Instructions to use shed-e/Summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/Summary 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="shed-e/Summary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shed-e/Summary") model = AutoModelForSeq2SeqLM.from_pretrained("shed-e/Summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shed-e/Summary: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/shed-e/Summary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shed-e/Summary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shed-e/Summary/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 1ab8d3c5a8ee91be97018c91bd898cb6fc61045490224221862d767edc8eb138
- Size of remote file:
- 1.2 GB
- SHA256:
- bbd9b0c10debb43a21bcdb14788424012ddf74aecbcf28f2425df7d23f655c90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.