Hugging Face's logo

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

Image-Text-to-Text
Transformers
GGUF
English
qwen2_vl
text-generation-inference
VQA
Messy Handwriting OCR
OCR
code
Model card Files
xet
Community
2
Qwen2-VL-OCR-2B-Instruct-GGUF / README.md
prithivMLmods's picture
prithivMLmods
Update README.md
8814b60 verified
preview code
|
raw
history blame contribute delete
6.16 kB
metadata
license: apache-2.0
language:
  - en
base_model:
  - prithivMLmods/Qwen2-VL-OCR-2B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - text-generation-inference
  - VQA
  - Messy Handwriting OCR
  - OCR
  - code

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):

image.png