phi-3
Datasets
All datasets matching “phi-3”train_positive_phi3_kblam_rwkuMagpie-Phi3-Pro-1M-v0.1
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
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High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/HayatoHongo/Magpie-Phi3-Pro-1M-v0.1.Magpie-Phi3-Pro-300K-Filtered
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Phi3-Pro-300K-Filtered.medical-logits-phi3.5-mini_medmcqa_pubmMagpie-Phi3-Pro-1M-v0.1
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Phi3-Pro-1M-v0.1.medical_dirichlet_phi3This dataset was generated using: https://github.com/sarus-tech/dp-llm-ft .
Patients, diseases, symptoms lists, treatments are all generated using a local Phi 3.5 (microsoft/Phi-3.5-mini-instruct).
The diseases are sampled independently for each patient, using a discrete distribution of diseases sampled from a Dirichlet distribution.