Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from OpenAssistant/reward-model-deberta-v3-base: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://OpenAssistant/reward-model-deberta-v3-base@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base/resolve/refs%2Fpr%2F1/optimizer.pt
1.48 GB
- Xet hash:
- 8370a587343c0d2820e812800a74a5cbf456430afe5ed56a187d22bbd880604d
- Size of remote file:
- 1.48 GB
- SHA256:
- c51e84122732847167b0a9f8184f3097cc9a26a23cb3292d287d7e432736c480
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