How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:
Use Docker
docker model run hf.co/prithivMLmods/OneThinker-8B-AIO-GGUF:
Quick Links

OneThinker-8B-AIO-GGUF

The OneThinker-8B model, including the OneThinker-SFT-Qwen3-8B variant, is an all-in-one reasoning model for images and videos based on the Qwen-3-VL-Instruct-8B architecture, optimized for comprehensive multimodal reasoning tasks such as image question answering, video understanding, grounding, tracking, and segmentation. It achieves top-tier performance across a wide range of benchmarks including 70.6% accuracy on MMMU for image QA and strong video QA results, surpassing other leading open-source models like Qwen3-VL-Instruct-8B. The model uses a unified text format to integrate diverse reasoning tasks, enabling advanced STEM, general knowledge, and multimodal reasoning capabilities. It supports large context lengths and has been trained with extensive GPU resources, making it a powerful tool for visual and video reasoning tasks with state-of-the-art results in multimedia understanding and question answering.

OneThinker-8B [GGUF]

File Name Quant Type File Size File Link
OneThinker-8B.BF16.gguf BF16 16.4 GB Download
OneThinker-8B.F32.gguf F32 32.8 GB Download
OneThinker-8B.Q8_0.gguf Q8_0 8.71 GB Download
OneThinker-8B.mmproj-bf16.gguf mmproj-bf16 1.16 GB Download
OneThinker-8B.mmproj-f32.gguf mmproj-f32 2.31 GB Download
OneThinker-8B.mmproj-q8_0.gguf mmproj-q8_0 752 MB Download

OneThinker-SFT-Qwen3-8B [GGUF]

File Name Quant Type File Size File Link
OneThinker-SFT-Qwen3-8B.BF16.gguf BF16 16.4 GB Download
OneThinker-SFT-Qwen3-8B.F32.gguf F32 32.8 GB Download
OneThinker-SFT-Qwen3-8B.Q8_0.gguf Q8_0 8.71 GB Download
OneThinker-SFT-Qwen3-8B.mmproj-bf16.gguf mmproj-bf16 1.16 GB Download
OneThinker-SFT-Qwen3-8B.mmproj-f32.gguf mmproj-f32 2.31 GB Download
OneThinker-SFT-Qwen3-8B.mmproj-q8_0.gguf mmproj-q8_0 752 MB Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

Downloads last month
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GGUF
Model size
8B params
Architecture
qwen3vl
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