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---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/docscopeOCR-7B-050425-exp
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- document
- ocr
---
# **docscopeOCR-7B-050425-exp-GGUF**
> The **docscopeOCR-7B-050425-exp** model is a fine-tuned version of **Qwen/Qwen2.5-VL-7B-Instruct**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats.
## Model File
| File Name | Size | Format | Description |
|-----------------------------------------------|---------|----------------|----------------------------------------|
| docscopeOCR-7B-050425-exp.IQ4_XS.gguf | 4.25 GB | GGUF (IQ4_XS) | Int4 extra-small quantized model |
| docscopeOCR-7B-050425-exp.Q2_K.gguf | 3.02 GB | GGUF (Q2_K) | 2-bit quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_L.gguf | 4.09 GB | GGUF (Q3_K_L) | 3-bit large quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_M.gguf | 3.81 GB | GGUF (Q3_K_M) | 3-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_S.gguf | 3.49 GB | GGUF (Q3_K_S) | 3-bit small quantized model |
| docscopeOCR-7B-050425-exp.Q4_K_M.gguf | 4.68 GB | GGUF (Q4_K_M) | 4-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q5_K_M.gguf | 5.44 GB | GGUF (Q5_K_M) | 5-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q5_K_S.gguf | 5.32 GB | GGUF (Q5_K_S) | 5-bit small quantized model |
| docscopeOCR-7B-050425-exp.Q6_K.gguf | 6.25 GB | GGUF (Q6_K) | 6-bit quantized model |
| docscopeOCR-7B-050425-exp.Q8_0.gguf | 8.1 GB | GGUF (Q8_0) | 8-bit quantized model |
| config.json | 36 B | JSON | Configuration file |
| .gitattributes | 2.25 kB | Text | Git attributes configuration |
## 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):
 |