Instructions to use Eli-Rhm/Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eli-Rhm/Reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Eli-Rhm/Reward")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Eli-Rhm/Reward") model = AutoModelForSequenceClassification.from_pretrained("Eli-Rhm/Reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Eli-Rhm/Reward: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/Eli-Rhm/Reward/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Eli-Rhm/Reward/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Eli-Rhm/Reward/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- 7ee0cfb09c5b99651101f7cb61652ee4cefe20e96221b123f65727043f1b9787
- Size of remote file:
- 439 MB
- SHA256:
- e276cc4b10512d942b61acdf0f45d295981e575faaa368d317740b369fb5dff5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.