Adding `transformers` as a library, and also mentioning the `custom_code` tag

Hey team!

This is an insanely visible model, and the community loves it. I wanted to tag it properly so that the visibility increases and we can have better tracking of number of downloads and usage.

README.md

@@ -9,6 +9,8 @@ tags:
 - layout
 - table
 - formula
+- transformers
+- custom_code
 language:
 - en
 - zh
@@ -49,6 +51,85 @@ dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
 4.  **Efficient and Fast Performance:** Built upon a compact 1.7B LLM, **dots.ocr** provides faster inference speeds than many other high-performing models based on larger foundations.
 
 
+## Usage with transformers
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
+from qwen_vl_utils import process_vision_info
+from dots_ocr.utils import dict_promptmode_to_prompt
+
+model_path = "./weights/DotsOCR"
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    attn_implementation="flash_attention_2",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+
+image_path = "demo/demo_image1.jpg"
+prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
+
+1. Bbox format: [x1, y1, x2, y2]
+
+2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
+
+3. Text Extraction & Formatting Rules:
+    - Picture: For the 'Picture' category, the text field should be omitted.
+    - Formula: Format its text as LaTeX.
+    - Table: Format its text as HTML.
+    - All Others (Text, Title, etc.): Format their text as Markdown.
+
+4. Constraints:
+    - The output text must be the original text from the image, with no translation.
+    - All layout elements must be sorted according to human reading order.
+
+5. Final Output: The entire output must be a single JSON object.
+"""
+
+messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path
+                },
+                {"type": "text", "text": prompt}
+            ]
+        }
+    ]
+
+# Preparation for inference
+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",
+)
+
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=24000)
+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
+)
+print(output_text)
+```
+
 ### Performance Comparison: dots.ocr vs. Competing Models
 <img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/chart.png" border="0" />
 
@@ -1231,4 +1312,4 @@ We also thank [DocLayNet](https://github.com/DS4SD/DocLayNet), [M6Doc](https://g
 - **Performance Bottleneck:** Despite its 1.7B parameter LLM foundation, **dots.ocr** is not yet optimized for high-throughput processing of large PDF volumes. 
 
 We are committed to achieving more accurate table and formula parsing, as well as enhancing the model's OCR capabilities for broader generalization, all while aiming for **a more powerful, more efficient model**. Furthermore, we are actively considering the development of **a more general-purpose perception model** based on Vision-Language Models (VLMs), which would integrate general detection, image captioning, and OCR tasks into a unified framework. **Parsing the content of the pictures in the documents** is also a key priority for our future work.
-We believe that collaboration is the key to tackling these exciting challenges. If you are passionate about advancing the frontiers of document intelligence and are interested in contributing to these future endeavors, we would love to hear from you. Please reach out to us via email at: [[email protected]].
+We believe that collaboration is the key to tackling these exciting challenges. If you are passionate about advancing the frontiers of document intelligence and are interested in contributing to these future endeavors, we would love to hear from you. Please reach out to us via email at: [[email protected]].