Update README.md

README.md

@@ -1,49 +1,3 @@
----
-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
----
-
-# 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
@@ -55,8 +9,8 @@ MODEL_ID = "helizac/dots.ocr-4bit"
 
 local_model_path = snapshot_download(repo_id=MODEL_ID)
 
-model = AutoModelForCausalLM.from_pretrained(local_model_path, device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained(local_model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(local_model_path, device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
+processor = AutoProcessor.from_pretrained(local_model_path, trust_remote_code=True, use_fast=True)
 
 image_path = "test.jpg"
 image = Image.open(image_path)
@@ -82,14 +36,9 @@ text = processor.apply_chat_template(messages, tokenize=False, add_generation_pr
 image_inputs, _ = process_vision_info(messages)
 inputs = processor(text=[text], images=image_inputs, padding=True, return_tensors="pt").to(model.device)
 
-generated_ids = model.generate(**inputs, max_new_tokens=1048, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.15)
+generated_ids = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.15)
 
 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)
-```
-
-## License
-
-This model is released under the MIT License, same as the original model.
+print(output_text)