Create README.md

README.md

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+使用[Firefly](https://github.com/yangjianxin1/Firefly)项目微调baichuan2-13b-base。训练数据约为一百万多轮对话数据，包括项目分享的moss数据+2万条school math数据。
+
+更多详情见项目：[Firefly](https://github.com/yangjianxin1/Firefly)
+
+CMMLU榜单：
+
+| 模型                        | CMMLU      |
+|---------------------------|------------|
+| Baichuan2-13B-Chat        | 58.4       |
+| **firefly-baichuan2-13b** | **56.83**  |
+| WeMix-LLaMA2-70B          | 	56        |
+| ChatGPT                   | 53.9       |
+| InternLM-20B-Chat         | 	52.2      |
+| Baichuan-13B-Chat         | 50.7       |
+| chinese-alpaca-2-13b      | 45.17      |
+| LLaMA-2-70B-Chat          | 43.3       |
+| openbuddy-llama2-13b-v8.1 | 41.66      |
+| belle-llama2-13b          | 41.57      |
+| ziya-llama-13b            | 39.9       |
+| chinese-alpaca-plus-13b   | 39.9       |
+| flagalpha-llama2-13b-chat | 39.20      |
+| llama-2-13b-chat          | 38.65      |
+| yayi-13b-llama2           | 30.73      |
+| linly-llama2-13b          | 26.32      |
+
+单轮对话:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+"""
+单轮对话，不具有对话历史的记忆功能
+"""
+
+
+def main():
+    model_name = 'YeungNLP/firefly-baichuan2-13b'
+
+    max_new_tokens = 500
+    top_p = 0.9
+    temperature = 0.35
+    repetition_penalty = 1.0
+    device = 'cuda'
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+    ).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        # llama不支持fast
+        use_fast=False if model.config.model_type == 'llama' else True
+    )
+    # QWenTokenizer比较特殊，pad_token_id、bos_token_id、eos_token_id均为None。eod_id对应的token为<|endoftext|>
+    if tokenizer.__class__.__name__ == 'QWenTokenizer':
+        tokenizer.pad_token_id = tokenizer.eod_id
+        tokenizer.bos_token_id = tokenizer.eod_id
+        tokenizer.eos_token_id = tokenizer.eod_id
+
+    text = input('User：')
+    while True:
+        text = text.strip()
+        # chatglm使用官方的数据组织格式
+        if model.config.model_type == 'chatglm':
+            text = '[Round 1]\n\n问：{}\n\n答：'.format(text)
+            input_ids = tokenizer(text, return_tensors="pt", add_special_tokens=False).input_ids.to(device)
+        # 为了兼容qwen-7b，因为其对eos_token进行tokenize，无法得到对应的eos_token_id
+        else:
+            input_ids = tokenizer(text, return_tensors="pt", add_special_tokens=False).input_ids.to(device)
+            bos_token_id = torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long).to(device)
+            eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long).to(device)
+            input_ids = torch.concat([bos_token_id, input_ids, eos_token_id], dim=1)
+        with torch.no_grad():
+            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
+            )
+        outputs = outputs.tolist()[0][len(input_ids[0]):]
+        response = tokenizer.decode(outputs)
+        response = response.strip().replace(tokenizer.eos_token, "").strip()
+        print("Firefly：{}".format(response))
+        text = input('User：')
+
+
+if __name__ == '__main__':
+    main()
+```
+
+
+多轮对话:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+def main():
+    model_name = 'YeungNLP/firefly-baichuan2-13b'
+
+    device = 'cuda'
+    max_new_tokens = 500    # 每轮对话最多生成多少个token
+    history_max_len = 1000  # 模型记忆的最大token长度
+    top_p = 0.9
+    temperature = 0.35
+    repetition_penalty = 1.0
+
+    # 加载模型
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+    ).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True,
+        # llama不支持fast
+        use_fast=False if model.config.model_type == 'llama' else True
+    )
+    # QWenTokenizer比较特殊，pad_token_id、bos_token_id、eos_token_id均为None。eod_id对应的token为<|endoftext|>
+    if tokenizer.__class__.__name__ == 'QWenTokenizer':
+        tokenizer.pad_token_id = tokenizer.eod_id
+        tokenizer.bos_token_id = tokenizer.eod_id
+        tokenizer.eos_token_id = tokenizer.eod_id
+
+    # 记录所有历史记录
+    if model.config.model_type != 'chatglm':
+        history_token_ids = torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long)
+    else:
+        history_token_ids = torch.tensor([[]], dtype=torch.long)
+
+    # 开始对话
+    utterance_id = 0    # 记录当前是第几轮对话，为了契合chatglm的数据组织格式
+    user_input = input('User：')
+    while True:
+        utterance_id += 1
+        # chatglm使用官方的数据组织格式
+        if model.config.model_type == 'chatglm':
+            user_input = '[Round {}]\n\n问：{}\n\n答：'.format(utterance_id, user_input)
+            user_input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids
+        # firefly的数据组织格式
+        # 为了兼容qwen-7b，因为其对eos_token进行tokenize，无法得到对应的eos_token_id
+        else:
+            input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids
+            eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long)
+            user_input_ids = torch.concat([input_ids, eos_token_id], dim=1)
+        history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1)
+        model_input_ids = history_token_ids[:, -history_max_len:].to(device)
+        with torch.no_grad():
+            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(tokenizer.eos_token, ""))
+        user_input = input('User：')
+
+
+if __name__ == '__main__':
+    main()
+```