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lm_eval/models/huggingface.py

@@ -96,6 +96,10 @@ class HFLM(LM):
         # PEFT and quantization options
         peft: Optional[str] = None,
         autogptq: Optional[Union[bool, str]] = False,
+        # Chat templating settings
+        use_chat_template: Optional[bool] = False,
+        # TODO: validate a template exists in tokenizer config, if this flag is true
+        system_prompt: Optional[str] = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -241,6 +245,9 @@ class HFLM(LM):
             else:
                 self.tokenizer.add_special_tokens({"pad_token": "<|pad|>"})
 
+        self.system_prompt = system_prompt
+        self.use_chat_template = use_chat_template
+
         self._max_length = max_length
 
         self.batch_schedule = 1
@@ -691,9 +698,11 @@ class HFLM(LM):
             context, continuation = req.args[0].strip(), req.args[1]
             chat = []
             if self.system_prompt is not None:
-                chat += {"role": "system", "content": "You are a helpful assistant."}
+                chat += [{"role": "system", "content": "You are a helpful assistant."}]
 
-            chat += ({"role": "user", "content": context},)
+            chat += [
+                {"role": "user", "content": context},
+            ]
             # TODO: expose settings for chat formatting:
             # - whether some "trigger" / start of assistant response might be placed in assistant's generation for it
             # - if few-shot, should the fewshots be placed in separate convo turns? provided in user's single turn?...
@@ -786,6 +795,7 @@ class HFLM(LM):
         return context_enc, continuation_enc
 
     def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        if self.use_chat_template:
             print(f"First element before prompt formatting...\n{requests[0].args}")
             requests = self.wrap_chat_template(requests)
             print(f"First element after prompt formatting...\n{requests[0].args}")
@@ -1064,6 +1074,7 @@ class HFLM(LM):
         return re_ord.get_original(res)
 
     def generate_until(self, requests: List[Instance]) -> List[str]:
+        if self.use_chat_template:
             print(f"First element before prompt formatting...\n{requests[0].args}")
             requests = self.tok_chat_template(requests)
             print(f"First element after prompt formatting...\n{requests[0].args}")