README.md

---
language: dv
tags:
- vision
- image-to-text
- OCR
- Dhivehi
- PaliGemma2
- thaana
license: apache-2.0
datasets:
- alakxender/dhivehi-vrd-images
metrics:
- accuracy
base_model:
- google/paligemma2-3b-pt-224
library_name: transformers
---

# PaliGemma2 VRD Dhivehi OCR Model

## Model Description

This is a fine-tuned version of the PaliGemma2 model specifically optimized for Optical Character Recognition (OCR) of Dhivehi text in images. (ck-162810) 
The model is based on the `google/paligemma2-3b-pt-224` architecture and has been fine-tuned for improved performance in reading and transcribing Dhivehi text from images.

## Model Details

- **Model type:** Vision-Language Model
- **Base model:** google/paligemma2-3b-pt-224
- **Fine-tuning approach:** QLoRA
- **Input format:** Images with text
- **Output format:** Text transcription
- **Supported languages:** Primarily Dhivehi

## How to Use

### Option 1: Direct Loading

```python
from transformers.image_utils import load_image
import torch
from transformers import PaliGemmaForConditionalGeneration, AutoProcessor

# Print GPU information
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"Current GPU: {torch.cuda.get_device_name(0)}")
    print(f"GPU memory allocated: {torch.cuda.memory_allocated(0) / 1024**2:.2f} MB")
    print(f"GPU memory cached: {torch.cuda.memory_reserved(0) / 1024**2:.2f} MB")

model_id = "alakxender/paligemma2-qlora-dhivehi-ocr-224-sl-14k"
print("Loading model...")
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)

print("Loading image...")
image = load_image("ocr1.png")

print("Processing image...")
prompt = "What text is written in this image?"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to("cuda")
input_len = model_inputs["input_ids"].shape[-1]

print("Model inputs device:", model_inputs["input_ids"].device)
print("Model device:", model.device)
print("Generating output...")
with torch.inference_mode():
    generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
    generation = generation[0][input_len:]
    decoded = processor.decode(generation, skip_special_tokens=True)
    print(decoded)

print("Done!")
```

### Option 2: Memory-Efficient PEFT Loading

```python
from transformers.image_utils import load_image
import torch
from transformers import PaliGemmaForConditionalGeneration, AutoProcessor
from peft import PeftModel, PeftConfig

# Define model ID
model_id = "alakxender/paligemma2-qlora-dhivehi-ocr-224-sl-14k"

# Load the PEFT configuration to get the base model path
print("Loading PEFT configuration...")
peft_config = PeftConfig.from_pretrained(model_id)

# Load the base model
print(f"Loading base model: {peft_config.base_model_name_or_path}...")
base_model = PaliGemmaForConditionalGeneration.from_pretrained(
    peft_config.base_model_name_or_path,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Load the adapter on top of the base model
print(f"Loading PEFT adapter: {model_id}...")
model = PeftModel.from_pretrained(base_model, model_id)

# Load the processor from the base model
processor = AutoProcessor.from_pretrained(peft_config.base_model_name_or_path)

print("Loading image...")
image = load_image("ocr1.png")

print("Processing image...")
prompt = "What text is written in this image?"
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16)

# Move inputs to the same device as the model
if hasattr(model, 'device'):
    device = model.device
else:
    # If device isn't directly accessible, infer from model parameters
    device = next(model.parameters()).device
    
model_inputs = {k: v.to(device) for k, v in model_inputs.items()}

input_len = model_inputs["input_ids"].shape[-1]
# Process without printing device information

print("Generating output...")
with torch.inference_mode():
    generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
    generation = generation[0][input_len:]
    decoded = processor.decode(generation, skip_special_tokens=True)
    print(decoded)

print("Done!")
```

## Training Details

- **Base Model:** google/paligemma2-3b-pt-224
- **Dataset:** alakxender/dhivehi-vrd-mix-img-questions
- **Training Configuration:**
  - Batch size: 2 per device
  - Gradient accumulation steps: 8
  - Effective batch size: 16
  - Learning rate: 2e-5
  - Weight decay: 1e-6
  - Adam β2: 0.999
  - Warmup steps: 2
  - Training steps: 20,000
  - Epochs: 1
  - Mixed precision: bfloat16

- **QLoRA Configuration:**
  - Quantization: 4-bit NF4
  - LoRA rank (r): 8
  - Target modules: 
    - q_proj
    - k_proj
    - v_proj
    - o_proj
    - gate_proj
    - up_proj
    - down_proj
  - Task type: CAUSAL_LM
  - Optimizer: paged_adamw_8bit

- **Data Processing:**
  - Image resize method: LANCZOS
  - Input format: RGB images
  - Text prompt format: "answer [question]"

- **Training Metrics:**
  - Initial loss: ~15
  - Final loss: ~2
  - Learning rate: Decreasing from 1.5e-5 to 5e-6
  - Gradient norm: Stabilized around 20-60
  - Model checkpointing: Every 1000 steps
  - Logging frequency: Every 100 steps

## Performance

The model showed consistent improvement during training:
- Loss decreased significantly in the first 5k steps and stabilized afterwards
- Gradient norms remained in a healthy range throughout training
- Learning rate was automatically adjusted following a linear decay schedule
- Training completed successfully with convergence in loss metrics
- Training progress was monitored using Weights & Biases

## Model Architecture

This model uses Parameter-Efficient Fine-Tuning (PEFT) with QLoRA:
- **Quantization:** 4-bit quantization for memory efficiency
- **LoRA Adaptation:** Low-rank adaptation of key transformer components
- **Memory Optimization:** Uses paged optimizer for efficient memory usage
- **Mixed Precision:** bfloat16 for training stability and speed

## Limitations

- Primarily optimized for Dhivehi text
- Performance may vary with different image qualities and text styles
- May or may not perform optimally on handwritten text

## Dataset

- **Source Dataset:** alakxender/dhivehi-vrd-images (mixed)
- **Processed Dataset:** alakxender/dhivehi-vrd-b1-img-questions
- **Dataset Size:**
  - Total: 1M+ samples

- **Question Types:**
The dataset uses a variety of question prompts for OCR tasks, including:
```
- "What text is written in this image?"
- "Can you read and transcribe the Dhivehi text shown in this image?"
- "What is the Dhivehi text visible in this image?"
- "Please read out the text content from this image"
- "What Dhivehi text can you see in this image?"
- "Is there any text visible in this image? If so, what does it say?"
- "Could you transcribe the Dhivehi text shown in this image?"
- "What does the text in this image say?"
- "Can you read the Dhivehi text in this image? What does it say?"
- "Please identify and transcribe any text visible in this image"
- "What Dhivehi text is present in this image?"
```

- **Dataset Format:**
  - Features:
    - `image`: Image containing Dhivehi text
    - `question`: Randomly selected question from the question pool
    - `answer`: Ground truth Dhivehi text transcription
  - Processing: Memory-efficient chunked processing (10,000 samples per chunk)