Instructions to use AMKCode/Qwen2.5-0.5B-q4f16_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMKCode/Qwen2.5-0.5B-q4f16_1-MLC with Transformers:

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

pipe = pipeline("text-generation", model="AMKCode/Qwen2.5-0.5B-q4f16_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("AMKCode/Qwen2.5-0.5B-q4f16_1-MLC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMKCode/Qwen2.5-0.5B-q4f16_1-MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMKCode/Qwen2.5-0.5B-q4f16_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": "AMKCode/Qwen2.5-0.5B-q4f16_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AMKCode/Qwen2.5-0.5B-q4f16_1-MLC

SGLang

How to use AMKCode/Qwen2.5-0.5B-q4f16_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 "AMKCode/Qwen2.5-0.5B-q4f16_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": "AMKCode/Qwen2.5-0.5B-q4f16_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 "AMKCode/Qwen2.5-0.5B-q4f16_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": "AMKCode/Qwen2.5-0.5B-q4f16_1-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AMKCode/Qwen2.5-0.5B-q4f16_1-MLC with Docker Model Runner:
```
docker model run hf.co/AMKCode/Qwen2.5-0.5B-q4f16_1-MLC
```

Qwen2.5-0.5B-q4f16_1-MLC

File size: 1,824 Bytes

---
base_model: Qwen/Qwen2.5-0.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlc-ai
- MLC-Weight-Conversion
---
---
library_name: mlc-llm
base_model: Qwen/Qwen2.5-0.5B
tags:
- mlc-llm
- web-llm
---

# AMKCode/Qwen2.5-0.5B-q4f16_1-MLC

This is the [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) model in MLC format `q4f16_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/AMKCode/Qwen2.5-0.5B-q4f16_1-MLC
```

### REST Server

In command line, run
```bash
mlc_llm serve HF://mlc-ai/AMKCode/Qwen2.5-0.5B-q4f16_1-MLC
```

### Python API

```python
from mlc_llm import MLCEngine

# Create engine
model = "HF://mlc-ai/AMKCode/Qwen2.5-0.5B-q4f16_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).