---
base_model: google/paligemma2-3b-pt-448
library_name: peft
---

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# PaliGemma2-3b-VQAv2

This model is a fine-tuned version of [google/paligemma2-3b-pt-448](https://huggingface.co./google/paligemma2-3b-pt-448) on half of the VQAv2 validation split, for task conditioning.
Fine-tuning script is [here](https://github.com/merveenoyan/smol-vision/blob/main/paligemma.py) which also comes in notebook form [here](https://github.com/merveenoyan/smol-vision/blob/main/Fine_tune_PaliGemma.ipynb).
Make sure you install transformers in main branch before using this or running fine-tuning.

## How to Use

Below is the code to use this model. Also see [inference notebook](https://colab.research.google.com/drive/100IQcvMvGm9y--oelbLfI__eHCoz5Ser?usp=sharing).

```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
import requests

model_id = "merve/paligemma2-3b-vqav2"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained("google/paligemma2-3b-pt-224")

prompt = "What is behind the cat?"
image_file = "https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/cat.png?download=true"
raw_image = Image.open(requests.get(image_file, stream=True).raw)

inputs = processor(prompt, raw_image.convert("RGB"), return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)

print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])
# gramophone
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2

### Framework versions

- Transformers (main as of Dec 5)
- PEFT 0.13.2