from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
from PIL import Image
import torch

# Load the specialized OMDA architecture
model = AutoModelForCausalLM.from_pretrained("BINOMDA/OMDA-PROMPTER", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("BINOMDA/OMDA-PROMPTER")
processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") # The vision processor is for SigLIP

# Generate description
image = Image.open("your-image.jpg").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(model.device)
generated_ids = model.generate(pixel_values, max_new_tokens=800, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id)
description = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(description)

Training Details

Family Name: OMDA (Optical Multi-modal Description Architecture)
Developer: BINOMDA
Vision Backbone: SigLIP (Frozen)[cite: 1]
Language Decoder: OMDA-GPT2 Hybrid[cite: 1]
Architecture Type: Multimodal Cross-Attention[cite: 1]

Training Data: Curated dataset of images with detailed descriptions

Max Sequence Length: 2048 tokens

Training Epochs: 5

Learning Rate: 2e-5