Update README.md

README.md

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 ---
 license: apache-2.0
 ---
+
+## 一、基于baichuan 7b模型进行sft，对其人类意图
+
+
+## 二、sft数据是在开源MOSS数据中通过各个类别均衡采样15w数据进行sft
+
+## 模型推理
+
+Install package:
+```
+pip install transformers
+pip install sentencepiece
+pip install vllm
+```
+### huggingface结合fastapi起服务，支持多轮对话
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import uvicorn
+from fastapi import FastAPI
+import jsonlines
+
+device = 'cuda'
+model_name = 'mxmax/baichuan-7b-sft-001'
+max_new_tokens = 500
+top_p = 0.9
+temperature = 0.35
+repetition_penalty = 1.0
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    device_map={'': 0}#'auto'
+).cuda()
+# model = PeftModel.from_pretrained(model, adapter_name)
+model.eval()
+model = model.to(device)
+# 输入模型的最大长度
+history_max_len = 1024
+def model_infer(user_input):
+    history_token_ids = tokenizer('<s>', return_tensors="pt").input_ids
+    user_input_ids = tokenizer(user_input, return_tensors="pt").input_ids
+    history_token_ids = torch.concat((history_token_ids, user_input_ids[:, -history_max_len:]), dim=1)
+    model_input_ids = history_token_ids.to(device)
+    outputs = model.generate(
+        input_ids=model_input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p,
+        temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id
+    )
+    model_input_ids_len = model_input_ids.size(1)
+    response_ids = outputs[:, model_input_ids_len:]
+    response = tokenizer.batch_decode(response_ids)
+    return response[0].strip().replace('</s>', "")
+
+app = FastAPI()
+
+@app.get('/')
+async def root():
+    return {"msg": "Hello World"}
+
+@app.post('/baichuan_sft_001')
+async def baichuan_sft_001(message: dict):
+    prompt = ''
+    for l in message['context']:
+        prompt += 'human:'+l['human']+'\nassistant:'+l['assistant']+'</s>'
+    result = model_infer(prompt)
+    message['context'][-1]['assistant'] = result
+    return {'model_ouput':result}
+
+if __name__ == '__main__':
+    uvicorn.run('model_serving:app',host="0.0.0.0", port=6006)
+```
+
+
+### vllm结合fastapi起服务,加速推理，支持多轮对话
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import uvicorn
+from fastapi import FastAPI
+import jsonlines
+from vllm import LLM, SamplingParams
+
+device = 'cuda'
+model_name = 'mxmax/baichuan-7b-sft-001'
+max_new_tokens = 512
+top_p = 0.9
+temperature = 0.35
+repetition_penalty = 0.1
+history_max_len = 1024
+sampling_params = SamplingParams(temperature=temperature, top_p=top_p, max_tokens=max_new_tokens, presence_penalty=repetition_penalty)
+
+# Create an LLM.
+llm = LLM(model=model_name,trust_remote_code=True,dtype='float16')
+file = jsonlines.open('chat_record.json','a')
+app = FastAPI()
+
+@app.get('/')
+async def root():
+    return {"msg": "Hello World"}
+
+@app.post('/baichuan_sft_001')
+async def baichuan_sft_001(message: dict):
+    prompt = ''
+    for l in message['context']:
+        prompt += 'human:'+l['human']+'\nassistant:'+l['assistant']+'</s>'
+    prompt = '<s>'+prompt[-history_max_len:]
+    outputs = llm.generate([prompt], sampling_params)
+    result = outputs[0].outputs[0].text
+    message['context'][-1]['assistant'] = result
+    return {'model_ouput':result}
+
+if __name__ == '__main__':
+    uvicorn.run('vllm_serving:app',host="0.0.0.0", port=6006)
+```
+