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internlm/
internlm3-8b-instruct-smoothquant-fp8

Name: internlm3-8b-instruct-smoothquant-fp8
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InternLM

InternLM ^HOT

💻Github Repo • 🤗Demo • 🤔Reporting Issues • 📜Technical Report

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Introduction

This is a quantization of internlm/internlm3-8b-instruct for NVIDIA GPUs like Ada Lovelace and Hopper architectures. Refer to lmdeploy for more information.

InternLM3 has open-sourced an 8-billion parameter instruction model, InternLM3-8B-Instruct, designed for general-purpose usage and advanced reasoning. This model has the following characteristics:

Enhanced performance at reduced cost: State-of-the-art performance on reasoning and knowledge-intensive tasks surpass models like Llama3.1-8B and Qwen2.5-7B. Remarkably, InternLM3 is trained on only 4 trillion high-quality tokens, saving more than 75% of the training cost compared to other LLMs of similar scale.
Deep thinking capability: InternLM3 supports both the deep thinking mode for solving complicated reasoning tasks via the long chain-of-thought and the normal response mode for fluent user interactions.

Usage

LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.

pip install lmdeploy

You can run batch inference locally with the following python code:

python

import lmdeploy
model_id = "internlm/internlm3-8b-instruct-smoothquant-fp8"
pipe = lmdeploy.pipeline(model_id)
response = pipe("Please tell me five scenic spots in Shanghai")
print(response)

Or you can launch an OpenAI compatible server with the following command:

lmdeploy serve api_server internlm/internlm3-8b-instruct-smoothquant-fp8 --model-name internlm3-8b-instruct --server-port 23333

Then you can send a chat request to the server:

bash

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm3-8b-instruct",
    "messages": [
    {"role": "user", "content": "Please tell me five scenic spots in Shanghai"}
    ]
    }'

Find more details in the LMDeploy documentation

Open Source License

Code and model weights are licensed under Apache-2.0.

Citation

@misc{cai2024internlm2,
      title={InternLM2 Technical Report},
      author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin},
      year={2024},
      eprint={2403.17297},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}