TigerResearch/tigerbot-70b-base-v1

A cutting-edge foundation for your very own LLM.

💻Github • 🌐 TigerBot • 🤗 Hugging Face

快速开始

• 方法1，通过transformers使用

  • 下载 TigerBot Repo

    git clone https://github.com/TigerResearch/TigerBot.git
    

  • 启动infer代码

    python infer.py --model_path TigerResearch/tigerbot-70b-base-v1 --model_type base
    

• 方法2:

  • 下载 TigerBot Repo

    git clone https://github.com/TigerResearch/TigerBot.git
    

  • 安装git lfs： git lfs install

  • 通过huggingface或modelscope平台下载权重

    git clone https://huggingface.co/TigerResearch/tigerbot-70b-base-v1
    git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-base-v1.git
    

  • 启动infer代码

    python infer.py --model_path tigerbot-70b-base-v1 --model_type base
    

---

Quick Start

• Method 1, use through transformers

  • Clone TigerBot Repo

    git clone https://github.com/TigerResearch/TigerBot.git
    

  • Run infer script

    python infer.py --model_path TigerResearch/tigerbot-70b-base-v1 --model_type base
    

• Method 2:

  • Clone TigerBot Repo

    git clone https://github.com/TigerResearch/TigerBot.git
    

  • install git lfs： git lfs install

  • Download weights from huggingface or modelscope

    git clone https://huggingface.co/TigerResearch/tigerbot-70b-base-v1
    git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-base-v1.git
    

  • Run infer script

    python infer.py --model_path tigerbot-70b-base-v1 --model_type base
    

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	62.1
ARC (25-shot)	62.46
HellaSwag (10-shot)	83.61
MMLU (5-shot)	65.49
TruthfulQA (0-shot)	52.76
Winogrande (5-shot)	80.19
GSM8K (5-shot)	37.76
DROP (3-shot)	52.45