Orange Problem Git LoRA

GIT LoRA Fine-Tuned on RICO-Screen2Words

Model Description

This repository contains LoRA adapters for the GIT (Generative Image-to-Text Transformer) model, fine-tuned for UI screen caption generation.

The model generates natural language descriptions of mobile UI screenshots.

Instead of full fine-tuning, LoRA (Low-Rank Adaptation) is used to efficiently adapt the base model while training only a small number of parameters.

Base model:

microsoft/git-base

Dataset used:

rootsautomation/RICO-Screen2Words

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Intended Use

The model takes a mobile UI screenshot as input and generates a caption describing the interface.

Example use cases:

• UI documentation
• Accessibility tools
• Screen summarization
• Interface understanding

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Training Details

The model was fine-tuned using the RICO-Screen2Words dataset, which contains screenshots of mobile applications paired with human-written captions.

Training method:

• Parameter-efficient fine-tuning using LoRA
• Base model: microsoft/git-base
• Vision encoder: Vision Transformer
• Text decoder: Transformer language model

Training was designed to run on NVIDIA T4 GPUs using HuggingFace Transformers and PEFT.

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Files in This Repository

This repository contains the LoRA adapter weights:

adapter_config.json
adapter_model.safetensors

These adapters can be loaded on top of the base GIT model.

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Loading the Model

To use the adapters, first load the base model and then load the LoRA adapters.

from transformers import AutoProcessor, GitForCausalLM
from peft import PeftModel

base_model = GitForCausalLM.from_pretrained("microsoft/git-base")

model = PeftModel.from_pretrained(
    base_model,
    "HarshaDiwakar/orange-problem-git-lora"
)

processor = AutoProcessor.from_pretrained("microsoft/git-base")

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Merging LoRA Adapters

The LoRA adapters can optionally be merged with the base model before inference.

model = model.merge_and_unload()

This produces a standalone model equivalent to full fine-tuning.

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Example Inference

from PIL import Image

image = Image.open("example_ui.png")

inputs = processor(images=image, return_tensors="pt")

outputs = model.generate(**inputs)

caption = processor.batch_decode(outputs, skip_special_tokens=True)

print(caption)

Example output:

"This screen shows a shopping application with product listings and navigation tabs."

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Requirements

transformers
peft
torch
Pillow

Install dependencies:

pip install transformers peft torch pillow

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Dataset

The model was trained on:

https://huggingface.co/datasets/rootsautomation/RICO-Screen2Words

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Limitations

• Performance depends on the diversity of UI layouts present in the dataset.
• The model may struggle with very complex or uncommon UI designs.

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Citation

• RICO Dataset
• Microsoft GIT model