dots_table

dots_table

This is a fine-tuned version of DotsOCR, optimized for document OCR tasks.

Model Details

• Base Model: DotsOCR (1.7B parameters)
• Training: LoRA fine-tuning with rank 48
• Task: Document text extraction and OCR
• Input: Document images
• Output: Extracted text in structured format

Usage

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

# Load model and processor
model = AutoModelForCausalLM.from_pretrained(
    "NirajRajai/dots_table",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="flash_attention_2"
)
processor = AutoProcessor.from_pretrained(
    "NirajRajai/dots_table",
    trust_remote_code=True
)

# Process image
image = Image.open("document.png")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Extract the text content from this image."}
        ]
    }
]

# Generate text
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print(output_text)

Training Details

• Hardware: NVIDIA H100 80GB
• Training Duration: 3 epochs
• Batch Size: 2 (with gradient accumulation)
• Learning Rate: 5e-5
• Optimizer: AdamW 8-bit

License

Apache 2.0

Citation

If you use this model, please cite the original DotsOCR paper and this fine-tuned version.