---
license: bigscience-bloom-rail-1.0
language:
- zh
---


# 体验
 🚀 点击链接，即可体验🔗 **[http://101.68.79.42:7861/](http://101.68.79.42:7861/)**


## 介绍
1. ✅ 对`bloom-7b`模型做了sft
2. 🚀 训练代码和推理代码全部分享，可以查看链接[https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/chinese_bloom](https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/chinese_bloom)

## 🚀更新
| 模型链接                                                                                                                         | 训练的数据量    | 模型版本 | 备注                     |
|------------------------------------------------------------------------------------------------------------------------------|-----------|------|------------------------|
| [https://huggingface.co./yuanzhoulvpi/chinese_bloom_7b_chat](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](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](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
1. 🎯 `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！
2. 🔄 `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）
3. 😛 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
![](images/a923de3471e716b2f31f81cf5d594fe8.jpg)
- example 2
![](images/ca8400fa29e7302bde72c9108f74f78f.jpg)
- example 3
![](images/d14a752bce41fe613d6732b83c5861c1.jpg)
![](images/38373adaf09c3bc179d7652f3ee9dacb.jpg)
- example 4
![](images/WechatIMG3534.jpeg)
- example 5
![](images/WechatIMG3535.jpeg)