Instructblip Flan T5 Xl 8bit Nf4

🚀 InstructBLIP model

This is an InstructBLIP model using Flan - T5-xl as the language model, which can be used for image - to - text tasks like image captioning.

🚀 Quick Start

Quantization with bitsandbytes
8 - bit / nf4 / Safetensors
-Mediocre 🥱

The InstructBLIP model was introduced in the paper InstructBLIP: Towards General - purpose Vision - Language Models with Instruction Tuning by Dai et al.

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

InstructBLIP is a visual instruction tuned version of [BLIP - 2](https://huggingface.co/docs/transformers/main/model_doc/blip - 2). Refer to the paper for details.

📚 Documentation

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-flan-t5-xl")
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")

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)

How to use

For code examples, we refer to the documentation.

📄 License

This project is under the MIT license.