Instructions to use Jasim9085/bart-autoencoder-thought-vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jasim9085/bart-autoencoder-thought-vector with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jasim9085/bart-autoencoder-thought-vector") model = AutoModelForSeq2SeqLM.from_pretrained("Jasim9085/bart-autoencoder-thought-vector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from Jasim9085/bart-autoencoder-thought-vector: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/Jasim9085/bart-autoencoder-thought-vector/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://Jasim9085/bart-autoencoder-thought-vector/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Jasim9085/bart-autoencoder-thought-vector/resolve/main/last-checkpoint/optimizer.pt
1.12 GB
- Xet hash:
- 313ce01e8d0c7060594d83cf5541024093f7e25104a360b77b96c9f2479e2f17
- Size of remote file:
- 1.12 GB
- SHA256:
- 531b511d4e83008119f801defafb040e8cec0ca7220192f17d6491a3727b6dd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.