Instructions to use ldenoue/Title_Generation_T5Small_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldenoue/Title_Generation_T5Small_Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ldenoue/Title_Generation_T5Small_Model") model = AutoModelForSeq2SeqLM.from_pretrained("ldenoue/Title_Generation_T5Small_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/decoder_with_past_model_quantized.onnx from ldenoue/Title_Generation_T5Small_Model: direct link, hf CLI and curl.
- Browser
- Download file 39 MB
-
https://huggingface.co/ldenoue/Title_Generation_T5Small_Model/resolve/main/onnx/decoder_with_past_model_quantized.onnx
- Command line
-
hf download hf://ldenoue/Title_Generation_T5Small_Model/onnx/decoder_with_past_model_quantized.onnx
-
curl -L -o decoder_with_past_model_quantized.onnx https://huggingface.co/ldenoue/Title_Generation_T5Small_Model/resolve/main/onnx/decoder_with_past_model_quantized.onnx
39 MB
- Xet hash:
- 3c133f99372c68f9dc41bbacfe8033909001d0319f490765b40096736dc2cd1c
- Size of remote file:
- 39 MB
- SHA256:
- c727ea643ab893b89167079f07fbcfa6c36771c5e8b5f63a0e1c152b1cbcac71
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.