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---
license: gemma
library_name: peft
tags:
- generated_from_trainer
base_model: google/paligemma-3b-pt-224
model-index:
- name: paligemma_VQAMed
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_VQAMed2019
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the [VQAMed 2019](https://zenodo.org/records/10499039) dataset.
Fine-tuning code is [here](https://colab.research.google.com/github/mahmoudBidry/Finetune-Google-Paligemma-3B-VQA/blob/main/Fine_tune_PaliGemma_on_VQAMed2019_dataset.ipynb).
## How to use
To use the model, follow the [colab notebook](https://colab.research.google.com/drive/1SfrNNHE32k9kBWdR6U0DQr4LI_AVIAb1?usp=sharing).
Below is a quick example.
To ensure you have the latest version of Transformers, install it using the following command:
```bash
!pip install -qU git+https://github.com/huggingface/transformers.git
```
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
import torch
from PIL import Image
import requests
processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
model = PaliGemmaForConditionalGeneration.from_pretrained("MahmoudRox/Paligemma_VQAMED2019")
prompt = "Which part of the body is in the picture?" #your question
image_file = "https://prod-images-static.radiopaedia.org/images/9289883/1c20962e46c92ee83a3f551adb24fa_big_gallery.jpg" #your image
raw_image = Image.open(requests.get(image_file, stream=True).raw)
def generate_response(prompt, image):
inputs = processor(images=image, text=prompt, return_tensors="pt")
# Check if the attention mask needs to be inverted
attention_mask = inputs['attention_mask']
if torch.max(attention_mask) == 1:
attention_mask = 1 - attention_mask
# Generate a response
outputs = model.generate(
input_ids=inputs['input_ids'],
attention_mask=attention_mask,
pixel_values=inputs['pixel_values'],
max_new_tokens=1,
no_repeat_ngram_size=2
)
# Decode and print the response
decoded_response = processor.decode(outputs[0], skip_special_tokens=True)[len(prompt):]
return decoded_response
print(generate_response(prompt, raw_image))
#spine
```
### 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
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |