strangervisionhf/excess_layer_pruned-nanonets-1.5b

This is the OCR weight component of the Nanonets-OCR2-1.5B-exp model. These weights cannot be used for other use cases. If you wish to do so, please visit the original model page!

Previously, inference with the model [https://huggingface.co/nanonets/Nanonets-OCR2-1.5B-exp] would fail with the error: ValueError: 'num_hidden_layers' (16) must be equal to the number of layer types (28).

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

Last updated: 4:00 AM (IST), October 25, 2025.

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

!pip install flash-attn==2.7.3 --no-build-isolation

• FlashAttention requires L4 or higher GPUs.

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

import gradio as gr
import torch
from PIL import Image
from transformers import (
    AutoModelForImageTextToText,
    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 strangervisionhf/excess_layer_pruned-nanonets-1.5b
MODEL_ID = "strangervisionhf/excess_layer_pruned-nanonets-1.5b"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2"
).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 selected 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|>", "").replace("<end_of_utterance>", "")
        yield buffer, buffer

with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
    gr.Markdown("# **nanonets-1.5b**", 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")
        
    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, debug=True)