Instructions to use silmi224/led with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="silmi224/led")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/silmi224/led/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 8bed9347a30d2313284c1020c7c49f802371a83f0f8aaabd4d2c561890e476f3
- Size of remote file:
- 4.73 kB
- SHA256:
- cffb114b88834e374a2b97d391bd4c812c6563333400201a03700d897a3cca2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.