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 "SUSTech/SUS-Chat-72B" \
--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": "SUSTech/SUS-Chat-72B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
🐷SUS-Chat: Instruction tuning done right
not currently in use中文 ｜ English
🐷SUS-Chat: Instruction tuning done right
中文 ｜ English
News
2023-12-09: 🔥
Tigerbotvariant has been deleted,SUS-Chat-34Bis now the the top-ranked LLaMA model and the top-ranked chat model.2023-12-07: SUS-Chat-34B is now available on WiseModel🧠.
2023-12-06: Try SUS-Chat-34B chat-ui.
2023-12-05: SUS-Chat-34B is now available on ModelScope🤖
2023-12-05: SUS-Chat-34B is ranked 2nd in Open LLM leaderboard and surpassed all models under 70B.
2023-12-01: SUS-Chat-34B is now available on HuggingFace🤗.
Introduction
- Downloads last month
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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 "SUSTech/SUS-Chat-72B" \ --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": "SUSTech/SUS-Chat-72B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'