How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roleplaiapp/Omni-Reasoner-2B-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": "roleplaiapp/Omni-Reasoner-2B-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/roleplaiapp/Omni-Reasoner-2B-Q8_0-GGUF:Q8_0
Quick Links

roleplaiapp/Omni-Reasoner-2B-Q8_0-GGUF

Repo: roleplaiapp/Omni-Reasoner-2B-Q8_0-GGUF
Original Model: Omni-Reasoner-o1 Organization: prithivMLmods Quantized File: omni-reasoner-2b-q8_0.gguf Quantization: GGUF Quantization Method: Q8_0
Use Imatrix: False
Split Model: False

Overview

This is an GGUF Q8_0 quantized version of Omni-Reasoner-o1.

Quantization By

I often have idle A100 GPUs while building/testing and training the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai

Downloads last month
5
GGUF
Model size
2B params
Architecture
qwen2vl
Hardware compatibility
Log In to add your hardware

8-bit

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

Model tree for roleplaiapp/Omni-Reasoner-2B-Q8_0-GGUF

Base model

Qwen/Qwen2-VL-2B
Quantized
(53)
this model