Add tool_format to overwrite tool formatter template

src/llamafactory/chat/hf_engine.py

@@ -54,7 +54,7 @@ class HuggingfaceEngine(BaseEngine):
         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 @@ class VllmEngine(BaseEngine):
         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/template.py

@@ -379,6 +379,7 @@ 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
@@ -387,6 +388,9 @@ def get_template_and_fix_tokenizer(
         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:
         if not stop_words:
@@ -660,7 +664,6 @@ _register_template(
     format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
     format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
     format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
-    format_tools=ToolFormatter(tool_format="glm4"),
     format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
     stop_words=["<|user|>", "<|observation|>"],
     efficient_eos=True,

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."},