metadata
language:
- en
- es
tags:
- vision-language
- image-to-text
- ocr
- safety-moderation
license: apache-2.0
metrics:
- bleu
- meteor
- rouge
👁️ Valdam Vision 2.5
Valdam Vision 2.5 is a Multimodal Vision-Language Model (VLM) designed for scene description, spatial OCR, and Zero-Shot content moderation.
This release integrates visual patch projection from a pretrained Vision Transformer with a modular autoregressive Transformer Decoder.
🌟 Key Features
- Autoregressive Text Generation: Generates descriptions by applying cross-attention over visual patch embeddings.
- Zero-Shot Moderation (Valdam Shield): Content screening powered by CLIP ViT-L/14 semantic similarity matching.
- Spatial Layout OCR: Reconstructs read order and paragraphs from designs using spatial coordinates.
- Inference Latency: Optimized pipeline with an average response time of ~0.08s on standard hardware.
📦 Repository Structure
model.safetensors: Model weights in safe-tensors format containing header metadata.architecture.py: Model architecture definition (ViT Encoder & Transformer Decoder).inference.py: Script to initialize and run local predictions.config.json: Hyperparameters and model configuration.vocab.json: Mapping of vocabulary tokens.
🚀 Quickstart
from inference import ValdamVisionLocalPredictor
# Initialize the predictor
predictor = ValdamVisionLocalPredictor(
safetensors_weights_path="model.safetensors",
config_path="config.json",
vocab_path="vocab.json",
device="cuda"
)
# Run prediction on a preprocessed image tensor
result = predictor.predict(image_tensor)
print(f"Result: {result}")