cstr/paddleocr-vl-0.9b-GGUF overview
PaddleOCR VL 0.9B — CrispEmbed GGUF CrispEmbed native GGUF quantizations of PaddlePaddle/PaddleOCR VL https://huggingface.co/PaddlePaddle/PaddleOCR VL . End to…
Runs locally from ~1.21 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
Model README
---
base_model: PaddlePaddle/PaddleOCR-VL
language:
- multilingual
license: apache-2.0
tags:
- gguf
- ocr
- document-understanding
- crispembed
- paddleocr
---
PaddleOCR-VL-0.9B — CrispEmbed GGUF
CrispEmbed-native GGUF quantizations of PaddlePaddle/PaddleOCR-VL.
End-to-end VLM-based OCR: text recognition, table extraction, formula recognition, chart understanding. 109 languages.
Files
| File | Size | Description |
|------|------|-------------|
| paddleocr-vl-0.9b-q4_k.gguf | 1.3 GB | 4-bit K-quant — smallest |
| paddleocr-vl-0.9b-q8_0.gguf | 1.4 GB | 8-bit quantization — recommended |
| paddleocr-vl-0.9b-f16.gguf | 2.3 GB | fp16 reference |
Model
- Architecture: NaViT-style ViT (27L, 1152d, SigLIP 2D RoPE + learned position embeddings)
+ Projector (pre-norm → 2×2 spatial merge → MLP)
+ ERNIE-4.5-0.3B LLM decoder (18L, 1024d, 16/2 GQA, MRoPE, SwiGLU)
- Parameters: ~0.9B total
- Languages: 109 (multilingual)
- Tasks: OCR, Table Recognition, Formula Recognition, Chart Recognition
- License: Apache 2.0
Usage with CrispEmbed
# OCR
./crispembed -m paddleocr-vl-0.9b-q8_0.gguf --ocr document.png
# With specific prompt
./crispembed -m paddleocr-vl-0.9b-q8_0.gguf --ocr-prompt "Table Recognition:" table.png
Conversion
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
python models/convert-paddleocr-vl-to-gguf.py \\
--model PaddlePaddle/PaddleOCR-VL \\
--output paddleocr-vl-0.9b-f16.gguf --dtype f16
./build/crispembed-quantize paddleocr-vl-0.9b-f16.gguf paddleocr-vl-0.9b-q8_0.gguf q8_0
License
Apache 2.0 — same as the base model.
Run cstr/paddleocr-vl-0.9b-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models