Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large-v2 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-v2 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-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from OpenAssistant/reward-model-deberta-v3-large-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenAssistant/reward-model-deberta-v3-large-v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 84ef547f20cad874d00667c7ad39b2176d709e6cd60b0703308dcb97e49fb9e2
- Size of remote file:
- 1.74 GB
- SHA256:
- bb3306045ebdcf84e3dbd51a55338b6ed255a832cfe179bbd65051b282c5e244
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