Create README.md

README.md

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+QLoRA+百万数据对baichun-7b模型进行高效指令微调
+
+更多详情请查看Github项目: [Firefly(流萤): 中文对话式大语言模型(全量微调+QLoRA)](https://github.com/yangjianxin1/Firefly)
+
+单轮对话脚本:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = 'YeungNLP/firefly-baichuan-7b-qlora-sft-merge'
+max_new_tokens = 500
+top_p = 0.9
+temperature = 0.35
+repetition_penalty = 1.0
+device = 'cuda'
+input_pattern = '<s>{}</s>'
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    device_map='auto'
+)
+model.eval()
+model = model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+text = input('User：')
+while True:
+    text = input_pattern.format(text)
+    input_ids = tokenizer(text, return_tensors="pt").input_ids
+    input_ids = input_ids.to(device)
+    outputs = model.generate(
+        input_ids=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
+    )
+    rets = tokenizer.batch_decode(outputs)
+    output = rets[0].strip().replace(text, "").replace('</s>', "")
+    print("Firefly：{}".format(output))
+    text = input('User：')
+```
+
+
+多轮对话脚本:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+device = 'cuda'
+model_name = 'YeungNLP/firefly-baichuan-7b1-qlora-sft-merge'
+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='auto'
+)
+model.eval()
+model = model.to(device)
+# 记录所有历史记录
+history_token_ids = tokenizer('<s>', return_tensors="pt").input_ids
+# 输入模型的最大长度
+history_max_len = 1000
+user_input = input('User：')
+while True:
+    user_input = '{}</s>'.format(user_input)
+    user_input_ids = tokenizer(user_input, return_tensors="pt").input_ids
+    history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1)
+    model_input_ids = history_token_ids[:, -history_max_len:].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:]
+    history_token_ids = torch.concat((history_token_ids, response_ids.cpu()), dim=1)
+    response = tokenizer.batch_decode(response_ids)
+    print("Firefly：" + response[0].strip().replace('</s>', ""))
+    user_input = input('User：')
+```
+