Instructions to use CognitiveKernel/Qwen3-8B-CK-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CognitiveKernel/Qwen3-8B-CK-Pro with Transformers:

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

pipe = pipeline("text-generation", model="CognitiveKernel/Qwen3-8B-CK-Pro")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CognitiveKernel/Qwen3-8B-CK-Pro", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CognitiveKernel/Qwen3-8B-CK-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CognitiveKernel/Qwen3-8B-CK-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CognitiveKernel/Qwen3-8B-CK-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CognitiveKernel/Qwen3-8B-CK-Pro

SGLang

How to use CognitiveKernel/Qwen3-8B-CK-Pro 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 "CognitiveKernel/Qwen3-8B-CK-Pro" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CognitiveKernel/Qwen3-8B-CK-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "CognitiveKernel/Qwen3-8B-CK-Pro" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CognitiveKernel/Qwen3-8B-CK-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CognitiveKernel/Qwen3-8B-CK-Pro with Docker Model Runner:
```
docker model run hf.co/CognitiveKernel/Qwen3-8B-CK-Pro
```

Qwen3-8B-CK-Pro / .gitattributes

CognitiveKernel

Rename README.md to readme.md

578bfe3 verified about 1 year ago

Download

History Blame Contribute Delete

1.6 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: dcf1e7ee47369a792fd3b4895f416ed32c17dae2be69afc028415a3940921ba6
Size of remote file:: 1.6 kB
SHA256:: 2a78bd63a1da77ce42e9a76e0cff59e6a5957b1935f20e40a8df34d4fc2c07c6

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.