How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/docscopeOCR-7B-050425-exp-GGUF",
	filename="",
)
llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

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):

image.png

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