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prithivMLmods/Garnet-OCR-3B-0422-GGUF
Garnet-OCR-3B-0422-GGUF is a image-text-to-text model from prithivMLmods. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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 mathem…
Downloads · 30 days
281
10% of all-time downloads
All-time downloads
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.gguf37 GB · 100%
From the Hugging Face model README
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.
| 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 |
(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):
