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---
license: bigscience-bloom-rail-1.0
language:
- zh
---
## 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)
## 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]))
```
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