fix #2438

src/llmtuner/chat/chat_model.py

@@ -94,6 +94,9 @@ class ChatModel:
         tools: Optional[str] = None,
         **input_kwargs,
     ) -> List[Response]:
+        if not self.can_generate:
+            raise ValueError("The current model does not support `chat`.")
+
         gen_kwargs, prompt_length = self._process_args(messages, system, tools, **input_kwargs)
         generate_output = self.model.generate(**gen_kwargs)
         response_ids = generate_output[:, prompt_length:]
@@ -123,6 +126,9 @@ class ChatModel:
         tools: Optional[str] = None,
         **input_kwargs,
     ) -> Generator[str, None, None]:
+        if not self.can_generate:
+            raise ValueError("The current model does not support `stream_chat`.")
+
         gen_kwargs, _ = self._process_args(messages, system, tools, **input_kwargs)
         streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
         gen_kwargs["streamer"] = streamer
@@ -134,9 +140,11 @@ class ChatModel:
 
     @torch.inference_mode()
     def get_scores(self, batch_input: List[str], **input_kwargs) -> List[float]:
+        if self.can_generate:
+            raise ValueError("Cannot get scores using an auto-regressive model.")
+
         max_length = input_kwargs.pop("max_length", None)
         device = getattr(self.model.pretrained_model, "device", "cuda")
-
         inputs = self.tokenizer(
             batch_input,
             padding=True,

src/llmtuner/model/patcher.py

@@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 import torch
 from datasets import load_dataset
+from peft import PeftModel
 from transformers import BitsAndBytesConfig, GPTQConfig, PreTrainedModel, PreTrainedTokenizerBase
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.utils.versions import require_version
@@ -307,7 +308,12 @@ def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
         if isinstance(self.pretrained_model, PreTrainedModel):
             return self.pretrained_model.get_input_embeddings()
 
+    def create_or_update_model_card(self: "AutoModelForCausalLMWithValueHead", output_dir: str) -> None:
+        if isinstance(self.pretrained_model, PeftModel):
+            self.pretrained_model.create_or_update_model_card(output_dir)
+
     ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
     setattr(model, "_keys_to_ignore_on_save", ignore_modules)
     setattr(model, "tie_weights", MethodType(tie_weights, model))
     setattr(model, "get_input_embeddings", MethodType(get_input_embeddings, model))
+    setattr(model, "create_or_update_model_card", MethodType(create_or_update_model_card, model))