Update README.md

README.md

@@ -1,107 +1,103 @@
----
-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
-import os
-import traceback
-
-# This assumes the utility script is available in your environment
-from qwen_vl_utils import process_vision_info
-
-# Replace with your Hugging Face username
-MODEL_ID = "[YOUR-HF-USERNAME]/dots.ocr-4bit"
-
-print("Loading 4-bit quantized model from the Hub...")
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID,
-    device_map="auto",
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16, 
-)
-processor = AutoProcessor.from_pretrained(
-    MODEL_ID,
-    trust_remote_code=True
-)
-print("✅ Model and processor loaded successfully!")
-
-# --- Inference ---
-image_path = "demo/demo_image1.jpg" # Make sure you have this image
-image = Image.open(image_path)
-prompt_text = "Parse all layout info, both detection and recognition"
-
-messages = [
-    {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": prompt_text}]}
-]
-
-# Prepare inputs using the official workflow
-text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-image_inputs, _ = process_vision_info(messages)
-inputs = processor(
-    text=[text], images=image_inputs, padding=True, return_tensors="pt"
-).to(model.device)
-
-# Generate with parameters to prevent looping with the 4-bit model
-generated_ids = model.generate(
-    **inputs, max_new_tokens=4096, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.15
-)
-
-# Trim and decode output
-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("\n--- Inference Result ---")
-print(output_text)
-```
-
-## License
-
+---
+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
+import os
+import traceback
+
+# This assumes the utility script is available in your environment
+from qwen_vl_utils import process_vision_info
+
+# Replace with your Hugging Face username
+MODEL_ID = "[YOUR-HF-USERNAME]/dots.ocr-4bit"
+
+print("Loading 4-bit quantized model from the Hub...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16, 
+)
+processor = AutoProcessor.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True
+)
+print("✅ Model and processor loaded successfully!")
+
+# --- Inference ---
+image_path = "demo/demo_image1.jpg" # Make sure you have this image
+image = Image.open(image_path)
+prompt_text = "Parse all layout info, both detection and recognition"
+
+messages = [
+    {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": prompt_text}]}
+]
+
+# Prepare inputs using the official workflow
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+image_inputs, _ = process_vision_info(messages)
+inputs = processor(
+    text=[text], images=image_inputs, padding=True, return_tensors="pt"
+).to(model.device)
+
+# Generate with parameters to prevent looping with the 4-bit model
+generated_ids = model.generate(
+    **inputs, max_new_tokens=4096, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.15
+)
+
+# Trim and decode output
+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("\n--- Inference Result ---")
+print(output_text)
+```
+
+## License
+
 This model is released under the MIT License, same as the original model.