模型链接	训练的数据量	模型版本	备注
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat	15w中文指令数据	v1
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat_v2	150w条中文指令数据	v2	目前已经测试过效果，相较于v1，效果有所提升
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat_v3	420w条中文指令数据	v3	目前效果还没测试，欢迎大家测试

个人感受

🎯 bloom系列的模型，在中文领域，具有极大的潜力，在经过有监督微调训练之后，效果非常惊人！
🔄 bloom系列的模型，覆盖中文、英文、代码、法语、西班牙语等。即使拿来做翻译、拿来做代码生成，也都没问题！（后期将会分享相关教程）
😛 当前的这个bloom-7b模型，我是非常喜欢滴，特地在8xA100机器上训练了部分数据。整体效果非常不错~

如何使用

python

from transformers import AutoModelForCausalLM, AutoTokenizer


checkpoint = "yuanzhoulvpi/chinese_bloom_7b_chat"#"bigscience/bloomz-3b" #"bigscience/bloom-7b1"#  "output_dir/checkpoint-8260"#
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).half().cuda()

PROMPT_DICT = {
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:"
    ),
}

from typing import Optional
def generate_input(instruction:Optional[str]= None, input_str:Optional[str] = None) -> str:
    if input_str is None:
        return PROMPT_DICT['prompt_no_input'].format_map({'instruction':instruction})
    else:
        return PROMPT_DICT['prompt_input'].format_map({'instruction':instruction, 'input':input_str})


for i in range(5):
    print("*"*80)

    inputs = tokenizer.encode(generate_input(instruction="你是谁"), return_tensors="pt")
    outputs = model.generate(inputs,num_beams=3,
                            max_new_tokens=512,
                            do_sample=False, 
                            top_k=10,
                            penalty_alpha=0.6,
                            temperature=0.8,
                            repetition_penalty=1.2)
    print(tokenizer.decode(outputs[0]))

效果

不管是写代码还是写文案，bloom-7b在中文领域有极大的潜力

example 1
example 2
example 3
example 4
example 5