How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="roleplaiapp/Omni-Reasoner-2B-Q8_0-GGUF",
	filename="omni-reasoner-2b-q8_0.gguf",
)
llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

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

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GGUF
Model size
2B params
Architecture
qwen2vl
Hardware compatibility
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8-bit

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