PaddleOCR-VL-1.5-ONNX – AI Model by lbm364dl | AlphaNeural AI

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lbm364dl/
PaddleOCR-VL-1.5-ONNX

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transformers.js

image-text-to-text

table-recognition

vision-language

PaddlePaddle/PaddleOCR-VL-1.5

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PaddleOCR-VL-1.5 ONNX (Q4 Quantized)

ONNX conversion of PaddlePaddle/PaddleOCR-VL-1.5 for browser deployment with Transformers.js.

Model Details

Base model: PaddleOCR-VL-1.5 (0.9B parameters)
Quantization: INT4 (MatMulNBits, 32-block symmetric)
Total size: ~991 MB (down from 3.4 GB FP32)
License: Apache 2.0
Table recognition accuracy: 92.76% TEDS on PubTabNet

Files

File	Size	Description
`onnx/embed_tokens.onnx`	405 MB	Token embedding lookup
`onnx/vision_encoder.onnx`	369 MB	NaViT vision encoder + MLP projector
`onnx/decoder_model_merged.onnx`	217 MB	Ernie4.5 LLM decoder with KV cache

Architecture

The model is split into 3 ONNX sub-models following the Florence-2 pattern:

embed_tokens: Converts token IDs to embeddings
vision_encoder: Processes image patches into visual tokens (NaViT + projector MLP)
decoder_model_merged: Autoregressive text decoder with KV cache support

Key features:

M-RoPE (3D multimodal rotary position embeddings)
GQA (16 attention heads, 2 KV heads, head_dim=128)
Merged decoder supporting both prefill and generation via use_cache_branch

Validation

All components validated against PyTorch reference:

Component	FP32 Cosine Sim	Q4 Cosine Sim
embed_tokens	1.000000	1.000000
vision_encoder	1.000000	0.960662
decoder (prefill)	1.000000	0.970726
decoder (with past)	1.000000	-

Usage

Intended for browser-based OCR and table recognition via ONNX Runtime Web (WebGPU/WASM).

Credits