Add tool_format parameter to rewrite templates for different function call formats.

src/llamafactory/chat/hf_engine.py

@@ -54,7 +54,7 @@ def __init__(
         self.tokenizer = tokenizer_module["tokenizer"]
         self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left" if self.can_generate else "right"
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
         self.model = load_model(
             self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
         )  # must after fixing tokenizer to resize vocab

src/llamafactory/chat/vllm_engine.py

@@ -59,7 +59,7 @@ def __init__(
         self.tokenizer = tokenizer_module["tokenizer"]
         self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left"
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
         self.generating_args = generating_args.to_dict()
 
         engine_args = {

src/llamafactory/data/loader.py

@@ -148,7 +148,7 @@ def get_dataset(
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"] = None,
 ) -> Union["Dataset", "IterableDataset"]:
-    template = get_template_and_fix_tokenizer(tokenizer, data_args.template)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
     if data_args.train_on_prompt and template.efficient_eos:
         raise ValueError("Current template does not support `train_on_prompt`.")
 

src/llamafactory/data/template.py

@@ -379,13 +379,17 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
 def get_template_and_fix_tokenizer(
     tokenizer: "PreTrainedTokenizer",
     name: Optional[str] = None,
+    tool_format: Optional[str] = None,
 ) -> Template:
     if name is None:
         template = TEMPLATES["empty"]  # placeholder
     else:
         template = TEMPLATES.get(name, None)
         if template is None:
             raise ValueError("Template {} does not exist.".format(name))
+
+    if tool_format:
+        template.format_tools = ToolFormatter(tool_format=tool_format)
 
     stop_words = template.stop_words
     if template.replace_eos:

src/llamafactory/hparams/data_args.py

@@ -29,6 +29,10 @@ class DataArguments:
         default=None,
         metadata={"help": "Which template to use for constructing prompts in training and inference."},
     )
+    tool_format: Optional[str] = field(
+        default=None,
+        metadata={"help": "Specifies the tool format template for function calling ."},
+    )
     dataset: Optional[str] = field(
         default=None,
         metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},

tests/data/test_formatter.py

@@ -111,9 +111,9 @@ def test_glm4_tool_formatter():
         }
     ]
     assert formatter.apply(content=json.dumps(tools)) == [
-        "你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
-        "你的任务是针对用户的问题和要求提供适当的答复和支持。"
-        "\n\n## test_tool\n\n{}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(
+        "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
+        "你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具\n\n"
+        "## test_tool\n\n{}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(
             json.dumps(tools[0], indent=4)
         )
     ]