up

request_llm/bridge_chatglmft.py

@@ -63,9 +63,9 @@ class GetGLMFTHandle(Process):
                     # 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')
-                    assert os.path.exists(ChatGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
-                    conf = os.path.join(ChatGLM_PTUNING_CHECKPOINT, "config.json")
+                    CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT')
+                    assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
+                    conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
                     with open(conf, 'r', encoding='utf8') as f:
                         model_args = json.loads(f.read())
                     if 'model_name_or_path' not in model_args:
@@ -78,9 +78,9 @@ class GetGLMFTHandle(Process):
                     config.pre_seq_len = model_args['pre_seq_len']
                     config.prefix_projection = model_args['prefix_projection']
 
-                    print(f"Loading prefix_encoder weight from {ChatGLM_PTUNING_CHECKPOINT}")
+                    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"))
+                    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."):