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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: google/paligemma-3b-pt-224 |
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model-index: |
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- name: paligemma_VQAMed |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# paligemma_VQAMed2019 |
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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. |
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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). |
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## How to use |
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To use the model, follow the [colab notebook](https://colab.research.google.com/drive/1SfrNNHE32k9kBWdR6U0DQr4LI_AVIAb1?usp=sharing). |
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Below is a quick example. |
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To ensure you have the latest version of Transformers, install it using the following command: |
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```bash |
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!pip install -qU git+https://github.com/huggingface/transformers.git |
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``` |
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```python |
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from transformers import AutoProcessor, PaliGemmaForConditionalGeneration |
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import torch |
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from PIL import Image |
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import requests |
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processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224") |
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model = PaliGemmaForConditionalGeneration.from_pretrained("MahmoudRox/Paligemma_VQAMED2019") |
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prompt = "Which part of the body is in the picture?" #your question |
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image_file = "https://prod-images-static.radiopaedia.org/images/9289883/1c20962e46c92ee83a3f551adb24fa_big_gallery.jpg" #your image |
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raw_image = Image.open(requests.get(image_file, stream=True).raw) |
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def generate_response(prompt, image): |
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inputs = processor(images=image, text=prompt, return_tensors="pt") |
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# Check if the attention mask needs to be inverted |
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attention_mask = inputs['attention_mask'] |
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if torch.max(attention_mask) == 1: |
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attention_mask = 1 - attention_mask |
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# Generate a response |
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outputs = model.generate( |
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input_ids=inputs['input_ids'], |
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attention_mask=attention_mask, |
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pixel_values=inputs['pixel_values'], |
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max_new_tokens=1, |
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no_repeat_ngram_size=2 |
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) |
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# Decode and print the response |
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decoded_response = processor.decode(outputs[0], skip_special_tokens=True)[len(prompt):] |
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return decoded_response |
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print(generate_response(prompt, raw_image)) |
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#spine |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- num_epochs: 2 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
