vLLM How to use TitanML/Qwen-7B-Chat-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitanML/Qwen-7B-Chat-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TitanML/Qwen-7B-Chat-Int4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/TitanML/Qwen-7B-Chat-Int4
SGLang How to use TitanML/Qwen-7B-Chat-Int4 with SGLang:
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "TitanML/Qwen-7B-Chat-Int4" \
--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": "TitanML/Qwen-7B-Chat-Int4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use 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 "TitanML/Qwen-7B-Chat-Int4" \
--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": "TitanML/Qwen-7B-Chat-Int4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' 
