Update README.md

README.md

@@ -1,3 +1,45 @@
 ---
 license: cc-by-nc-4.0
+datasets:
+- yuyijiong/Long-Instruction-Chinese
+language:
+- zh
+- en
+pipeline_tag: text-generation
 ---
+[LongAlpaca](https://huggingface.co/Yukang/LongAlpaca-7B)通过对 llama2-chat 进行少量长文本数据的微调，展现出了优秀的长文本对话能力。\
+LongAlpaca-7b-chinese 和 LongAlpaca 使用类似的训练方法：先使用线性位置插值，然后通过少量长文本数据的微调，使其获得优秀的长文本对话能力。\
+此模型由atom-7b-chat经过lora微调得到， 通过线性位置插值，将文本长度从4k扩展到16k，可以完成上万字的多文档检索、论文总结等任务，而短对话能力几乎没有下降。\
+使用方法：
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+model_path="yuyijiong/LongAlpaca-7b-chinese"
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# use auto mode, automatically select precision based on the device.
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", load_in_8bit=True).eval()
+
+question="中国的首都是什么？"
+input_text = "<s>Human: " + question + "\n</s><s>Assistant: "
+input_ids = tokenizer(input_text, return_tensors='pt').input_ids.to(model.device)
+
+with torch.no_grad():
+    with torch.autocast('cuda'):
+        output = model.generate(input_ids=input_ids,
+                                max_new_tokens=max_new_tokens,
+                                do_sample=True,
+                                temperature=0.85,
+                                top_k=None,
+                                top_p=0.9,
+                                use_cache=True,
+                                **kwargs)
+
+reply = tokenizer.decode(output[0], skip_special_tokens=False)
+reply_return=reply.split('Assistant:')[-1].replace('</s>', '')
+
+print('模型回答：', reply_return)
+```