README.md

使用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

技术细节分享：Firefly增强Baichuan-13B的多轮对话能力

训练loss：

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:

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:

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()