Qwen2-VL-OCR-2B-Instruct-GGUF

prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF

Qwen2-VL-OCR-2B-Instruct-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 Qwen2-VL-OCR-2B-Instruct model is a fine-tuned version of Qwen/Qwen2-VL-2B-Instruct, tailored for tasks that involve Optical Character Recognition (OCR), image-to-text conversion, math problem solving with LaTeX f…

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.gguf23.2 GB · 100%

At a glance

Task: Image-Text-to-Text
Library: transformers
License: apache-2.0
Model type: qwen2_vl
Access: Public
Created: May 15, 2025
Updated: May 15, 2025
SHA: 8814b608

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Base models

prithivMLmods/Qwen2-VL-OCR-2B-Instruct

Model card

From the Hugging Face model README

Qwen2-VL-OCR-2B-Instruct-GGUF [ VL / OCR ]

The Qwen2-VL-OCR-2B-Instruct model is a fine-tuned version of Qwen/Qwen2-VL-2B-Instruct, tailored for tasks that involve Optical Character Recognition (OCR), image-to-text conversion, math problem solving with LaTeX formatting and Messy Handwriting OCR. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.

Model Files (Qwen2-VL-OCR-2B-Instruct, GGUF)

File Name	Size	Quantization	Format	Description
`Qwen2-VL-OCR-2B-Instruct.f16.gguf`	3.09 GB	FP16	GGUF	Full precision (float16)
`Qwen2-VL-OCR-2B-Instruct.Q2_K.gguf`	676 MB	Q2_K	GGUF	2-bit quantized
`Qwen2-VL-OCR-2B-Instruct.Q3_K_L.gguf`	880 MB	Q3_K_L	GGUF	3-bit quantized (K L variant)
`Qwen2-VL-OCR-2B-Instruct.Q3_K_M.gguf`	824 MB	Q3_K_M	GGUF	3-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q3_K_S.gguf`	761 MB	Q3_K_S	GGUF	3-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q4_K_M.gguf`	986 MB	Q4_K_M	GGUF	4-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q4_K_S.gguf`	940 MB	Q4_K_S	GGUF	4-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q5_K_M.gguf`	1.13 GB	Q5_K_M	GGUF	5-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q5_K_S.gguf`	1.1 GB	Q5_K_S	GGUF	5-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q6_K.gguf`	1.27 GB	Q6_K	GGUF	6-bit quantized
`Qwen2-VL-OCR-2B-Instruct.Q8_0.gguf`	1.65 GB	Q8_0	GGUF	8-bit quantized

i1 Quantized Variants

File Name	Size	Quantization	Description
`Qwen2-VL-OCR-2B-Instruct.i1-IQ1_M.gguf`	464 MB	i1-IQ1_M	i1 1-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ1_S.gguf`	437 MB	i1-IQ1_S	i1 1-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_M.gguf`	601 MB	i1-IQ2_M	i1 2-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_S.gguf`	564 MB	i1-IQ2_S	i1 2-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_XS.gguf`	550 MB	i1-IQ2_XS	i1 2-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_XXS.gguf`	511 MB	i1-IQ2_XXS	i1 2-bit extra extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_M.gguf`	777 MB	i1-IQ3_M	i1 3-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_S.gguf`	762 MB	i1-IQ3_S	i1 3-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_XS.gguf`	732 MB	i1-IQ3_XS	i1 3-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_XXS.gguf`	669 MB	i1-IQ3_XXS	i1 3-bit extra extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ4_NL.gguf`	936 MB	i1-IQ4_NL	i1 4-bit with no-layernorm quantization
`Qwen2-VL-OCR-2B-Instruct.i1-IQ4_XS.gguf`	896 MB	i1-IQ4_XS	i1 4-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-Q4_0.gguf`	938 MB	i1-Q4_0	i1 4-bit traditional quant
`Qwen2-VL-OCR-2B-Instruct.i1-Q4_1.gguf`	1.02 GB	i1-Q4_1	i1 4-bit traditional variant

Metadata

File Name	Size	Description
`.gitattributes`	3.37 kB	Git LFS tracking file
`config.json`	34 B	Config placeholder
`README.md`	672 B	Model readme

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.4
GGUF	Q3_K_S	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	IQ4_XS	0.6
GGUF	Q4_K_S	0.6	fast, recommended
GGUF	Q4_K_M	0.6	fast, recommended
GGUF	Q5_K_S	0.6
GGUF	Q5_K_M	0.7
GGUF	Q6_K	0.7	very good quality
GGUF	Q8_0	0.9	fast, best quality
GGUF	f16	1.6	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Task: Image-Text-to-Text
Library: transformers
Type: qwen2_vl
License: apache-2.0
Languages: en
Created: May 15, 2025
Updated: May 15, 2025