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--- |
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base_model: gv-hf/paligemma2-3b-pt-448 |
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library_name: peft |
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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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# PaliGemma2-3b-VQAv2 |
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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. |
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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). |
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Make sure you install transformers in main branch before using this or running fine-tuning. |
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## How to Use |
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Below is the code to use this model. Also see [inference notebook](https://colab.research.google.com/drive/100IQcvMvGm9y--oelbLfI__eHCoz5Ser?usp=sharing). |
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```python |
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from transformers import AutoProcessor, PaliGemmaForConditionalGeneration |
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from PIL import Image |
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import requests |
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model_id = "merve/paligemma2-3b-vqav2" |
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model = PaliGemmaForConditionalGeneration.from_pretrained(model_id) |
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processor = AutoProcessor.from_pretrained("google/paligemma2-3b-pt-224") |
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prompt = "What is behind the cat?" |
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image_file = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cat.png?download=true" |
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raw_image = Image.open(requests.get(image_file, stream=True).raw) |
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inputs = processor(prompt, raw_image.convert("RGB"), return_tensors="pt") |
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output = model.generate(**inputs, max_new_tokens=20) |
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print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):]) |
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# gramophone |
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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: 4 |
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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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- Transformers (main as of Dec 5) |
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- PEFT 0.13.2 |
