Instructions to use Edentns/Worktro-S2-q0f16-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edentns/Worktro-S2-q0f16-MLC with Transformers:

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

pipe = pipeline("text-generation", model="Edentns/Worktro-S2-q0f16-MLC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Edentns/Worktro-S2-q0f16-MLC", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Edentns/Worktro-S2-q0f16-MLC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edentns/Worktro-S2-q0f16-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": "Edentns/Worktro-S2-q0f16-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edentns/Worktro-S2-q0f16-MLC

SGLang

How to use Edentns/Worktro-S2-q0f16-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 "Edentns/Worktro-S2-q0f16-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": "Edentns/Worktro-S2-q0f16-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 "Edentns/Worktro-S2-q0f16-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": "Edentns/Worktro-S2-q0f16-MLC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edentns/Worktro-S2-q0f16-MLC with Docker Model Runner:
```
docker model run hf.co/Edentns/Worktro-S2-q0f16-MLC
```

Worktro-S2-q0f16-MLC

모델 설명 (Model Description)

Worktro-S2-q0f16-MLC 모델은 Worktro-S2 모델을 MLC-LLM 라이브러리에서 동작할 수 있도록 컨버팅된 소형 언어 모델(sLM)입니다. 자세한 내용은 Worktro-S2 모델 설명을 참고해주세요.

The Worktro-S2-q0f16-MLC model is a small language model (sLM) that has been converted from the Worktro-S2 model to operate within the MLC-LLM library. For more detailed information, please refer to the description of the Worktro-S2 model.

라이선스 (License)

이 모델은 cc-by-nc-4.0 라이선스 하에 제공됩니다. 이 라이선스는 다른 사람들이 비상업적 용도 및 연구 목적으로 모델을 공유하고 수정할 수 있도록 허용합니다.

This model is provided under the cc-by-nc-4.0 license. This license allows others to share and adapt the model for non-commercial purposes and research use.

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