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 |
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