Instructions to use sfairXC/FsfairX-LLaMA3-RM-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sfairXC/FsfairX-LLaMA3-RM-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sfairXC/FsfairX-LLaMA3-RM-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sfairXC/FsfairX-LLaMA3-RM-v0.1") model = AutoModelForSequenceClassification.from_pretrained("sfairXC/FsfairX-LLaMA3-RM-v0.1") - Notebooks
- Google Colab
- Kaggle
Update README.md
#4
by johnowhitaker - opened
tokenizer -> rm_tokenizer (assuming this is not intentional? example usage fails without this change as tokenizer isn't defined)
hendrydong changed pull request status to merged