How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "TigerResearch/tigerbot-13b-base-v2" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TigerResearch/tigerbot-13b-base-v2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
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-13b-base-v2 --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-13b-base-v2 git clone https://www.modelscope.cn/TigerResearch/tigerbot-13b-base-v2.git启动infer代码
python infer.py --model_path tigerbot-13b-base-v2 --model_type base --max_generate_length 64
Quick Start
Method 1, use through transformers
Clone TigerBot Repo
git clone https://github.com/TigerResearch/TigerBot.gitRun infer script
python infer.py --model_path TigerResearch/tigerbot-13b-base-v2 --model_type base
Method 2:
Clone TigerBot Repo
git clone https://github.com/TigerResearch/TigerBot.gitinstall git lfs：
git lfs installDownload weights from huggingface or modelscope
git clone https://huggingface.co/TigerResearch/tigerbot-13b-base-v2 git clone https://www.modelscope.cn/TigerResearch/tigerbot-13b-base-v2.gitRun infer script
python infer.py --model_path tigerbot-13b-base-v2 --model_type base --max_generate_length 64
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 52.11 |
| ARC (25-shot) | 53.84 |
| HellaSwag (10-shot) | 77.05 |
| MMLU (5-shot) | 53.57 |
| TruthfulQA (0-shot) | 44.06 |
| Winogrande (5-shot) | 74.98 |
| GSM8K (5-shot) | 17.06 |
| DROP (3-shot) | 44.21 |
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "TigerResearch/tigerbot-13b-base-v2" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TigerResearch/tigerbot-13b-base-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'