Instructions to use RLHFlow/ArmoRM-Llama3-8B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RLHFlow/ArmoRM-Llama3-8B-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RLHFlow/ArmoRM-Llama3-8B-v0.1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RLHFlow/ArmoRM-Llama3-8B-v0.1", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("RLHFlow/ArmoRM-Llama3-8B-v0.1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
add AIBOM
#20 opened 8 months ago
by
RiccardoDav
output score not correct?
#19 opened 12 months ago
by
weiminw
Adding Evaluation Results
#18 opened over 1 year ago
by
leaderboard-pr-bot
This tokenizer is different from Meta-Llama-3-8B-Instruct
#17 opened almost 2 years ago
by
AlignLearner
Why is the code-complexity coefficient so high in the demo example?
1
#16 opened almost 2 years ago
by
icdt