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:
- 5899f219ee02a34293ec6f8d61d4c60dc612d0700c98a4d5693f18a6414e2aba
- Size of remote file:
- 4.66 GB
- SHA256:
- b1acce6ac65ae852f3166f7a9693eef2d99c72f81e4728ce8c66e01d6b5b4722
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