
 """
+
 from axolotl.utils.data.pretraining import (  # noqa: F401
     encode_pretraining,
     wrap_pretraining_dataset,

src/axolotl/utils/distributed.py

@@ -1,6 +1,7 @@
 """
 utility helpers for distributed checks
 """
+
 import os
 import pickle  # nosec
 from contextlib import contextmanager

src/axolotl/utils/environment.py

@@ -1,10 +1,13 @@
 """
 utils to get GPU info for the current environment
 """
+
 from accelerate.utils.environment import (
     check_cuda_p2p_ib_support as accelerate_check_cuda_p2p_ib_support,
 )
-from accelerate.utils.environment import get_gpu_info
+from accelerate.utils.environment import (
+    get_gpu_info,
+)
 
 
 def check_cuda_p2p_ib_support():

src/axolotl/utils/freeze.py

@@ -1,6 +1,7 @@
 """
 module to freeze/unfreeze parameters by name
 """
+
 import logging
 import re
 from typing import Callable, List, Tuple, Union

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -1,4 +1,5 @@
 """custom checkpointing utils"""
+
 from axolotl.utils.gradient_checkpointing.unsloth import (
     Unsloth_Offloaded_Gradient_Checkpointer,
 )

src/axolotl/utils/model_shard_quant.py

@@ -1,6 +1,7 @@
 """
 module to handle loading model on cpu/meta device for FSDP
 """
+
 import os
 import time
 from typing import List, Optional, Type, Union
@@ -45,13 +46,13 @@ def _replace_linear(
 
         if isinstance(module, torch.nn.Linear) and name not in skip_modules:
             if issubclass(linear_replacement, Linear4bit):
-                model._modules[  # pylint: disable=protected-access
-                    name
-                ] = linear_replacement(
-                    module.in_features,
-                    module.out_features,
-                    module.bias is not None,
-                    **kwargs,
+                model._modules[name] = (  # pylint: disable=protected-access
+                    linear_replacement(
+                        module.in_features,
+                        module.out_features,
+                        module.bias is not None,
+                        **kwargs,
+                    )
                 )
             else:
                 raise ValueError(

src/axolotl/utils/models.py

@@ -741,9 +741,9 @@ class ModelLoader:
                 )
             else:
                 if self.cfg.gptq_disable_exllama is not None:
-                    self.model_config.quantization_config[
-                        "disable_exllama"
-                    ] = self.cfg.gptq_disable_exllama
+                    self.model_config.quantization_config["disable_exllama"] = (
+                        self.cfg.gptq_disable_exllama
+                    )
                 self.model_kwargs["quantization_config"] = GPTQConfig(
                     **self.model_config.quantization_config
                 )

src/axolotl/utils/optimizers/adopt.py

@@ -4,6 +4,7 @@ Copied from https://github.com/iShohei220/adopt
 ADOPT: Modified Adam Can Converge with Any β2 with the Optimal Rate (2024)
 Taniguchi, Shohei and Harada, Keno and Minegishi, Gouki and Oshima, Yuta and Jeong, Seong Cheol and Nagahara, Go and Iiyama, Tomoshi and Suzuki, Masahiro and Iwasawa, Yusuke and Matsuo, Yutaka
 """
+
 # mypy: ignore-errors
 # pylint: skip-file
 # flake8: noqa

src/axolotl/utils/samplers/__init__.py

@@ -1,5 +1,6 @@
 """
 axolotl samplers module
 """
+
 from .multipack import MultipackBatchSampler  # noqa: F401
 from .utils import get_dataset_lengths  # noqa: F401

src/axolotl/utils/samplers/utils.py

@@ -1,6 +1,7 @@
 """
 helper util to calculate dataset lengths
 """
+
 import numpy as np
 
 

src/axolotl/utils/schedulers.py

@@ -1,4 +1,5 @@
 """Module for custom LRScheduler class"""
+
 import math
 from functools import partial
 

src/axolotl/utils/trainer.py

@@ -538,9 +538,9 @@ def setup_fsdp_envs(cfg):
     if cfg.fsdp_config.fsdp_auto_wrap_policy:
         os.environ["FSDP_AUTO_WRAP_POLICY"] = cfg.fsdp_config.fsdp_auto_wrap_policy
     if cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap:
-        os.environ[
-            "FSDP_TRANSFORMER_CLS_TO_WRAP"
-        ] = cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
+        os.environ["FSDP_TRANSFORMER_CLS_TO_WRAP"] = (
+            cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
+        )
 
 
 def prepare_optim_env(cfg):

src/setuptools_axolotl_dynamic_dependencies.py

@@ -1,6 +1,7 @@
 """
 dynamic requirements for axolotl
 """
+
 import platform
 import re
 from importlib.metadata import PackageNotFoundError, version

tests/cli/test_cli_merge_sharded_fsdp_weights.py

@@ -1,4 +1,5 @@
 """pytest tests for axolotl CLI merge_sharded_fsdp_weights command."""
+
 # pylint: disable=duplicate-code
 
 from unittest.mock import patch

tests/cli/test_cli_sweeps.py

@@ -1,6 +1,7 @@
 """
 unit tests for generating sweep configurations
 """
+
 from axolotl.cli.main import generate_sweep_configs
 
 

tests/cli/test_utils.py

@@ -1,4 +1,5 @@
 """pytest tests for axolotl CLI utils."""
+
 # pylint: disable=redefined-outer-name
 
 import json

tests/conftest.py

@@ -1,6 +1,7 @@
 """
 shared pytest fixtures
 """
+
 import functools
 import importlib
 import shutil

tests/core/chat/test_messages.py

@@ -1,6 +1,7 @@
 """
 Tests for the chat messages module
 """
+
 import unittest
 
 import pytest