Instructions to use prithivMLmods/OneThinker-8B-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/OneThinker-8B-AIO-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/OneThinker-8B-AIO-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/OneThinker-8B-AIO-GGUF", filename="OneThinker-8B.BF16.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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with 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:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
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:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
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:BF16 # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
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:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
Use Docker
docker model run hf.co/prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/OneThinker-8B-AIO-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": "prithivMLmods/OneThinker-8B-AIO-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/prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
- SGLang
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/OneThinker-8B-AIO-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/OneThinker-8B-AIO-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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/OneThinker-8B-AIO-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/OneThinker-8B-AIO-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" } } ] } ] }' - Ollama
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Ollama:
ollama run hf.co/prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
- Unsloth Studio new
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for prithivMLmods/OneThinker-8B-AIO-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for prithivMLmods/OneThinker-8B-AIO-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for prithivMLmods/OneThinker-8B-AIO-GGUF to start chatting
- Pi new
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/OneThinker-8B-AIO-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
Run Hermes
hermes
- Docker Model Runner
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
- Lemonade
How to use prithivMLmods/OneThinker-8B-AIO-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/OneThinker-8B-AIO-GGUF:BF16
Run and chat with the model
lemonade run user.OneThinker-8B-AIO-GGUF-BF16
List all available models
lemonade list
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: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):
- Downloads last month
- 80
8-bit
16-bit
32-bit

Install from brew
# 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: