Instructions to use prodm93/lgl_summarise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prodm93/lgl_summarise with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("prodm93/lgl_summarise") model = AutoModelForSeq2SeqLM.from_pretrained("prodm93/lgl_summarise", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prodm93/lgl_summarise: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/prodm93/lgl_summarise/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prodm93/lgl_summarise/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prodm93/lgl_summarise/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- c228e6add69290bd4d40a633c9371e9e867de82261f47cb8d2e065c812db7f19
- Size of remote file:
- 1.22 GB
- SHA256:
- 558f128d9f5539cb2a8ce81daa874f2cd970e0577af5866467e41a5cabe9238b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.