- Local Apps Settings
- vLLM
How to use quant-mind/Spark-X2.5-4B-W4A16-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quant-mind/Spark-X2.5-4B-W4A16-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "quant-mind/Spark-X2.5-4B-W4A16-AutoRound",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/quant-mind/Spark-X2.5-4B-W4A16-AutoRound
- SGLang
How to use quant-mind/Spark-X2.5-4B-W4A16-AutoRound 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 "quant-mind/Spark-X2.5-4B-W4A16-AutoRound" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "quant-mind/Spark-X2.5-4B-W4A16-AutoRound",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "quant-mind/Spark-X2.5-4B-W4A16-AutoRound" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "quant-mind/Spark-X2.5-4B-W4A16-AutoRound",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Docker Model Runner
How to use quant-mind/Spark-X2.5-4B-W4A16-AutoRound with Docker Model Runner:
docker model run hf.co/quant-mind/Spark-X2.5-4B-W4A16-AutoRound
