Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OpenAssistant/reward-model-deberta-v3-large: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenAssistant/reward-model-deberta-v3-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 22a395256917324f1d29109c9ff258869df4a1689a111d38016aa32947625afb
- Size of remote file:
- 1.74 GB
- SHA256:
- 903103d4990515d537c46262632aef9a8f7d0a815edd289868c9967e5b15c265
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