How to use from
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 "Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC" \
    --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": "Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC",
		"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 "Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC" \
        --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": "Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

library_name: mlc-llm base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - mlc-llm - web-llm

Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC

This is the Qwen2.5-0.5B-Instruct model in MLC format q3f16_1. The conversion was done using the MLC-Weight-Conversion space. The model can be used for projects MLC-LLM and WebLLM.

Example Usage

Here are some examples of using this model in MLC LLM. Before running the examples, please install MLC LLM by following the installation documentation.

Chat

In command line, run

mlc_llm chat HF://mlc-ai/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC

REST Server

In command line, run

mlc_llm serve HF://mlc-ai/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC

Python API

from mlc_llm import MLCEngine

# Create engine
model = "HF://mlc-ai/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC"
engine = MLCEngine(model)

# Run chat completion in OpenAI API.
for response in engine.chat.completions.create(
    messages=[{"role": "user", "content": "What is the meaning of life?"}],
    model=model,
    stream=True,
):
    for choice in response.choices:
        print(choice.delta.content, end="", flush=True)
print("\n")

engine.terminate()

Documentation

For more information on MLC LLM project, please visit our documentation and GitHub repo.

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