Instructblip Flan T5 Xl 8bit

🚀 InstructBLIP模型

InstructBLIP模型以[Flan - T5 - xl](https://huggingface.co/google/flan - t5 - xl)作為語言模型。InstructBLIP由Dai等人在論文InstructBLIP: Towards General - purpose Vision - Language Models with Instruction Tuning中提出。

免責聲明：發佈InstructBLIP的團隊並未為該模型撰寫模型卡片，此模型卡片由Hugging Face團隊編寫。

🚀 快速開始

模型描述

InstructBLIP是[BLIP - 2](https://huggingface.co/docs/transformers/main/model_doc/blip - 2)的視覺指令調優版本。詳情請參考相關論文。

預期用途和限制

使用方法如下：

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)

如何使用

關於代碼示例，請參考文檔。

📄 許可證

本項目採用MIT許可證。