fix: use both eos and bos tokens as stop sequences for hf-tokenizer-c…

…onfig chat format.

llama_cpp/llama_chat_format.py

@@ -379,7 +379,8 @@ def hf_autotokenizer_to_chat_completion_handler(
 
 
 def hf_tokenizer_config_to_chat_formatter(
-    tokenizer_config: Dict[str, Any]
+    tokenizer_config: Dict[str, Any],
+    add_generation_prompt: bool = True,
 ) -> ChatFormatter:
     assert isinstance(tokenizer_config, dict)
 
@@ -401,31 +402,34 @@ def hf_tokenizer_config_to_chat_formatter(
         lstrip_blocks=True,
     ).from_string(chat_template)
 
-    def format_autotokenizer(
+    def format_tokenizer_config(
         messages: List[llama_types.ChatCompletionRequestMessage],
         **kwargs: Any,
     ) -> ChatFormatterResponse:
         # TODO: veryify this is correct
         # Add a blank assistant message to the end of the messages to prompt the model to generate a response
-        prompt = env.render(
-            messages=[
+        if add_generation_prompt:
+            messages = [
                 *messages,
                 llama_types.ChatCompletionRequestAssistantMessage(
                     role="assistant", content=""
                 ),
-            ],
+            ]
+        prompt = env.render(
+            messages=messages,
             bos_token=bos_token,
             eos_token=eos_token,
         )
-        return ChatFormatterResponse(prompt=prompt, stop=eos_token)
+        return ChatFormatterResponse(prompt=prompt, stop=[eos_token, bos_token])
 
-    return format_autotokenizer
+    return format_tokenizer_config
 
 
 def hf_tokenizer_config_to_chat_completion_handler(
     tokenizer_config: Dict[str, Any],
+    add_generation_prompt: bool = True,
 ) -> LlamaChatCompletionHandler:
-    chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config)
+    chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config, add_generation_prompt=add_generation_prompt)
     return chat_formatter_to_chat_completion_handler(chat_formatter)