Downloads · 30 days
292
11% of all-time downloads
prithivMLmods/Garnet-OCR-7B-Post-0422-GGUF
Garnet-OCR-7B-Post-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-7B-0422 model is a refined and highly optimized evolution of Gliese-OCR-7B-Post2.0-final, built upon the Qwen2.5-VL architecture. This release focuses on high-precision mathematical formula extraction,…
Downloads · 30 days
292
11% of all-time downloads
All-time downloads
2.8K
Public
Repo size
74 GB
Likes
1
Public
Click a slice to open those files.
.gguf75.3 GB · 100%
From the Hugging Face model README
The Garnet-OCR-7B-0422 model is a refined and highly optimized evolution of Gliese-OCR-7B-Post2.0-final, built upon the Qwen2.5-VL architecture. This release focuses on high-precision mathematical formula extraction, structured markdown generation, and accurate table reconstruction, making it especially effective for technical and scientific documents. Fine-tuned with enhanced datasets targeting mathematical notation, document structure, and layout fidelity, the model delivers superior performance across complex documents including research papers, scanned PDFs, handwritten equations, structured forms, and analytical reports.
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Garnet-OCR-7B-0422.BF16.gguf | BF16 | 15.2 GB | Download |
| Garnet-OCR-7B-0422.F16.gguf | F16 | 15.2 GB | Download |
| Garnet-OCR-7B-0422.F32.gguf | F32 | 30.5 GB | Download |
| Garnet-OCR-7B-0422.Q8_0.gguf | Q8_0 | 8.1 GB | Download |
| Garnet-OCR-7B-0422.mmproj-bf16.gguf | mmproj-bf16 | 1.36 GB | Download |
| Garnet-OCR-7B-0422.mmproj-f16.gguf | mmproj-f16 | 1.36 GB | Download |
| Garnet-OCR-7B-0422.mmproj-f32.gguf | mmproj-f32 | 2.71 GB | Download |
| Garnet-OCR-7B-0422.mmproj-q8_0.gguf | mmproj-q8_0 | 856 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):
