phi3
Datasets
All datasets matching “phi3”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/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.Magpie-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.phi_30K_qwq_0K_eval_2e29
mlfoundations-dev/phi_30K_qwq_0K_eval_2e29
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
Accuracy
26.7
42.2
44.8
8.1
32.0
47.1
0.8
0.3
0.1
20.7
2.3
0.3
AIME24
Average Accuracy: 26.67% ± 1.63%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
23.33%
7
30
2
26.67%… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/phi_30K_qwq_0K_eval_2e29.the_pile_Wikipedia_phi3_8k