add fine tune model

config.py

@@ -126,4 +126,8 @@ put your new bing cookies here
 # 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://help.aliyun.com/document_detail/450255.html
 ENABLE_AUDIO = False
 ALIYUN_TOKEN=""    # 例如 f37f30e0f9934c34a992f6f64f7eba4f
-ALIYUN_APPKEY=""   # 例如 RoPlZrM88DnAFkZK
+ALIYUN_APPKEY=""   # 例如 RoPlZrM88DnAFkZK
+
+
+# ChatGLM Finetune Model Path
+ChatGLM_PTUNING_CHECKPOINT = ""

request_llm/bridge_all.py

@@ -269,6 +269,24 @@ if "newbing" in AVAIL_LLM_MODELS:   # same with newbing-free
         })
     except:
         print(trimmed_format_exc())
+if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
+    try:
+        from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
+        from .bridge_chatglmft import predict as chatglmft_ui
+        # claude
+        model_info.update({
+            "chatglmft": {
+                "fn_with_ui": chatglmft_ui,
+                "fn_without_ui": chatglmft_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
+
 
 def LLM_CATCH_EXCEPTION(f):
     """
@@ -372,6 +390,6 @@ def predict(inputs, llm_kwargs, *args, **kwargs):
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
 
-    method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]
+    method = model_info[llm_kwargs['llm_model']]["fn_with_ui"]  # 如果这里报错，检查config中的AVAIL_LLM_MODELS选项
     yield from method(inputs, llm_kwargs, *args, **kwargs)
 

request_llm/bridge_chatglmft.py

@@ -59,12 +59,18 @@ class GetGLMFTHandle(Process):
                 if self.chatglmft_model is None:
                     from transformers import AutoConfig
                     import torch
-                    conf = 'request_llm\current_ptune_model.json'
-                    if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
-                    with open('request_llm\current_ptune_model.json', 'r', encoding='utf8') as f:
-                        model_args = json.loads(f.read())
-                    
-                    tokenizer = AutoTokenizer.from_pretrained(
+                    # conf = 'request_llm/current_ptune_model.json'
+                    # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
+                    # with open(conf, 'r', encoding='utf8') as f:
+                    #     model_args = json.loads(f.read())
+                    ChatGLM_PTUNING_CHECKPOINT, = get_conf('ChatGLM_PTUNING_CHECKPOINT')
+                    conf = os.path.join(ChatGLM_PTUNING_CHECKPOINT, "config.json")
+                    with open(conf, 'r', encoding='utf8') as f:
+                        model_args_ = json.loads(f.read())
+                        model_args_.update(model_args)
+                        model_args = model_args_
+
+                    self.chatglmft_tokenizer = AutoTokenizer.from_pretrained(
                         model_args['model_name_or_path'], trust_remote_code=True)
                     config = AutoConfig.from_pretrained(
                         model_args['model_name_or_path'], trust_remote_code=True)
@@ -72,17 +78,14 @@ class GetGLMFTHandle(Process):
                     config.pre_seq_len = model_args['pre_seq_len']
                     config.prefix_projection = model_args['prefix_projection']
 
-                    if model_args['ptuning_checkpoint'] is not None:
-                        print(f"Loading prefix_encoder weight from {model_args['ptuning_checkpoint']}")
-                        model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
-                        prefix_state_dict = torch.load(os.path.join(model_args['ptuning_checkpoint'], "pytorch_model.bin"))
-                        new_prefix_state_dict = {}
-                        for k, v in prefix_state_dict.items():
-                            if k.startswith("transformer.prefix_encoder."):
-                                new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
-                        model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
-                    else:
-                        model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
+                    print(f"Loading prefix_encoder weight from {ChatGLM_PTUNING_CHECKPOINT}")
+                    model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
+                    prefix_state_dict = torch.load(os.path.join(ChatGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
+                    new_prefix_state_dict = {}
+                    for k, v in prefix_state_dict.items():
+                        if k.startswith("transformer.prefix_encoder."):
+                            new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+                    model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
 
                     if model_args['quantization_bit'] is not None:
                         print(f"Quantized to {model_args['quantization_bit']} bit")
@@ -91,13 +94,12 @@ class GetGLMFTHandle(Process):
                     if model_args['pre_seq_len'] is not None:
                         # P-tuning v2
                         model.transformer.prefix_encoder.float()
-                    
-                    model = model.eval()
+                    self.chatglmft_model = model.eval()
 
                     break
                 else:
                     break
-            except:
+            except Exception as e:
                 retry += 1
                 if retry > 3: 
                     self.child.send('[Local Message] Call ChatGLMFT fail 不能正常加载ChatGLMFT的参数。')