[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,12 +1,12 @@
 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
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.0
+    rev: v0.16.0
     hooks:
       - id: ruff
         args: [--fix]

data/test_data_loading.py

@@ -1,6 +1,7 @@
-import pytest
 import json
 
+import pytest
+
 
 @pytest.fixture
 def load_data():

dataset.py

@@ -1,9 +1,10 @@
 import json
-from typing import Any, Dict, List
+from typing import Any
 
 import torch
 from loguru import logger
 from torch.utils.data import Dataset
+
 from utils.tool_utils import function_formatter
 
 
@@ -18,10 +19,10 @@ def __init__(self, file, tokenizer, max_seq_length, template):
         self.observation_format = template["observation_format"]
 
         self.max_seq_length = max_seq_length
-        logger.info("Loading data: {}".format(file))
+        logger.info(f"Loading data: {file}")
         with open(file, "r", encoding="utf8") as f:
             data_list = f.readlines()
-        logger.info("There are {} data in dataset".format(len(data_list)))
+        logger.info(f"There are {len(data_list)} data in dataset")
         self.data_list = data_list
 
     def __len__(self):
@@ -93,13 +94,13 @@ def __getitem__(self, index):
         return inputs
 
 
-class SFTDataCollator(object):
+class SFTDataCollator:
     def __init__(self, tokenizer, max_seq_length):
         self.tokenizer = tokenizer
         self.max_seq_length = max_seq_length
         self.pad_token_id = tokenizer.pad_token_id
 
-    def __call__(self, batch: List[Dict[str, Any]]) -> Dict[str, Any]:
+    def __call__(self, batch: list[dict[str, Any]]) -> dict[str, Any]:
         # Find the maximum length in the batch
         lengths = [len(x["input_ids"]) for x in batch if x["input_ids"] is not None]
         # Take the maximum length in the batch, if it exceeds max_seq_length, take max_seq_length

demo.py

@@ -2,10 +2,11 @@
 from dataclasses import dataclass
 
 import torch
+from datasets import Dataset
 from peft import LoraConfig
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from trl import SFTTrainer, SFTConfig
-from datasets import Dataset
+from trl import SFTConfig, SFTTrainer
+
 from dataset import SFTDataCollator, SFTDataset
 from utils.constants import model2template
 

full_automation.py

@@ -3,8 +3,8 @@
 
 import requests
 import yaml
-from loguru import logger
 from huggingface_hub import HfApi
+from loguru import logger
 
 from demo import LoraTrainingArguments, train_lora
 from utils.constants import model2base_model, model2size
@@ -36,8 +36,7 @@
     # download in chunks
     response = requests.get(data_url, stream=True)
     with open("data/demo_data.jsonl", "wb") as f:
-        for chunk in response.iter_content(chunk_size=8192):
-            f.write(chunk)
+        f.writelines(response.iter_content(chunk_size=8192))
 
     # train all feasible models and merge
     for model_id in all_training_args.keys():

utils/tool_utils.py

@@ -1,5 +1,5 @@
-from typing import Dict, Any, List, Tuple
 import json
+from typing import Any
 
 DEFAULT_TOOL_PROMPT = (
     "You have access to the following tools:\n{tool_text}"
@@ -14,7 +14,7 @@
 DEFAULT_FUNCTION_SLOTS = "Action: {name}\nAction Input: {arguments}\n"
 
 
-def tool_formater(tools: List[Dict[str, Any]]) -> str:
+def tool_formater(tools: list[dict[str, Any]]) -> str:
     tool_text = ""
     tool_names = []
     for tool in tools:
@@ -52,7 +52,7 @@ def tool_formater(tools: List[Dict[str, Any]]) -> str:
 
 
 def function_formatter(tool_calls, function_slots=DEFAULT_FUNCTION_SLOTS) -> str:
-    functions: List[Tuple[str, str]] = []
+    functions: list[tuple[str, str]] = []
     if not isinstance(tool_calls, list):
         tool_calls = [tool_calls]  # parrallel function calls