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---
license: bigscience-bloom-rail-1.0
language:
- zh
---
# 体验链接
1. 🔗[http://101.68.79.42:7861/](http://101.68.79.42:7861/)
## 🚀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 |
## 介绍
1. ✅ 对`bloom-7b`模型做了sft，本次版本为V2版本(使用了150w条有监督数据做sft)，相较于V1版本，效果更好！！！
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)
## 如何使用
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "yuanzhoulvpi/chinese_bloom_7b_chat_v2"#"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]))
```
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