update readme

.github/workflows/tests.yml

@@ -22,7 +22,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install black ruff
+          python -m pip install ruff
 
       - name: Check quality
         run: |

Makefile

@@ -3,9 +3,9 @@
 check_dirs := src tests
 
 quality:
-	black --check $(check_dirs)
 	ruff $(check_dirs)
+	ruff format --check $(check_dirs)
 
 style:
-	black $(check_dirs)
 	ruff $(check_dirs) --fix
+	ruff format $(check_dirs)

README.md

@@ -398,6 +398,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
+> [!TIP]
+> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` to infer the fine-tuned model.
+
 > [!WARNING]
 > Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training.
 
@@ -426,6 +429,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
+> [!TIP]
+> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` to infer the fine-tuned model.
+
 ### Distributed Training
 
 #### Use Huggingface Accelerate

README_zh.md

@@ -398,6 +398,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
+> [!TIP]
+> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` 来进行微调模型的推理。
+
 > [!WARNING]
 > 如果使用 fp16 精度进行 LLaMA-2 模型的 PPO 训练，请使用 `--per_device_train_batch_size=1`。
 
@@ -426,6 +429,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
+> [!TIP]
+> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` 来进行微调模型的推理。
+
 ### 多 GPU 分布式训练
 
 #### 使用 Huggingface Accelerate

pyproject.toml

@@ -2,11 +2,8 @@
 requires = ["setuptools>=61.0"]
 build-backend = "setuptools.build_meta"
 
-[tool.black]
-line-length = 119
-target-version = ["py38"]
-
 [tool.ruff]
+target-version = "py38"
 line-length = 119
 indent-width = 4
 
@@ -17,17 +14,7 @@ select = ["C", "E", "F", "I", "W"]
 [tool.ruff.lint.isort]
 lines-after-imports = 2
 known-first-party = ["llmtuner"]
-
+known-third-party = [
-[tool.ruff.format]
-quote-style = "double"
-indent-style = "space"
-skip-magic-trailing-comma = false
-line-ending = "auto"
-
-[isort]
-default_section = "FIRSTPARTY"
-known_first_party = "llmtuner"
-known_third_party = [
     "accelerate",
     "datasets",
     "gradio",
@@ -37,10 +24,9 @@ known_third_party = [
     "transformers",
     "trl"
 ]
-line_length = 119
+
-lines_after_imports = 2
+[tool.ruff.format]
-multi_line_output = 3
+quote-style = "double"
-include_trailing_comma = true
+indent-style = "space"
-force_grid_wrap = 0
+skip-magic-trailing-comma = false
-use_parentheses = true
+line-ending = "auto"
-ensure_newline_before_comments = true

src/llmtuner/data/formatter.py

@@ -75,7 +75,8 @@ class Formatter(ABC):
     tool_format: Literal["default"] = "default"
 
     @abstractmethod
-    def apply(self, **kwargs) -> SLOTS: ...
+    def apply(self, **kwargs) -> SLOTS:
+        ...
 
     def extract(self, content: str) -> Union[str, Tuple[str, str]]:
         raise NotImplementedError

src/llmtuner/data/template.py

@@ -308,6 +308,15 @@ _register_template(
 )
 
 
+_register_template(
+    name="atom",
+    format_user=StringFormatter(
+        slots=[{"bos_token"}, "Human: {{content}}\n", {"eos_token"}, {"bos_token"}, "Assistant:"]
+    ),
+    format_assistant=StringFormatter(slots=["{{content}}\n", {"eos_token"}]),
+)
+
+
 _register_template(
     name="baichuan",
     format_user=StringFormatter(slots=[{"token": "<reserved_102>"}, "{{content}}", {"token": "<reserved_103>"}]),
@@ -515,7 +524,7 @@ _register_template(
 _register_template(
     name="openchat",
     format_user=StringFormatter(slots=["GPT4 Correct User: {{content}}", {"eos_token"}, "GPT4 Correct Assistant:"]),
-    format_assistant=StringFormatter(slots=["{{content}}"]),
+    format_assistant=StringFormatter(slots=["{{content}}", {"eos_token"}]),
     format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
     force_system=True,
 )
@@ -644,9 +653,3 @@ _register_template(
     format_user=StringFormatter(slots=[{"token": "<human>"}, ":{{content}}\n", {"token": "<bot>"}, ":"]),
     format_separator=EmptyFormatter(slots=["\n"]),
 )
-
-_register_template(
-    name="atom",
-    format_user=StringFormatter(slots=[{"bos_token"}, "Human: {{content}}\n", {"eos_token"}, {"bos_token"}, "Assistant:"]),
-    format_assistant=StringFormatter(slots=["{{content}}\n", {"eos_token"}]),
-)

src/llmtuner/extras/constants.py

@@ -884,6 +884,7 @@ register_model_group(
     template="zephyr",
 )
 
+
 register_model_group(
     models={
         "Atom-7B": {

src/llmtuner/webui/components/top.py

@@ -34,9 +34,7 @@ def create_top() -> Dict[str, "Component"]:
 
     model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
         get_model_path, [model_name], [model_path], queue=False
-    ).then(
+    ).then(get_template, [model_name], [template], queue=False)  # do not save config since the below line will save
-        get_template, [model_name], [template], queue=False
-    )  # do not save config since the below line will save
 
     model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)