add mlu model class for comparison

pykoi/chat/llm/constants.py

@@ -10,3 +10,4 @@ class ModelSource(Enum):
     OPENAI = "openai"
     HUGGINGFACE = "huggingface"
     PEFT_HUGGINGFACE = "peft_huggingface"
+    MLU = "mlu"

pykoi/chat/llm/mlu.py

@@ -0,0 +1,49 @@
+"""MLU HF model."""
+from transformers import GenerationConfig
+from pykoi.chat.llm.abs_llm import AbsLlm
+
+from transformers import GenerationConfig
+
+
+class MLUWrapper(AbsLlm):
+    model_source = "mlu_trainer"
+
+    def __init__(self, trainer, tokenizer, name=None):
+        self._trainer = trainer
+        self._model = trainer.model
+        self._tokenizer = tokenizer
+        self._name = name
+        self._model.to("cuda:0")
+        self._model.eval()
+        super().__init__()
+
+    @property
+    def name(self):
+        if self._name:
+            return self._name
+        return "_".join([str(MLUWrapper.model_source), "trainer_model"])
+
+    def predict(self, message: str, num_of_response: int = 1):
+        MAX_RESPONSE = 100
+        prompt_template = """Below is a sentence that you need to complete. Write a response that appropriately completes the request. Sentence: {instruction}\n Response:"""
+        answer_template = """{response}"""
+
+        generation_output = self._model.generate(
+            input_ids=self._tokenizer(
+                prompt_template.format(instruction=message), return_tensors="pt"
+            )["input_ids"].cuda(),
+            generation_config=GenerationConfig(
+                do_sample=False, num_beams=2
+            ),  # Match the standalone function
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=MAX_RESPONSE,
+            num_return_sequences=num_of_response,
+        )
+
+        response = [
+            self._tokenizer.decode(seq, skip_special_tokens=True)
+            for seq in generation_output.sequences
+        ]
+        response = [resp.split("\n")[1] for resp in response if "\n" in resp]
+        return response

pykoi/chat/llm/model_factory.py

@@ -48,9 +48,11 @@ def create_model(model_source: Union[str, ModelSource], **kwargs) -> AbsLlm:
                 from pykoi.chat.llm.peft_huggingface import PeftHuggingfacemodel
 
                 return PeftHuggingfacemodel(**kwargs)
+            elif model_source == ModelSource.MLU:
+                from pykoi.chat.llm.mlu import MLUWrapper
+
+                return MLUWrapper(**kwargs)
             else:
-                raise ValueError(
-                    f"[llm_factory]: Unknown model source {model_source}"
-                )
+                raise ValueError(f"[llm_factory]: Unknown model source {model_source}")
         except ValueError as ex:
             raise ValueError("[llm_factory]: initialize model failure") from ex