HarshaDiwakar
/

orange-problem-git-lora

@@ -1,199 +1,170 @@
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 ---
+license: apache-2.0
+base_model: microsoft/git-base
+tags:
+- multimodal
+- image-to-text
+- lora
+- transformers
+- ui-captioning
+datasets:
+- rootsautomation/RICO-Screen2Words
 ---
+# 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
+```
+---
+# 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
+---
+# 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.
+---
+# 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.
+---
+# Loading the Model
+To use the adapters, first load the base model and then load the LoRA adapters.
+```python
+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")
+```
+---
+# Merging LoRA Adapters
+The LoRA adapters can optionally be merged with the base model before inference.
+```python
+model = model.merge_and_unload()
+```
+This produces a standalone model equivalent to full fine-tuning.
+---
+# Example Inference
+```python
+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."
+```
+---
+# Requirements
+```
+transformers
+peft
+torch
+Pillow
+```
+Install dependencies:
+```
+pip install transformers peft torch pillow
+```
+---
+# Dataset
+The model was trained on:
+https://huggingface.co/datasets/rootsautomation/RICO-Screen2Words
+---
+# Limitations
+- Performance depends on the diversity of UI layouts present in the dataset.
+- The model may struggle with very complex or uncommon UI designs.
+---
+# Citation
+- RICO Dataset
+- Microsoft GIT model