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prithivMLmods/Gliese-OCR-7B-Post2.0-final-GGUF

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
1likes2.2kdownloads
Model Card

Gliese-OCR-7B-Post2.0-final-GGUF

The Gliese-OCR-7B-Post2.0-final model is a refined and optimized version of Gliese-OCR-7B-Post1.0, built upon the Qwen2.5-VL architecture. It represents the final iteration in the Gliese-OCR series, offering enhanced efficiency, precision, and visualization capabilities for document OCR, visual analysis, and information extraction. Fine-tuned with extended document visualization data and OCR-focused objectives, this model delivers superior accuracy across a wide range of document types, including scanned PDFs, handwritten pages, structured forms, and analytical reports

Model Files

File NameQuant TypeFile Size
Gliese-OCR-7B-Post2.0-final.f16.ggufF1615.2 GB
Gliese-OCR-7B-Post2.0-final.Q2_K.ggufQ2_K3.02 GB
Gliese-OCR-7B-Post2.0-final.Q3KL.ggufQ3KL4.09 GB
Gliese-OCR-7B-Post2.0-final.Q3KM.ggufQ3KM3.81 GB
Gliese-OCR-7B-Post2.0-final.Q3KS.ggufQ3KS3.49 GB
Gliese-OCR-7B-Post2.0-final.Q4KM.ggufQ4KM4.68 GB
Gliese-OCR-7B-Post2.0-final.Q4KS.ggufQ4KS4.46 GB
Gliese-OCR-7B-Post2.0-final.Q5KM.ggufQ5KM5.44 GB
Gliese-OCR-7B-Post2.0-final.Q5KS.ggufQ5KS5.32 GB
Gliese-OCR-7B-Post2.0-final.Q6_K.ggufQ6_K6.25 GB
Gliese-OCR-7B-Post2.0-final.Q8_0.ggufQ8_08.1 GB
Gliese-OCR-7B-Post2.0-final.IQ4_XS.ggufIQ4_XS4.25 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ1_M.ggufi1-IQ1_M2.04 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ1_S.ggufi1-IQ1_S1.9 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ2_M.ggufi1-IQ2_M2.78 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ2_S.ggufi1-IQ2_S2.6 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ2_XS.ggufi1-IQ2_XS2.47 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ2_XXS.ggufi1-IQ2_XXS2.27 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ3_M.ggufi1-IQ3_M3.57 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ3_S.ggufi1-IQ3_S3.5 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ3_XS.ggufi1-IQ3_XS3.35 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ3_XXS.ggufi1-IQ3_XXS3.11 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ4_NL.ggufi1-IQ4_NL4.44 GB
Gliese-OCR-7B-Post2.0-final.i1-IQ4_XS.ggufi1-IQ4_XS4.22 GB
Gliese-OCR-7B-Post2.0-final.i1-Q2_K.ggufi1-Q2_K3.02 GB
Gliese-OCR-7B-Post2.0-final.i1-Q2KS.ggufi1-Q2KS2.83 GB
Gliese-OCR-7B-Post2.0-final.i1-Q3KL.ggufi1-Q3KL4.09 GB
Gliese-OCR-7B-Post2.0-final.i1-Q3KM.ggufi1-Q3KM3.81 GB
Gliese-OCR-7B-Post2.0-final.i1-Q3KS.ggufi1-Q3KS3.49 GB
Gliese-OCR-7B-Post2.0-final.i1-Q4_0.ggufi1-Q4_04.44 GB
Gliese-OCR-7B-Post2.0-final.i1-Q4_1.ggufi1-Q4_14.87 GB
Gliese-OCR-7B-Post2.0-final.i1-Q4KM.ggufi1-Q4KM4.68 GB
Gliese-OCR-7B-Post2.0-final.i1-Q4KS.ggufi1-Q4KS4.46 GB
Gliese-OCR-7B-Post2.0-final.i1-Q5KM.ggufi1-Q5KM5.44 GB
Gliese-OCR-7B-Post2.0-final.i1-Q5KS.ggufi1-Q5KS5.32 GB
Gliese-OCR-7B-Post2.0-final.i1-Q6_K.ggufi1-Q6_K6.25 GB
Gliese-OCR-7B-Post2.0-final.imatrix.ggufimatrix4.56 MB
Gliese-OCR-7B-Post2.0-final.mmproj-Q8_0.ggufmmproj-Q8_0856 MB
Gliese-OCR-7B-Post2.0-final.mmproj-f16.ggufmmproj-f161.35 GB

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):

image.png