🚀 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. It's designed for tasks such as Optical Character Recognition (OCR), image - to - text conversion, solving math problems with LaTeX formatting, and Messy Handwriting OCR. This model combines a conversational approach with visual and textual understanding to handle multi - modal tasks effectively.
✨ Features
- Tailored for OCR, image - to - text conversion, math problem solving, and Messy Handwriting OCR.
- Integrates conversational, visual, and textual understanding for multi - modal tasks.
📚 Documentation
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):

📄 License
This project is licensed under the Apache - 2.0 license.