Instructblip Vicuna 13b

🚀 InstructBLIP model

The InstructBLIP model uses Vicuna-13b as its language model. It aims to achieve better performance in vision - language tasks.

🚀 Quick Start

InstructBLIP is a visual instruction tuned version of BLIP-2. Refer to the paper InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Dai et al. for details.

Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team.

✨ Features

• Uses Vicuna-13b as the language model.
• It is a visual instruction tuned version of BLIP-2.

💻 Usage Examples

Basic Usage

from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
import torch
from PIL import Image
import requests

model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-13b")
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-13b")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
prompt = "What is unusual about this image?"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(device)

outputs = model.generate(
        **inputs,
        do_sample=False,
        num_beams=5,
        max_length=256,
        min_length=1,
        top_p=0.9,
        repetition_penalty=1.5,
        length_penalty=1.0,
        temperature=1,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)

Advanced Usage

For code examples, we refer to the documentation.

📚 Documentation

Intended uses & limitations

Usage is as described in the code example above.

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

📄 License

Other