Instructions to use nkn002/ctrlsum_bart_base_640k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkn002/ctrlsum_bart_base_640k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nkn002/ctrlsum_bart_base_640k") model = AutoModelForSeq2SeqLM.from_pretrained("nkn002/ctrlsum_bart_base_640k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from nkn002/ctrlsum_bart_base_640k: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/nkn002/ctrlsum_bart_base_640k/resolve/main/optimizer.pt
- Command line
-
hf download hf://nkn002/ctrlsum_bart_base_640k/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/nkn002/ctrlsum_bart_base_640k/resolve/main/optimizer.pt
1.12 GB
- Xet hash:
- a9eb72c1b441f63592e0f973b33bb281432cfee11a660eb8ff2b4d2acc9eaa08
- Size of remote file:
- 1.12 GB
- SHA256:
- 9853364620b4d5b05719394ffb7394756f43edaa0d39636851d3cce987402cd2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.