---
license: apache-2.0
base_model: DotsOCR
tags:
- vision
- ocr
- document-understanding
- text-extraction
datasets:
- custom
language:
- en
pipeline_tag: image-to-text
---

# 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

```python
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.