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---
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tags:
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- autotrain
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- text-generation-inference
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- image-text-to-text
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- text-generation
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- peft
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library_name: transformers
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base_model: google/paligemma-3b-pt-224
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license: other
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datasets:
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- abhishek/vqa_small
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---
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# Model Trained Using AutoTrain
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This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
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# Usage
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```python
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# you will need to adjust code if you didnt use peft
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from PIL import Image
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from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
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import torch
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import requests
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from peft import PeftModel
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base_model_id = BASE_MODEL_ID
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peft_model_id = THIS_MODEL_ID
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max_new_tokens = 100
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text = "Whats on the flower?"
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img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bee.JPG?download=true"
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image = Image.open(requests.get(img_url, stream=True).raw)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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base_model = PaliGemmaForConditionalGeneration.from_pretrained(base_model_id)
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processor = PaliGemmaProcessor.from_pretrained(base_model_id)
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model = PeftModel.from_pretrained(base_model, peft_model_id)
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model.merge_and_unload()
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model = model.eval().to(device)
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inputs = processor(text=text, images=image, return_tensors="pt").to(device)
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with torch.inference_mode():
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generated_ids = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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do_sample=False,
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)
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result = processor.batch_decode(generated_ids, skip_special_tokens=True)
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print(result)
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``` |
