| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: image-text-to-text |
| library_name: transformers |
| tags: |
| - text-generation-inference |
| - OCR |
| - VLM |
| --- |
| |
| > [!important] |
| This is the OCR weight component of the [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) model. These weights cannot be used for other use cases. If you wish to do so, please visit the original model page! |
|
|
| > This repository directly exposes the OCR-only weights for smoother transformers implementation of the PaddleOCR-VL model. |
|
|
| > [!note] |
| Last updated: 4:00 AM (IST), October 25, 2025. |
|
|
| > [!note] |
| The latest transformers version used as of the above date is `transformers==4.57.1` and the torch version `2.8.0+cu126` |
|
|
| ## Quick Start with Transformers |
|
|
| > #### Install the required packages |
|
|
| ```py |
| !pip install transformers torch torchvision gradio hf_xet \ |
| huggingface_hub pillow accelerate peft \ |
| matplotlib requests einops av sentencepiece\ |
| transformers-stream-generator |
| ``` |
|
|
| > ### notebook login |
|
|
| ```py |
| from huggingface_hub import notebook_login, HfApi |
| notebook_login() |
| ``` |
|
|
| > ### Run [app.py] |
|
|
| ```py |
| import os |
| import sys |
| from threading import Thread |
| from typing import Iterable |
| |
| import gradio as gr |
| import torch |
| from PIL import Image |
| from transformers import ( |
| AutoModelForCausalLM, |
| AutoProcessor, |
| TextIteratorStreamer, |
| ) |
| |
| from gradio.themes import Soft |
| from gradio.themes.utils import colors, fonts, sizes |
| |
| # --- Theme and CSS Setup --- |
| colors.steel_blue = colors.Color( |
| name="steel_blue", |
| c50="#EBF3F8", |
| c100="#D3E5F0", |
| c200="#A8CCE1", |
| c300="#7DB3D2", |
| c400="#529AC3", |
| c500="#4682B4", |
| c600="#3E72A0", |
| c700="#36638C", |
| c800="#2E5378", |
| c900="#264364", |
| c950="#1E3450", |
| ) |
| |
| class SteelBlueTheme(Soft): |
| def __init__( |
| self, |
| *, |
| primary_hue: colors.Color | str = colors.gray, |
| secondary_hue: colors.Color | str = colors.steel_blue, |
| neutral_hue: colors.Color | str = colors.slate, |
| text_size: sizes.Size | str = sizes.text_lg, |
| font: fonts.Font | str | Iterable[fonts.Font | str] = ( |
| fonts.GoogleFont("Outfit"), "Arial", "sans-serif", |
| ), |
| font_mono: fonts.Font | str | Iterable[fonts.Font | str] = ( |
| fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace", |
| ), |
| ): |
| super().__init__( |
| primary_hue=primary_hue, |
| secondary_hue=secondary_hue, |
| neutral_hue=neutral_hue, |
| text_size=text_size, |
| font=font, |
| font_mono=font_mono, |
| ) |
| super().set( |
| background_fill_primary="*primary_50", |
| background_fill_primary_dark="*primary_900", |
| body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)", |
| body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)", |
| button_primary_text_color="white", |
| button_primary_text_color_hover="white", |
| button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)", |
| button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)", |
| button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)", |
| button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)", |
| slider_color="*secondary_500", |
| slider_color_dark="*secondary_600", |
| block_title_text_weight="600", |
| block_border_width="3px", |
| block_shadow="*shadow_drop_lg", |
| button_primary_shadow="*shadow_drop_lg", |
| button_large_padding="11px", |
| color_accent_soft="*primary_100", |
| block_label_background_fill="*primary_200", |
| ) |
| |
| steel_blue_theme = SteelBlueTheme() |
| |
| css = """ |
| #main-title h1 { |
| font-size: 2.3em !important; |
| } |
| #output-title h2 { |
| font-size: 2.1em !important; |
| } |
| """ |
| |
| # --- Model Configuration and Loading --- |
| MAX_MAX_NEW_TOKENS = 4096 |
| DEFAULT_MAX_NEW_TOKENS = 2048 |
| MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
| |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
| |
| # Load PaddleOCR |
| MODEL_ID_P = "strangervisionhf/paddle.ocr_path_expose" # -> Original model: https://huggingface.co/PaddlePaddle/PaddleOCR-VL |
| processor = AutoProcessor.from_pretrained(MODEL_ID_P, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| MODEL_ID_P, |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16 |
| ).to(device).eval() |
| |
| # --- Generation Function --- |
| def generate_image(text: str, image: Image.Image, |
| max_new_tokens: int = 1024, |
| temperature: float = 0.6, |
| top_p: float = 0.9, |
| top_k: int = 50, |
| repetition_penalty: float = 1.2): |
| """Generate responses for image input using the PaddleOCR model.""" |
| if image is None: |
| yield "Please upload an image.", "Please upload an image." |
| return |
| |
| images = [image.convert("RGB")] |
| |
| # PaddleOCR has a specific message format |
| messages = [ |
| {"role": "user", "content": text} |
| ] |
| |
| prompt = processor.apply_chat_template(messages, add_generation_prompt=True) |
| inputs = processor(text=prompt, images=images, return_tensors="pt").to(device) |
| |
| streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) |
| generation_kwargs = { |
| **inputs, |
| "streamer": streamer, |
| "max_new_tokens": max_new_tokens, |
| "temperature": temperature, |
| "top_p": top_p, |
| "top_k": top_k, |
| "repetition_penalty": repetition_penalty, |
| "do_sample": True |
| } |
| thread = Thread(target=model.generate, kwargs=generation_kwargs) |
| thread.start() |
| |
| buffer = "" |
| for new_text in streamer: |
| buffer += new_text.replace("<|im_end|>", "").replace("<end_of_utterance>", "") |
| yield buffer, buffer |
| |
| |
| with gr.Blocks(css=css, theme=steel_blue_theme) as demo: |
| gr.Markdown("# **Paddle OCR Only**", elem_id="main-title") |
| with gr.Row(): |
| with gr.Column(scale=2): |
| image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...") |
| image_upload = gr.Image(type="pil", label="Upload Image", height=320) |
| image_submit = gr.Button("Submit", variant="primary") |
| |
| with gr.Accordion("Advanced options", open=False): |
| max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS) |
| temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6) |
| top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9) |
| top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50) |
| repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2) |
| |
| with gr.Column(scale=3): |
| gr.Markdown("## Output", elem_id="output-title") |
| raw_output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True) |
| with gr.Accordion("[Result.md]", open=False): |
| formatted_output = gr.Markdown(label="Formatted Result") |
| |
| gr.Markdown("Note: Currently, PaddleOCR VL only supports OCR inference. Structured OCR document parsing transformer inference is coming soon. [Report – Bug/Issue](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR3/discussions/1)") |
| |
| image_submit.click( |
| fn=generate_image, |
| inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty], |
| outputs=[raw_output, formatted_output] |
| ) |
| |
| if __name__ == "__main__": |
| demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True) |
| ``` |
