support minicpm #2404

src/llmtuner/api/app.py

@@ -120,6 +120,9 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
 
     def chat_completion(messages: Sequence[Dict[str, str]], system: str, tools: str, request: ChatCompletionRequest):
         if request.stream:
+            if tools:
+                raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
+
             generate = stream_chat_completion(messages, system, tools, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
 

src/llmtuner/data/template.py

@@ -218,7 +218,7 @@ def register_template(
     default_user_formatter = StringFormatter(slots=["{{content}}"])
     default_assistant_formatter = StringFormatter(slots=["{{content}}"] + eos_slots)
     default_function_formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}"] + eos_slots)
-    default_tool_formatter = ToolFormatter(slots="default")
+    default_tool_formatter = ToolFormatter(tool_format="default")
     default_separator_formatter = EmptyFormatter()
     templates[name] = template_class(
         format_user=format_user or default_user_formatter,
@@ -356,6 +356,14 @@ register_template(
 )
 
 
+register_template(
+    name="cpm",
+    format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
+    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    force_system=True,
+)
+
+
 register_template(
     name="deepseek",
     format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
@@ -464,7 +472,7 @@ register_template(
 
 register_template(
     name="orion",
-    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: </s>"]),
+    format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
     format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
     force_system=True,
 )

src/llmtuner/hparams/parser.py

@@ -63,13 +63,12 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
         if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
             raise ValueError("Cannot create new adapter upon a quantized model.")
 
-    if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
-        if finetuning_args.finetuning_type != "lora":
-            raise ValueError("Multiple adapters are only available for LoRA tuning.")
-
-        if model_args.quantization_bit is not None:
+        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
             raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
 
+    if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Only LoRA method has adapters.")
+
 
 def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     parser = HfArgumentParser(_TRAIN_ARGS)