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
Training details?
#2
by MicPie - opened
Hi, great work with the strong RM!
I found your blog post recently (https://efficient-unicorn-451.notion.site/Reward-Modeling-for-RLHF-abe03f9afdac42b9a5bee746844518d0) and I wondered if you used the same/a very similar recipe for Llama-3 when compared to the ones outlined there for Gemma and Mistral-7B?
Thank you and keep up the great work! :-)
Yes. The training recipe is similar to the previous ones.