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ChatGLM-6B-INT4-Slim: 低显存版ChatGLM-6B-INT4

介绍

ChatGLM-6B-INT4-Slim是在ChatGLM-6B-INT4的基础上通过裁剪词表构建的。因为ChatGLM-6B使用了icetk,在其词表中,前20000个token是预留给图片的,在文本模型中没有用到这些图片token,但是在infer和微调的时候,这些token对应的embedding依然需要被加载,并且在解码每一个token的时候需要多计算20K个logits,会占用不少显存。因此将这一部分token裁剪掉以节省显存。

除了词表外,ChatGLM-6B-INT4-Slim的其他结构与ChatGLM-6B-INT4完全一致,性能也完全一样,可以认为是ChatGLM-6B-INT4的一个低显存版等价平替。

ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型,基于 General Language Model (GLM) 架构,具有 62 亿参数。结合模型量化技术,用户可以在消费级的显卡上进行本地部署(INT4 量化级别下最低只需 6GB 显存)。ChatGLM-6B 使用了和 ChatGLM 相同的技术,针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练,辅以监督微调、反馈自助、人类反馈强化学习等技术的加持,62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。

ChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的,ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化,没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存(使用 CPU 即内存)即可推理,具有在嵌入式设备(如树莓派)上运行的可能。

在 CPU 上运行时,会根据硬件自动编译 CPU Kernel ,请确保已安装 GCC 和 OpenMP (Linux一般已安装,对于Windows则需手动安装),以获得最佳并行计算能力。

模型所有版权和Credit归ChatGLM官方团队所有,ChatGLM-6B-INT4-Slim只是为了方便大家使用而制作。

软件依赖

pip install protobuf==3.20.0 transformers==4.26.1 icetk cpm_kernels

代码调用

可以通过如下代码调用 ChatGLM-6B 模型来生成对话:

>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("silver/chatglm-6b-int4-slim", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("silver/chatglm-6b-int4-slim", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:

1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。

如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。

关于更多的使用说明,包括如何运行命令行和网页版本的 DEMO,以及使用模型量化以节省显存,请参考 Github Repo

协议

本仓库的代码依照 Apache-2.0 协议开源,ChatGLM-6B 模型的权重的使用则需要遵循 Model License

引用

如果你觉得这个工作有帮助的话,请考虑引用ChatGLM官方团队的论文:

@inproceedings{
  zeng2023glm-130b,
  title={{GLM}-130B: An Open Bilingual Pre-trained Model},
  author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
  booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
  year={2023},
  url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
@inproceedings{du2022glm,
  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={320--335},
  year={2022}
}
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