Update README.md

README.md

@@ -28,22 +28,22 @@ The overall performance of chatglm-6b-csc-zh-lora on CSC **test**:
 
 ## Usage
 
-本项目开源在lmft项目：[lmft](https://github.com/shibing624/lmft)，可支持ChatGLM模型，通过如下命令调用：
+本项目开源在textgen项目：[textgen](https://github.com/shibing624/textgen)，可支持ChatGLM原生模型和LoRA微调后的模型，通过如下命令调用：
 
 Install package:
 ```shell
-pip install -U lmft
+pip install -U textgen
 ```
 
 ```python
-from lmft import ChatGlmModel
+from textgen import ChatGlmModel
 model = ChatGlmModel("chatglm", "THUDM/chatglm-6b", lora_name="shibing624/chatglm-6b-csc-zh-lora")
 r = model.predict(["对下面中文拼写纠错：\n少先队员因该为老人让坐。\n答："])
 print(r) # ['少先队员应该为老人让座。\n错误字：因，坐']
 ```
 
 ## Usage (HuggingFace Transformers)
-Without [lmft](https://github.com/shibing624/lmft), you can use the model like this: 
+Without [textgen](https://github.com/shibing624/textgen), you can use the model like this: 
 
 First, you pass your input through the transformer model, then you get the generated sentence.
 
@@ -93,19 +93,16 @@ chatglm-6b-csc-zh-lora
 
 
 
-如果需要训练ChatGLM模型，请参考[https://github.com/shibing624/lmft](https://github.com/shibing624/lmft)
+如果需要训练ChatGLM模型，请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
 
 
 ## Citation
 
 ```latex
-@software{lmft,
+@software{textgen,
   author = {Xu Ming},
-  title = {lmft: Implementation of language model finetune},
-  year = {2023},
-  url = {https://github.com/shibing624/lmft},
+  title = {textgen: Implementation of language model finetune},
+  year = {2021},
+  url = {https://github.com/shibing624/textgen},
 }
 ```
-
-
-