Instructions to use Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/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

docker model run hf.co/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC

SGLang

How to use Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC 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 "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?"
			}
		]
	}'

Docker Model Runner
How to use Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC with Docker Model Runner:
```
docker model run hf.co/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC
```

Qwen2.5-0.5B-Instruct-q3f16_1-MLC / README.md

Qurtana

Upload README.md with huggingface_hub

11b7446 verified almost 2 years ago

preview code

Raw History Blame Contribute Delete

1.91 kB

	---
	base_model: Qwen/Qwen2.5-0.5B-Instruct
	language:
	- en
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
	pipeline_tag: text-generation
	tags:
	- chat
	- mlc-ai
	- MLC-Weight-Conversion
	---
	---
	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](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model in MLC format `q3f16_1`.
	The conversion was done using the [MLC-Weight-Conversion](https://huggingface.co/spaces/mlc-ai/MLC-Weight-Conversion) space.
	The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm).

	## 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](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).

	### Chat

	In command line, run
	```bash
	mlc_llm chat HF://mlc-ai/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC
	```

	### REST Server

	In command line, run
	```bash
	mlc_llm serve HF://mlc-ai/Qurtana/Qwen2.5-0.5B-Instruct-q3f16_1-MLC
	```

	### Python API

	```python
	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](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).