Merge branch 'main' of github.com:THUDM/ChatGLM-6B

PROJECT.md

@@ -11,6 +11,8 @@
 * [ChatGLM-Finetuning](https://github.com/liucongg/ChatGLM-Finetuning)：基于ChatGLM-6B模型，进行下游具体任务微调，涉及Freeze、Lora、P-tuning等，并进行实验效果对比。
 * [InstructGLM](https://github.com/yanqiangmiffy/InstructGLM)：基于ChatGLM-6B进行指令学习，汇总开源中英文指令数据，基于Lora进行指令数据微调，开放了Alpaca、Belle微调后的Lora权重，修复web_demo重复问题
 * [ChatGLM-web](https://github.com/NCZkevin/chatglm-web)：基于FastAPI和Vue3搭建的ChatGLM演示网站(支持chatglm流式输出、前端调整模型参数、上下文选择、保存图片、知识库问答等功能) 
+* [glm-bot](https://github.com/initialencounter/glm-bot)：将ChatGLM接入Koishi可在各大聊天平台上调用ChatGLM
 
 以下是部分针对本项目的教程/文档：
 * [Windows部署文档](https://github.com/ZhangErling/ChatGLM-6B/blob/main/deployment_windows.md)
+* [ChatGLM-6B 的部署与微调教程 @ModelWhale平台](https://www.heywhale.com/mw/project/6436d82948f7da1fee2be59e)

ptuning/main.py

@@ -354,6 +354,7 @@ def main():
         tokenizer=tokenizer,
         data_collator=data_collator,
         compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        save_prefixencoder=model_args.pre_seq_len is not None
     )
 
     # Training

ptuning/trainer.py

@@ -317,7 +317,9 @@ class Trainer:
         callbacks: Optional[List[TrainerCallback]] = None,
         optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
         preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        save_prefixencoder: bool = False,
     ):
+        self.save_prefixencoder = save_prefixencoder
         if args is None:
             output_dir = "tmp_trainer"
             logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
@@ -2825,12 +2827,17 @@ class Trainer:
                     state_dict = self.model.state_dict()
                 torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
         else:
-            state_dict = self.model.state_dict()
-            filtered_state_dict = {}
-            for k, v in self.model.named_parameters():
-                if v.requires_grad:
-                    filtered_state_dict[k] = state_dict[k]
-            self.model.save_pretrained(output_dir, state_dict=filtered_state_dict)
+            if self.save_prefixencoder:
+                print("Saving PrefixEncoder")
+                state_dict = self.model.state_dict()
+                filtered_state_dict = {}
+                for k, v in self.model.named_parameters():
+                    if v.requires_grad:
+                        filtered_state_dict[k] = state_dict[k]
+                self.model.save_pretrained(output_dir, state_dict=filtered_state_dict)
+            else:
+                print("Saving the whole model")
+                self.model.save_pretrained(output_dir, state_dict=state_dict)
         if self.tokenizer is not None:
             self.tokenizer.save_pretrained(output_dir)