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 "stas/tiny-random-llama-2" \
--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": "stas/tiny-random-llama-2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
This is a tiny random Llama model derived from "meta-llama/Llama-2-7b-hf".
See make_tiny_model.py for how this was done.
This is useful for functional testing (not quality generation, since its weights are random and the tokenizer has been shrunk to 3k items)
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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 "stas/tiny-random-llama-2" \ --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": "stas/tiny-random-llama-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'