How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstackorg/Qwen-3.5-upscaled-14B-noft-q8_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstackorg/Qwen-3.5-upscaled-14B-noft-q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'
Use Docker
docker model run hf.co/Pinkstackorg/Qwen-3.5-upscaled-14B-noft-q8_0-GGUF:F32
Quick Links

q8_0 gguf - tested.

This model is a ~14B parameter upscale of Qwen/Qwen3.5-9B. It has been merged with the base model to boost knowledge while keeping instruction following, because the base model was actually trained with some instruct-like examples.

You should fine-tune this model for it to be fully useable, while it has potential and all capabilities are still intact including vision, it currently faces looping issues, lower quality reasoning/COT. Due to mergekit not supporting qwen3.5, we used a custom layer-wise merge method.

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GGUF
Model size
14B params
Architecture
qwen35
Hardware compatibility
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8-bit

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