Update README.md

README.md

@@ -1,9 +1,4 @@
----
-language:
-- en
-- zh
-pipeline_tag: text-generation
----
+
 # Notice/注意事项
 **Qwen官方模型临时下架了，我将9月11日下载到本地的Qwen-7B-Chat模型重新上传到huggingface。**
 **该模型为9月11日下载的版本。经过多人下载测试可以正常使用。**
@@ -90,19 +85,19 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers.generation import GenerationConfig
 
 # Note: The default behavior now has injection attack prevention off.
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("tangger/Qwen-7B-Chat", trust_remote_code=True)
 
 # use bf16
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
+# model = AutoModelForCausalLM.from_pretrained("tangger/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
 # use fp16
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
+# model = AutoModelForCausalLM.from_pretrained("tangger/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
 # use cpu only
-# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval()
+# model = AutoModelForCausalLM.from_pretrained("tangger/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval()
 # use auto mode, automatically select precision based on the device.
-model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()
+model = AutoModelForCausalLM.from_pretrained("tangger/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()
 
 # Specify hyperparameters for generation
-model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
+model.generation_config = GenerationConfig.from_pretrained("tangger/Qwen-7B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
 
 # 第一轮对话 1st dialogue turn
 response, history = model.chat(tokenizer, "你好", history=None)