Image-to-Text
Transformers
PyTorch
ONNX
vision-encoder-decoder
image-text-to-text
image-captioning
Eval Results (legacy)
Instructions to use tarekziade/test-push with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarekziade/test-push 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="tarekziade/test-push")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("tarekziade/test-push") model = AutoModelForMultimodalLM.from_pretrained("tarekziade/test-push", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tarekziade/test-push: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/tarekziade/test-push/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://tarekziade/test-push@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tarekziade/test-push/resolve/refs%2Fpr%2F1/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.