How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ssweens/Kimi-VL-A3B-Thinking-2506-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": "ssweens/Kimi-VL-A3B-Thinking-2506-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/ssweens/Kimi-VL-A3B-Thinking-2506-GGUF:
Quick Links

GGUFs for moonshotai/Kimi-VL-A3B-Thinking-2506

Didn't see any GGUFs for this model, which is a legit model, so baked a couple. Hopefully useful to someone. Just straight llama-quantize off a BF16 convert_hf_to_gguf.py run. Sanity checked.

Downloads last month
60
GGUF
Model size
16B params
Architecture
deepseek2
Hardware compatibility
Log In to add your hardware

4-bit

6-bit

8-bit

16-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support

Model tree for ssweens/Kimi-VL-A3B-Thinking-2506-GGUF