dhivehi-nougat-base

DHIVEHI NOUGAT BASE (IMAGE-TO-TEXT)

This model is a fine-tuned version of facebook/nougat-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0142

Model description

Finetuned dhivehi on text-image dataset, config all

Usage

from PIL import Image
import torch
from transformers import NougatProcessor, VisionEncoderDecoderModel
from pathlib import Path

# Load the model and processor
processor = NougatProcessor.from_pretrained("alakxender/dhivehi-nougat-base")
model = VisionEncoderDecoderModel.from_pretrained(
    "alakxender/dhivehi-nougat-base",  
    torch_dtype=torch.bfloat16,                 # Optional: Load the model with BF16 data type for faster inference and lower memory usage
    attn_implementation={                       # Optional: Specify the attention kernel implementations for different parts of the model
        "decoder": "flash_attention_2",         # Use FlashAttention-2 for the decoder for improved performance
        "encoder": "eager"                      # Use the default ("eager") attention implementation for the encoder
    }
)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

context_length = 128

def predict(img_path):
    # Ensure image is in RGB format
    image = Image.open(img_path).convert("RGB")  
    pixel_values = processor(image, return_tensors="pt").pixel_values.to(torch.bfloat16)

    # generate prediction
    outputs = model.generate(
        pixel_values.to(device),
        min_length=1,
        max_new_tokens=context_length,
        repetition_penalty=1.5,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        eos_token_id=processor.tokenizer.eos_token_id,
    )

    page_sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
    return page_sequence

print(predict("DV01-04_31.jpg"))

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 3
• eval_batch_size: 3
• seed: 42
• gradient_accumulation_steps: 6
• total_train_batch_size: 18
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Framework versions

• Transformers 4.47.0
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0