Instructions to use prithivMLmods/GutenOCR-3B-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/GutenOCR-3B-AIO-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/GutenOCR-3B-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/GutenOCR-3B-AIO-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/GutenOCR-3B-AIO-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
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/GutenOCR-3B-AIO-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
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/GutenOCR-3B-AIO-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/GutenOCR-3B-AIO-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-AIO-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-AIO-GGUF with Ollama:
ollama run hf.co/prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-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/GutenOCR-3B-AIO-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use prithivMLmods/GutenOCR-3B-AIO-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/GutenOCR-3B-AIO-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/GutenOCR-3B-AIO-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GutenOCR-3B-AIO-GGUF-Q4_K_M
List all available models
lemonade list
GutenOCR-3B-AIO-GGUF
GutenOCR-3B from rootsautomation is a 3B-parameter grounded OCR vision-language model fine-tuned from Qwen2.5-VL-3B, part of the GutenOCR family designed as a unified front-end for full-page reading, text detection, and grounding through prompt-specified input-output schemas, supporting line- and paragraph-level bounding boxes plus conditional "where is x?" queries on business documents, scientific articles, and synthetic data. It more than doubles the composite grounded OCR score of its Qwen2.5-VL backbone (0.348→0.811) across 10.5K held-out pages, substantially improving region-/line-level CER and text-detection F1 on Fox/OmniDocBench v1.5 benchmarks while revealing trade-offs in page linearization, color-guided OCR, and formula-heavy layouts. The single-checkpoint VLM integrates reading/detection/grounding seamlessly for business workflows and human-in-the-loop verification, with GGUF quantizations (Q4_K_S/M recommended at 1.9-2.0GB) available for efficient deployment.
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):
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