This version is experimental. Please refer to the newer versions pinned above to avoid any complexities.👆👆👆

This is a copy of the model weights from the 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.

Previously, inference with the model [https://huggingface.co/rednote-hilab/dots.ocr] would fail with the following error: Error loading dots-ocr model: Received a NoneType for argument 'video_processor', but a BaseVideoProcessor was expected. in the latest Transformers versions.

This page, which includes the model weights and corrected configuration, fixed the issue and allowed Transformers inference to run smoothly.

Last updated: 5:00 AM (IST), October 25, 2025.

A PR to fix the issue has been raised on the original model page [PR:38]: huggingface.co/rednote-hilab/dots.ocr/discussions/38

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

!pip install transformers torch torchvision gradio hf_xet \
 huggingface_hub pillow accelerate peft \
 matplotlib requests einops av sentencepiece\
 transformers-stream-generator

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

• FlashAttention requires L4 or higher GPUs [This includes GPUs like the A100, RTX 3090, RTX 4090, H100, etc...].

notebook login

from huggingface_hub import notebook_login, HfApi
notebook_login()

Run [app.py]

import os
import sys
from threading import Thread
from typing import Iterable
from huggingface_hub import snapshot_download

import gradio as gr
import spaces
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;
}
"""

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 Dots.OCR from the local, patched directory
MODEL_PATH_D = "strangervisionhf/dots.ocr-base-fix"
processor = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
 MODEL_PATH_D,
 attn_implementation="flash_attention_2",
 torch_dtype=torch.bfloat16,
 device_map="auto",
 trust_remote_code=True
).eval()


# --- Generation Function ---
@spaces.GPU
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 Dots.OCR model."""
 if image is None:
 yield "Please upload an image.", "Please upload an image."
 return

 images = [image.convert("RGB")]
 messages = [
 {
 "role": "user",
 "content": [{"type": "image"}] + [{"type": "text", "text": 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|>", "")
 yield buffer, buffer

with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
 gr.Markdown("# **dots.ocr-base-fix**", elem_id="main-title")
 gr.Markdown("Powered by `Dots.OCR`")
 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("[Report any Bug/Issue here](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)

Implementation Example

👁 Screenshot 2025-10-25 at 05-09-35 Multimodal OCR3 - a Hugging Face Space by prithivMLmods

👁 Screenshot 2025-10-25 at 05-09-47 Multimodal OCR3 - a Hugging Face Space by prithivMLmods

If you intend to run dots.ocr with the original model path, implement and fix the issue through code-side actions.

CACHE_PATH = "./model_cache"
if not os.path.exists(CACHE_PATH):
 os.makedirs(CACHE_PATH)


model_path_d_local = snapshot_download(
 repo_id='rednote-hilab/dots.ocr',
 local_dir=os.path.join(CACHE_PATH, 'dots.ocr'),
 max_workers=20,
 local_dir_use_symlinks=False
)

config_file_path = os.path.join(model_path_d_local, "configuration_dots.py")

if os.path.exists(config_file_path):
 with open(config_file_path, 'r') as f:
 input_code = f.read()

 lines = input_code.splitlines()
 if "class DotsVLProcessor" in input_code and not any("attributes = " in line for line in lines):
 output_lines = []
 for line in lines:
 output_lines.append(line)
 if line.strip().startswith("class DotsVLProcessor"):
 output_lines.append(" attributes = [\"image_processor\", \"tokenizer\"]")

 with open(config_file_path, 'w') as f:
 f.write('\n'.join(output_lines))
 print("Patched configuration_dots.py successfully.")

sys.path.append(model_path_d_local)


# Load Dots.OCR from the local, patched directory
MODEL_PATH_D = model_path_d_local
processor_d = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
model_d = AutoModelForCausalLM.from_pretrained(
 MODEL_PATH_D,
 attn_implementation="flash_attention_2",
 torch_dtype=torch.bfloat16,
 device_map="auto",
 trust_remote_code=True
).eval()