---
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
datasets:
- vq_av2
model-index:
- name: paligemma_vqav2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# paligemma_vqav2

This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on a small chunk of vq_av2 dataset.
Fine-tuning code is [here](https://colab.research.google.com/drive/1x_OEphRK0H97DqqxEyiMewqsTiLD_Xmi?usp=sharing).

## 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/paligemma_vqav2"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained("google/paligemma-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: 8
- 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

### Training results



### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1