Instructions to use memegpt/blip2_endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memegpt/blip2_endpoint with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="memegpt/blip2_endpoint")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("memegpt/blip2_endpoint") model = AutoModelForMultimodalLM.from_pretrained("memegpt/blip2_endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 820 Bytes
095cf65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from typing import Dict, List, Any
import torch
class EndpointHandler():
def __init__(self, path=""):
self.processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model.to(self.device)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
image = data.pop("inputs", data)
processed = self.processor(images=image, return_tensors="pt").to(self.device)
out = self.model.generate(**processed)
return self.processor.decode(out[0], skip_special_tokens=True) |