fix(chatglm): generation tokens not concatenated correctly

Signed-off-by: aarnphm-ec2-dev <29749331+aarnphm@users.noreply.github.com>

src/openllm/models/chatglm/configuration_chatglm.py

@@ -41,7 +41,7 @@ class ChatGLMConfig(
     Refer to [ChatGLM's GitHub page](https://github.com/THUDM/ChatGLM-6B) for more information.
     """
 
-    retain_history: bool = True
+    retain_history: bool = False
     """Whether to retain history given to the model. If set to True, then the model will retain given history."""
 
     use_half_precision: bool = True

src/openllm/models/chatglm/modeling_chatglm.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 from __future__ import annotations
 
-import re
 import typing as t
 
 import bentoml
@@ -39,32 +38,12 @@ class InvalidScoreLogitsProcessor(LogitsProcessor):
         return scores
 
 
-def process_response(
-    response: str,
-    use_default_prompt_template: bool = True,
-):
-    response = response.strip()
-    if use_default_prompt_template:
-        response = response.replace("[[训练时间]]", "2023年")
-        punkts = [
-            [",", "，"],
-            ["!", "！"],
-            [":", "："],
-            [";", "；"],
-            ["\?", "？"],
-        ]
-        for item in punkts:
-            response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
-            response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
-    return response
-
-
 class ChatGLM(openllm.LLM):
     __openllm_internal__ = True
 
-    default_model = "THUDM/chatglm-6b-int4"
+    default_model = "thudm/chatglm-6b-int4"
 
-    pretrained = ["THUDM/chatglm-6b", "THUDM/chatglm-6b-int8", "THUDM/chatglm-6b-int4"]
+    pretrained = ["thudm/chatglm-6b", "thudm/chatglm-6b-int8", "thudm/chatglm-6b-int4"]
 
     device = torch.device("cuda")
 
@@ -121,7 +100,7 @@ class ChatGLM(openllm.LLM):
         if self.config.retain_history:
             assert chat_history is not None, "'retain_history' is True while there is no history provided."
             chat_history.append((prompt, generation_result))
-        return generation_result
+        return "".join(generation_result)
 
     @torch.inference_mode()
     def generate(self, prompt: str, use_default_prompt_template: bool = True, **attrs: t.Any) -> str:
@@ -144,4 +123,4 @@ class ChatGLM(openllm.LLM):
         )
         outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) :]
         response = self.tokenizer.decode(outputs)
-        return process_response(response, use_default_prompt_template)
+        return self.model.process_response(response)