Update README.md

README.md

@@ -1,3 +1,53 @@
+---
+license: mit
+library_name: transformers
+tags:
+- dots_ocr
+- image-to-text
+- ocr
+- document-parse
+- layout
+- table
+- formula
+- quantized
+- 4-bit
+base_model: rednote-hilab/dots.ocr
+---
+
+<div align="center">
+    <img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/logo.png" width="300"/>
+</div>
+
+# dots.ocr-4bit: A 4-bit Quantized Version
+
+This repository contains a 4-bit quantized version of the powerful `dots.ocr` model by the **Rednote HiLab**. The quantization was performed using `bitsandbytes` (NF4 precision), providing significant memory and speed improvements with minimal performance loss, making this state-of-the-art model accessible on consumer-grade GPUs.
+
+This work is entirely a derivative of the original model. All credit for the model architecture, training, and groundbreaking research goes to the original authors. A huge thank you to them for open-sourcing their work.
+
+*   **Original Model:** [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)
+*   **Original GitHub:** [https://github.com/rednote-hilab/dots.ocr](https://github.com/rednote-hilab/dots.ocr)
+*   **Live Demo (Original):** [https://dotsocr.xiaohongshu.com](https://dotsocr.xiaohongshu.com)
+
+## Model Description (from original authors)
+> **dots.ocr** is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance.
+
+## How to Use This 4-bit Version
+
+First, ensure you have the necessary dependencies installed. Because this model uses custom code, you **must** clone the original repository and install it.
+
+```bash
+# It's recommended to clone the original repo to get all utility scripts
+git clone https://github.com/rednote-hilab/dots.ocr.git
+cd dots.ocr
+
+# Install the custom code and dependencies
+pip install -e .
+pip install torch transformers accelerate bitsandbytes peft sentencepiece
+```
+
+You can then use the 4-bit model with the following Python script. Note the inclusion of generation parameters (repetition_penalty, do_sample, etc.), which are recommended to prevent potential looping with the quantized model.
+
+```python
 import torch
 from transformers import AutoModelForCausalLM, AutoProcessor
 from PIL import Image
@@ -41,4 +91,9 @@ generated_ids = model.generate(**inputs, max_new_tokens=256, do_sample=True, tem
 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)
+print(output_text)
+```
+
+## License
+
+This model is released under the MIT License, same as the original model.