Instructions to use Roasters/Answer-Predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roasters/Answer-Predictor with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Roasters/Answer-Predictor") model = AutoModelForSeq2SeqLM.from_pretrained("Roasters/Answer-Predictor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Roasters/Answer-Predictor: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/Roasters/Answer-Predictor/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Roasters/Answer-Predictor/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Roasters/Answer-Predictor/resolve/main/flax_model.msgpack
242 MB
- Xet hash:
- 4b1d5cbd6c98ad0c9a810d879e3512a6c0f6e9592781525ade74e3293a037d0d
- Size of remote file:
- 242 MB
- SHA256:
- 9fd33195682e04b56989927b2cf34aeb668b0be333e26ebfc8f51309783b31f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.