How to use LayoutLM with Ollama or lMStudio

Leon Chase

• 3 min read

To use LayoutLM (or its variants like LayoutLMv2 or LayoutLMv3) with tools like Ollama or LM Studio, you need to understand that these models are multimodal and require both textual and visual inputs (e.g., bounding box coordinates, images). However, Ollama and LM Studio are primarily designed for running text-only large language models (LLMs) like Llama, Mistral, or GPT. These tools do not natively support multimodal models like LayoutLM because they lack the infrastructure to handle visual or layout data.

That said, there are ways to work around this limitation if you want to experiment with LayoutLM locally. Below is a step-by-step guide to downloading and using LayoutLM in a way that can be integrated into your workflow:

1. Understand the Limitations

  • Ollama and LM Studio: These tools are optimized for text-based LLMs and do not natively support multimodal models like LayoutLM.
  • LayoutLM: Requires additional inputs like bounding box coordinates, visual features, and tokenized text. You’ll need a custom setup to preprocess and feed these inputs into the model.

If you still want to proceed, you can use Hugging Face Transformers or other frameworks to load and run LayoutLM locally.

2. Download LayoutLM

Step 1: Install Required Libraries

You’ll need Python and the transformers library from Hugging Face. Install the required libraries:

pip install transformers torch torchvision

Step 2: Choose a LayoutLM Variant

There are multiple versions of LayoutLM:

  • LayoutLM: The original model.
  • LayoutLMv2: Improved version with better visual understanding.
  • LayoutLMv3: Latest version with enhanced performance.

For this guide, we’ll use LayoutLMv2 as an example.

Step 3: Load the Model

Use the transformers library to load the pre-trained LayoutLMv2 model and tokenizer:

from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification

# Load the processor and model
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
model = LayoutLMv2ForTokenClassification.from_pretrained("microsoft/layoutlmv2-base-uncased")

3. Prepare Input Data

LayoutLM requires text, bounding box coordinates, and optionally images as input. Here’s how to prepare the data:

Step 1: Extract Text and Bounding Boxes

You can use OCR tools like Tesseract or Pytesseract to extract text and bounding boxes from images or PDFs.

Example using Tesseract:

pip install pytesseract pillow

Python code to extract text and bounding boxes:

from PIL import Image
import pytesseract

# Load an image
image = Image.open("example.png")

# Use Tesseract to extract text and bounding boxes
data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)

# Extract words and bounding boxes
words = data["text"]
boxes = [[data["left"][i], data["top"][i], data["width"][i], data["height"][i]] for i in range(len(words))]

Step 2: Preprocess Inputs

Use the LayoutLM processor to tokenize the text and normalize the bounding boxes:

encoding = processor(
    image,
    words,
    boxes=boxes,
    return_tensors="pt"
)

4. Run Inference

Once the inputs are prepared, you can run inference using the LayoutLM model:

import torch

# Run inference
outputs = model(**encoding)

# Get predictions
predictions = torch.argmax(outputs.logits, dim=-1)
print(predictions)

5. Integrate with Ollama or LM Studio

Since Ollama and LM Studio do not natively support multimodal models like LayoutLM, you’ll need to use them indirectly by building a custom pipeline:

Option 1: Use Ollama/LM Studio for Post-Processing

  • Use LayoutLM to analyze the webpage or document layout.
  • Pass the extracted structured data (e.g., headers, buttons, text blocks) to Ollama or LM Studio for generating natural language descriptions.

Example:

# Generate a description using Ollama's API
import requests

description = "The webpage contains a header titled 'Welcome', a navigation bar with links to Home, About, and Contact, and a main content section with three paragraphs."

response = requests.post('http://localhost:11434/api/generate', json={
    "model": "llama2",
    "prompt": f"Describe the following layout: {description}"
})

print(response.json()["response"])

Option 2: Build a Custom GUI

  • Use a framework like Streamlit or Flask to create a GUI where users can upload documents or webpages.
  • Process the input with LayoutLM and display the results alongside outputs from Ollama or LM Studio.

6. Alternative: Use Docker for Multimodal Models

If you want a more robust solution, you can use Docker to containerize LayoutLM and other dependencies. This approach allows you to run LayoutLM in isolation while integrating it with Ollama or LM Studio via APIs.

Step 1: Create a Dockerfile

FROM python:3.9-slim

# Install dependencies
RUN pip install transformers torch torchvision pytesseract pillow flask

# Copy your app code
COPY app.py /app.py

# Expose the port
EXPOSE 5000

# Run the app
CMD ["python", "app.py"]

Step 2: Build and Run the Container

docker build -t layoutlm-app .
docker run --gpus all -p 5000:5000 layoutlm-app

Step 3: Access the API

You can now send requests to the LayoutLM API running in the Docker container.

7. Conclusion

While Ollama and LM Studio are not designed to run multimodal models like LayoutLM, you can still use LayoutLM locally by leveraging the Hugging Face Transformers library. To integrate LayoutLM with Ollama or LM Studio, you’ll need to build a custom pipeline that preprocesses the input data, runs LayoutLM for layout analysis, and then passes the results to Ollama or LM Studio for further processing.

Key takeaways:

  • Use Hugging Face Transformers to download and run LayoutLM locally.
  • Preprocess inputs (text, bounding boxes, images) before feeding them into LayoutLM.
  • Integrate LayoutLM with Ollama or LM Studio via APIs or custom pipelines.

By combining these tools, you can effectively describe webpage layouts or analyze documents while leveraging the strengths of both LayoutLM and text-based LLMs.