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 rng_state.pth from OpenAssistant/reward-model-deberta-v3-base: direct link, hf CLI and curl.
- Browser
- Download file 15.6 kB
-
https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base/resolve/main/rng_state.pth
- Command line
-
hf download hf://OpenAssistant/reward-model-deberta-v3-base/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base/resolve/main/rng_state.pth
15.6 kB
- Xet hash:
- 679f7a72c435771144ef20dbb3b40358a114e04f487c237ccbfb387cbbd239ae
- Size of remote file:
- 15.6 kB
- SHA256:
- aea50855e4d0949ea23f93e66d195e6490b97c8de68e1c07fb65d4dba061fdb8
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