Instructions to use OpenRLHF/Llama-3-8b-rm-mixture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenRLHF/Llama-3-8b-rm-mixture with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OpenRLHF/Llama-3-8b-rm-mixture") model = AutoModel.from_pretrained("OpenRLHF/Llama-3-8b-rm-mixture", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
The Llama3-8b-based Reward Model was trained using OpenRLHF and a combination of datasets available at https://huggingface.co/datasets/OpenLLMAI/preference_dataset_mixture2_and_safe_pku.
Base model: https://huggingface.co/OpenRLHF/Llama-3-8b-sft-mixture
Cosine Scheduler
Learning Rate: 9e-6
Warmup Ratio: 0.03
Batch Size: 256
Epoch: 1
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