Instructions to use prithivMLmods/Garnet-OCR-3B-0422-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Garnet-OCR-3B-0422-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
Use Docker
docker model run hf.co/prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Garnet-OCR-3B-0422-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF:BF16
- SGLang
How to use prithivMLmods/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-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/Garnet-OCR-3B-0422-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
- Unsloth Desktop
- Docker Model Runner
How to use prithivMLmods/Garnet-OCR-3B-0422-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
- Lemonade
How to use prithivMLmods/Garnet-OCR-3B-0422-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Garnet-OCR-3B-0422-GGUF:BF16
Run and chat with the model
lemonade run user.Garnet-OCR-3B-0422-GGUF-BF16
List all available models
lemonade list
- Atomic Chat
license: apache-2.0
base_model:
- prithivMLmods/Garnet-OCR-3B-0422
tags:
- text-generation-inference
- Document
- VLM
- KIE
- VL
- Camel
- Openpdf
- Extraction
- Linking
- Markdown
- Document Digitization
- Intelligent Document Processing (IDP)
- Intelligent Word Recognition (IWR)
- pdf2markdown
- image-to-text
- ocr
- llama-cpp
library_name: transformers
language:
- en
pipeline_tag: image-text-to-text
datasets:
- prithivMLmods/OpenDoc-Pdf-Preview
- prithivMLmods/Opendoc1-Analysis-Recognition
- allenai/olmOCR-mix-0225
- prithivMLmods/Openpdf-Analysis-Recognition
- prithivMLmods/OCR-Markdown-Dense-200x
Garnet-OCR-3B-0422-GGUF
The Garnet-OCR-3B-0422 model is a fine-tuned and optimized evolution of Megalodon-OCR-Sync-0713, built on top of the Qwen2.5-VL-3B-Instruct architecture. This version is specifically designed for high-precision mathematical formula extraction, structured markdown generation, and accurate table reconstruction, making it highly effective for technical, scientific, and structured documents. Trained on an enhanced mixture of document-centric datasets, including large-scale OCR-caption pairs and structured document corpora, the model improves layout fidelity, symbolic reasoning, and content structuring across diverse document types such as research papers, scanned PDFs, handwritten equations, and analytical reports.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Garnet-OCR-3B-0422.BF16.gguf | BF16 | 6.8 GB | Download |
| Garnet-OCR-3B-0422.F16.gguf | F16 | 6.8 GB | Download |
| Garnet-OCR-3B-0422.F32.gguf | F32 | 13.6 GB | Download |
| Garnet-OCR-3B-0422.Q8_0.gguf | Q8_0 | 3.62 GB | Download |
| Garnet-OCR-3B-0422.mmproj-bf16.gguf | mmproj-bf16 | 1.34 GB | Download |
| Garnet-OCR-3B-0422.mmproj-f16.gguf | mmproj-f16 | 1.34 GB | Download |
| Garnet-OCR-3B-0422.mmproj-f32.gguf | mmproj-f32 | 2.67 GB | Download |
| Garnet-OCR-3B-0422.mmproj-q8_0.gguf | mmproj-q8_0 | 848 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):
