300 lines
11 KiB
Markdown
300 lines
11 KiB
Markdown
---
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license: mit
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library_name: transformers
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language:
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- en
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pipeline_tag: image-text-to-text
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tags:
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- text-generation-inference
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- OCR
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- VLM
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- Markdown
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- pytorch
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new_version: prithivMLmods/Dots.OCR-Latest-BF16
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---
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> [!warning]
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This version is experimental. Please refer to the newer versions pinned above to avoid any complexities.👆👆👆
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> [!IMPORTANT]
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> This is a copy of the model weights from the [https://huggingface.co/rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) model. These weights cannot be used for other purposes. If you wish to do so, please visit the original model page.
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Previously, inference with the model [[https://huggingface.co/rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)] would fail with the following error:
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**Error loading dots-ocr model: Received a NoneType for argument 'video_processor', but a BaseVideoProcessor was expected.** in the latest Transformers versions.
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This page, which includes the model weights and corrected configuration, fixed the issue and allowed Transformers inference to run smoothly.
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> [!note]
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Last updated: 5:00 AM (IST), October 25, 2025.
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> [!note]
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A PR to fix the issue has been raised on the original model page **[PR:38]**: [huggingface.co/rednote-hilab/dots.ocr/discussions/38](https://huggingface.co/rednote-hilab/dots.ocr/discussions/38)
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> [!note]
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The latest transformers version used as of the above date is `transformers==4.57.1` and the torch version `2.8.0+cu126`
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## Quick Start with Transformers
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> #### Install the required packages
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```py
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!pip install transformers torch torchvision gradio hf_xet \
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huggingface_hub pillow accelerate peft \
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matplotlib requests einops av sentencepiece\
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transformers-stream-generator
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```
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```py
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flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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```
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- FlashAttention requires L4 or higher GPUs [This includes GPUs like the A100, RTX 3090, RTX 4090, H100, etc...].
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> ### notebook login
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```py
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from huggingface_hub import notebook_login, HfApi
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notebook_login()
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```
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> ### Run [app.py]
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```py
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import os
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import sys
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from threading import Thread
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from typing import Iterable
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from huggingface_hub import snapshot_download
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import gradio as gr
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import spaces
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import torch
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from PIL import Image
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from transformers import (
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AutoModelForCausalLM,
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AutoProcessor,
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TextIteratorStreamer,
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)
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from gradio.themes import Soft
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from gradio.themes.utils import colors, fonts, sizes
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# --- Theme and CSS Setup ---
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colors.steel_blue = colors.Color(
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name="steel_blue",
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c50="#EBF3F8",
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c100="#D3E5F0",
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c200="#A8CCE1",
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c300="#7DB3D2",
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c400="#529AC3",
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c500="#4682B4",
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c600="#3E72A0",
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c700="#36638C",
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c800="#2E5378",
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c900="#264364",
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c950="#1E3450",
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)
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class SteelBlueTheme(Soft):
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def __init__(
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self,
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*,
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primary_hue: colors.Color | str = colors.gray,
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secondary_hue: colors.Color | str = colors.steel_blue,
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neutral_hue: colors.Color | str = colors.slate,
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text_size: sizes.Size | str = sizes.text_lg,
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font: fonts.Font | str | Iterable[fonts.Font | str] = (
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fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
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),
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font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
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fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
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),
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):
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super().__init__(
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primary_hue=primary_hue,
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secondary_hue=secondary_hue,
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neutral_hue=neutral_hue,
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text_size=text_size,
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font=font,
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font_mono=font_mono,
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)
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super().set(
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background_fill_primary="*primary_50",
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background_fill_primary_dark="*primary_900",
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body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
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body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
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button_primary_text_color="white",
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button_primary_text_color_hover="white",
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button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
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button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
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button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
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button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
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slider_color="*secondary_500",
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slider_color_dark="*secondary_600",
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block_title_text_weight="600",
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block_border_width="3px",
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block_shadow="*shadow_drop_lg",
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button_primary_shadow="*shadow_drop_lg",
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button_large_padding="11px",
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color_accent_soft="*primary_100",
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block_label_background_fill="*primary_200",
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)
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steel_blue_theme = SteelBlueTheme()
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css = """
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#main-title h1 {
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font-size: 2.3em !important;
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}
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#output-title h2 {
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font-size: 2.1em !important;
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}
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"""
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MAX_MAX_NEW_TOKENS = 4096
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DEFAULT_MAX_NEW_TOKENS = 2048
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Load Dots.OCR from the local, patched directory
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MODEL_PATH_D = "strangervisionhf/dots.ocr-base-fix"
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processor = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_PATH_D,
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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).eval()
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# --- Generation Function ---
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@spaces.GPU
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def generate_image(text: str, image: Image.Image,
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max_new_tokens: int = 1024,
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temperature: float = 0.6,
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top_p: float = 0.9,
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top_k: int = 50,
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repetition_penalty: float = 1.2):
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"""Generate responses for image input using the Dots.OCR model."""
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if image is None:
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yield "Please upload an image.", "Please upload an image."
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return
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images = [image.convert("RGB")]
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messages = [
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{
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"role": "user",
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"content": [{"type": "image"}] + [{"type": "text", "text": text}]
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}
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]
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prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = {
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**inputs,
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"streamer": streamer,
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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"top_p": top_p,
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"top_k": top_k,
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"repetition_penalty": repetition_penalty,
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"do_sample": True
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}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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buffer = ""
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for new_text in streamer:
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buffer += new_text.replace("<|im_end|>", "")
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yield buffer, buffer
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with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
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gr.Markdown("# **dots.ocr-base-fix**", elem_id="main-title")
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gr.Markdown("Powered by `Dots.OCR`")
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with gr.Row():
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with gr.Column(scale=2):
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image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
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image_upload = gr.Image(type="pil", label="Upload Image", height=320)
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image_submit = gr.Button("Submit", variant="primary")
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with gr.Accordion("Advanced options", open=False):
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max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
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temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
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top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
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top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
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repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
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with gr.Column(scale=3):
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gr.Markdown("## Output", elem_id="output-title")
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raw_output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True)
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with gr.Accordion("[Result.md]", open=False):
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formatted_output = gr.Markdown(label="Formatted Result")
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gr.Markdown("[Report any Bug/Issue here](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR3/discussions/1)")
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image_submit.click(
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fn=generate_image,
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inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
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outputs=[raw_output, formatted_output]
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)
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if __name__ == "__main__":
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demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)
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```
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## Implementation Example
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## If you intend to run dots.ocr with the original model path, implement and fix the issue through code-side actions.
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```py
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CACHE_PATH = "./model_cache"
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if not os.path.exists(CACHE_PATH):
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os.makedirs(CACHE_PATH)
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model_path_d_local = snapshot_download(
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repo_id='rednote-hilab/dots.ocr',
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local_dir=os.path.join(CACHE_PATH, 'dots.ocr'),
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max_workers=20,
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local_dir_use_symlinks=False
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)
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config_file_path = os.path.join(model_path_d_local, "configuration_dots.py")
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if os.path.exists(config_file_path):
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with open(config_file_path, 'r') as f:
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input_code = f.read()
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lines = input_code.splitlines()
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if "class DotsVLProcessor" in input_code and not any("attributes = " in line for line in lines):
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output_lines = []
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for line in lines:
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output_lines.append(line)
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if line.strip().startswith("class DotsVLProcessor"):
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output_lines.append(" attributes = [\"image_processor\", \"tokenizer\"]")
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with open(config_file_path, 'w') as f:
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f.write('\n'.join(output_lines))
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print("Patched configuration_dots.py successfully.")
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sys.path.append(model_path_d_local)
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# Load Dots.OCR from the local, patched directory
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MODEL_PATH_D = model_path_d_local
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processor_d = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
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model_d = AutoModelForCausalLM.from_pretrained(
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MODEL_PATH_D,
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attn_implementation="flash_attention_2",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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).eval()
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``` |