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使用[Firefly](https://github.com/yangjianxin1/Firefly)项目微调baichuan-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)
技术细节分享：[Firefly增强Baichuan-13B的多轮对话能力](https://mp.weixin.qq.com/s/djO8Tg3emmy6wzw_rTUlcw)
训练loss：
[//]: # (<img src="https://huggingface.co/YeungNLP/firefly-baichuan-13b/resolve/main/firefly-baichuan-13b-loss.jpg" width="450">)
C-Eval榜单：
| Model | C-Eval | STEM | Social Science | Humanities | Other |
|----------------------------------|--------|-------|----------------|------------|-------|
| Baichuan-13B-Chat(官方) | 52.05 | 42.23 | 65.27 | 58.61 | 51.32 |
| **firefly-baichuan-13b** | 51.36 | 44.24 | 61.65 | 54.63 | 51.68 |
| chatglm2-6b(官方) | 50.45 | 41.91 | 60.73 | 59.24 | 47.82 |
| **firefly-chatglm2-6b** | 49.13 | 43.6 | 58.83 | 54.48 | 45.03 |
| openbuddy-llama2-13b-v11.1-bf16 | 43.36 | 39.79 | 50.28 | 44.78 | 42.13 |
| chinese-alpaca-2-13b(哈工大) | 41.86 | 36.52 | 49.7 | 47.97 | 38.33 |
| openbuddy-llama2-13b-v8.1-fp16 | 41.62 | 38.82 | 44.66 | 40.28 | 45.32 |
| chinese-alpaca-2-7b(哈工大) | 41.48 | 35.01 | 50.08 | 43.02 | 43.87 |
| belle-llama2-13B-chat-0.4M | 41.11 | 40.04 | 44.71 | 42.09 | 38.82 |
| ziya-llama-13b | 39.1 | - | - | - | - |
| llama-2-13b-chat(官方) | 36.38 | 33.68 | 46.38 | 34.47 | 34.1 |
| lama-2-7b-chat(官方) | 35.86 | 32.85 | 40.04 | 37.37 | 36.01 |
| flagalpha/Llama2-Chinese-7b-Chat | 34.54 | 35.21 | 37.9 | 33.11 | 31.7 |
| yayi-13b-llama2 | 34.15 | 36.48 | 30.64 | 32.67 | 34.6 |
| yayi-7b-llama2 | 30.18 | 25.88 | 38.23 | 34.56 | 26.31 |
| linly-llama2-7b | 28.35 | 26.06 | 33.47 | 29.71 | 26.53 |
| linly-llama2-13b | 27.86 | 27.67 | 26.95 | 27.93 | 28.95 |
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-baichuan-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-baichuan-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()
```
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