CDL-RecSys/blip2-opt-2.7b-hm

Model Card for Model ID

This is an adapter for the Salesforce BLIP2 2.7B model (more information on the model here). It was fine-tuned for generating product descriptions based on images using the H&M dataset from the kaggle challenge 2022.

How to Get Started with the Model

Use the code below to get started with the model. Make sure to replace the path with a local path to an image.

from transformers import Blip2Processor, Blip2ForConditionalGeneration
from PIL import Image
import torch

torch_dtype = torch.bfloat16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

base_checkpoint = "Salesforce/blip2-opt-2.7b"
base_model = Blip2ForConditionalGeneration.from_pretrained(base_checkpoint, torch_dtype=torch_dtype)

adapter_checkpoint = "CDL-RecSys/blip2-opt-2.7b-hm"
model = PeftModel.from_pretrained(base_model, model_id=adapter_checkpoint)

processor = Blip2Processor.from_pretrained(base_checkpoint)
tokenizer = processor.tokenizer

image = Image.open("path/to/your/image.jpg")
inputs = processor(image, return_tensors="pt").to(device, torch_dtype)
generated_ids = model.generate(**inputs, max_length=max_length)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
generated_text

Training Details

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Training Procedure

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.12.0