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:
- 0a5f6829ec1ec90ecbb2412cc22a6512186590fdd1165a0a9cefee52aab51f63
- Size of remote file:
- 4.66 GB
- SHA256:
- 2bef95ff3e2e8623ae49f1c7b1d287fa1d8e0c048c7ade8e349e2bb33cc7bf77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.