Instructions to use tscholak/2jrayxos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tscholak/2jrayxos with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tscholak/2jrayxos") model = AutoModelForSeq2SeqLM.from_pretrained("tscholak/2jrayxos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tscholak/2jrayxos: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/tscholak/2jrayxos/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tscholak/2jrayxos/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tscholak/2jrayxos/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- 862e2f8d2e4bbee8a206916cb7755560818c908e8da5a3adc116551e78491f21
- Size of remote file:
- 3.13 GB
- SHA256:
- f248d39a2070137ad39fc426945d89524f2d80fc04996b4f401900de1db4c50b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.