tiny fix

Former-commit-id: bb57478366a70a0871af30ab31c890f471e27ff4

src/llamafactory/data/formatter.py

@@ -216,7 +216,7 @@ class ToolFormatter(Formatter):
             self._tool_formatter = glm4_tool_formatter
             self._tool_extractor = glm4_tool_extractor
         else:
-            raise ValueError("Tool format was not found.")
+            raise NotImplementedError("Tool format {} was not found.".format(self.tool_format))
 
     def apply(self, **kwargs) -> SLOTS:
         content = kwargs.pop("content")

src/llamafactory/data/template.py

@@ -387,8 +387,9 @@ def get_template_and_fix_tokenizer(
         template = TEMPLATES.get(name, None)
         if template is None:
             raise ValueError("Template {} does not exist.".format(name))
-        
-    if tool_format:
+
+    if tool_format is not None:
+        logger.info("Using tool format: {}.".format(tool_format))
         template.format_tools = ToolFormatter(tool_format=tool_format)
 
     stop_words = template.stop_words
@@ -625,7 +626,6 @@ _register_template(
 
 _register_template(
     name="empty",
-    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     efficient_eos=True,
 )
 

src/llamafactory/hparams/data_args.py

@@ -29,10 +29,6 @@ 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."},
@@ -105,6 +101,10 @@ class DataArguments:
             "help": "Whether or not to pack the sequences in training. Will automatically enable in pre-training."
         },
     )
+    tool_format: Optional[str] = field(
+        default=None,
+        metadata={"help": "Tool format to use for constructing function calling examples."},
+    )
     tokenized_path: Optional[str] = field(
         default=None,
         metadata={"help": "Path to save or load the tokenized datasets."},

src/llamafactory/webui/components/train.py

@@ -291,7 +291,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         with gr.Column(scale=1):
             loss_viewer = gr.Plot()
 
-    input_elems.update({output_dir, config_path, device_count, ds_stage, ds_offload})
+    input_elems.update({output_dir, config_path, ds_stage, ds_offload})
     elem_dict.update(
         dict(
             cmd_preview_btn=cmd_preview_btn,

src/llamafactory/webui/runner.py

@@ -306,7 +306,7 @@ class Runner:
 
     def _form_config_dict(self, data: Dict["Component", Any]) -> Dict[str, Any]:
         config_dict = {}
-        skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path", "train.device_count"]
+        skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path"]
         for elem, value in data.items():
             elem_id = self.manager.get_id_by_elem(elem)
             if elem_id not in skip_ids: