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tarekziade
/
test-push

Image-to-Text
Transformers
PyTorch
ONNX
vision-encoder-decoder
image-text-to-text
image-captioning
Eval Results (legacy)
Model card Files
xet
 Community
1
How to use from the
Use from the
Transformers library
# 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="tarekziade/test-push")
# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("tarekziade/test-push")
model = AutoModelForImageTextToText.from_pretrained("tarekziade/test-push")
Quick Links

distilvit

This model is a work in progress. Fine-tuned version of those base models:

This model was trained on:

You can get that checkpoint using the 3083a3cef6e3c8dd90df3f088074bbe836b0f403 commit.

It was then further fine-tuned on :

You can find the code used to create the model here: https://github.com/mozilla/distilvit

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Base model

google/vit-base-patch16-224-in21k
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Dataset used to train tarekziade/test-push

Evaluation results