Text Classification
Transformers
Safetensors
English
llama
RLHF
Nexusflow
Athene
Reward Model
text-embeddings-inference
Instructions to use Nexusflow/Athene-RM-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nexusflow/Athene-RM-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nexusflow/Athene-RM-70B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nexusflow/Athene-RM-70B") model = AutoModelForSequenceClassification.from_pretrained("Nexusflow/Athene-RM-70B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c5bdd495f037989fdce938c5ebb322a36345f1fe5090dd4ce73a66cf0c85b01
- Size of remote file:
- 6.97 kB
- SHA256:
- d33e83d1d3253f657c395e1fe7413f9c14ac02058656414ebcd16ffeb0f0340f
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