Instructions to use dtorber/BioNLP-conditional-prompting-decoder-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-conditional-prompting-decoder-eLife 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="dtorber/BioNLP-conditional-prompting-decoder-eLife")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-conditional-prompting-decoder-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-conditional-prompting-decoder-eLife", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dtorber/BioNLP-conditional-prompting-decoder-eLife: direct link, hf CLI and curl.
- Browser
- Download file 648 MB
-
https://huggingface.co/dtorber/BioNLP-conditional-prompting-decoder-eLife/resolve/main/model.safetensors
- Command line
-
hf download hf://dtorber/BioNLP-conditional-prompting-decoder-eLife/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dtorber/BioNLP-conditional-prompting-decoder-eLife/resolve/main/model.safetensors
648 MB
- Xet hash:
- 41207c6b2b1193a121576d167054880bd0da254d0d79b42f4de2356c3b58f7ec
- Size of remote file:
- 648 MB
- SHA256:
- 0e60ed19526cc398c10beddfd433f6430445b9ca9ca48564e3b569a2e5caf77b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.