项目地址：[LLMPruner：大语言模型裁剪工具](https://github.com/yangjianxin1/LLMPruner)

LLMPruner是一个大语言模型裁剪工具，通过对大语言模型的冗余词表进行裁剪，减少模型参数量，降低显存占用，提升训练速度，并且能够保留预训练中学习到的知识。

本项目对Bloom进行词表裁剪，保留中文token和常用的英文token，词表由250880将至46145，缩减为原来的18.39%。裁剪得到的Bloom模型如下表：

| 裁剪模型                                                                        | 原模型                                        | 参数量比例  | 
|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------|--------|
| [YeungNLP/bloom-396m-zh](https://huggingface.co/YeungNLP/bloom-396m-zh) | [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m)       | 70.96% |  
| [YeungNLP/bloom-820m-zh](https://huggingface.co/YeungNLP/bloom-820m-zh) | [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1)         | 77.13% |     
| [YeungNLP/bloom-1b4-zh](https://huggingface.co/YeungNLP/bloom-1b4-zh)   | [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7)         | 81.14% |     
| [YeungNLP/bloom-2b6-zh](https://huggingface.co/YeungNLP/bloom-2b6-zh)   | [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b)           | 86.48% |     
| [YeungNLP/bloom-6b4-zh](https://huggingface.co/YeungNLP/bloom-6b4-zh)   | [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1)         |  90.81% |         
| [YeungNLP/bloomz-396m-zh](https://huggingface.co/YeungNLP/bloomz-396m-zh) | [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m)     | 70.96% |     
| [YeungNLP/bloomz-820m-zh](https://huggingface.co/YeungNLP/bloomz-820m-zh) | [bigscience/bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1)       | 77.13% |     
| [YeungNLP/bloomz-1b4-zh](https://huggingface.co/YeungNLP/bloomz-1b4-zh) | [bigscience/bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7)       | 81.14% |     
| [YeungNLP/bloomz-2b6-zh](https://huggingface.co/YeungNLP/bloomz-2b6-zh) | [bigscience/bloomz-3b](https://huggingface.co/bigscience/bloomz-3b)         | 86.48% |     
| [YeungNLP/bloomz-6b4-zh](https://huggingface.co/YeungNLP/bloomz-6b4-zh) | [bigscience/bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1)       | 90.81% |
| [YeungNLP/bloomz-6b4-mt-zh](https://huggingface.co/YeungNLP/bloomz-6b4-mt-zh) | [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) | 90.81% |   


使用方法：
```python
from transformers import BloomTokenizerFast, BloomForCausalLM

tokenizer = BloomTokenizerFast.from_pretrained('YeungNLP/bloom-1b4-zh')
model = BloomForCausalLM.from_pretrained('YeungNLP/bloom-1b4-zh')
print(tokenizer.batch_decode(model.generate(tokenizer.encode('长风破浪会有时', return_tensors='pt'))))
```
{}
transformers pytorch bloom text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

项目地址：LLMPruner：大语言模型裁剪工具


LLMPruner是一个大语言模型裁剪工具，通过对大语言模型的冗余词表进行裁剪，减少模型参数量，降低显存占用，提升训练速度，并且能够保留预训练中学习到的知识。


本项目对Bloom进行词表裁剪，保留中文token和常用的英文token，词表由250880将至46145，缩减为原来的18.39%。裁剪得到的Bloom模型如下表：


裁剪模型: YeungNLP/bloom-396m-zh, 原模型: bigscience/bloom-560m, 参数量比例: 70.96%
裁剪模型: YeungNLP/bloom-820m-zh, 原模型: bigscience/bloom-1b1, 参数量比例: 77.13%
裁剪模型: YeungNLP/bloom-1b4-zh, 原模型: bigscience/bloom-1b7, 参数量比例: 81.14%
裁剪模型: YeungNLP/bloom-2b6-zh, 原模型: bigscience/bloom-3b, 参数量比例: 86.48%
裁剪模型: YeungNLP/bloom-6b4-zh, 原模型: bigscience/bloom-7b1, 参数量比例: 90.81%
裁剪模型: YeungNLP/bloomz-396m-zh, 原模型: bigscience/bloomz-560m, 参数量比例: 70.96%
裁剪模型: YeungNLP/bloomz-820m-zh, 原模型: bigscience/bloomz-1b1, 参数量比例: 77.13%
裁剪模型: YeungNLP/bloomz-1b4-zh, 原模型: bigscience/bloomz-1b7, 参数量比例: 81.14%
裁剪模型: YeungNLP/bloomz-2b6-zh, 原模型: bigscience/bloomz-3b, 参数量比例: 86.48%
裁剪模型: YeungNLP/bloomz-6b4-zh, 原模型: bigscience/bloomz-7b1, 参数量比例: 90.81%
裁剪模型: YeungNLP/bloomz-6b4-mt-zh, 原模型: bigscience/bloomz-7b1-mt, 参数量比例: 90.81%


使用方法：



TAGS
#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #bloom #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
