Instructblip Vicuna 7b

🚀 InstructBLIP model

The InstructBLIP model uses Vicuna-7b as its language model. It aims to provide advanced vision - language processing capabilities.

🚀 Quick Start

The InstructBLIP model offers a powerful solution for vision - language tasks. It is based on instruction tuning and can handle various visual and textual inputs.

✨ Features

• Utilizes Vicuna-7b as the language model.
• It is a visual instruction tuned version of BLIP-2.

📚 Documentation

Model description

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.

Intended uses & limitations

Usage is as follows:

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

model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b")
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")

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)

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.

How to use

For code examples, we refer to the documentation.

📄 License

License: other