xsample/Llama-3.1-MIG-Tulu-3-8B-SFT

Llama-3.1-MIG-Tulu-3-8B-SFT

Project | Github | Paper | HuggingFace's collection

Llama-3.1-MIG-Tulu-3-8B-SFT is fine-tuned on automatically selected 50K data.

Performance

Method	Data Size	ARC	BBH	GSM	HE	MMLU	IFEval	Avg_obj	AE	MT	Wild	Avg_sub	Avg
Pool	939K	69.15	63.88	83.40	63.41	65.77	67.10	68.79	8.94	6.86	-24.66	38.40	53.59
Random	50K	74.24	64.80	70.36	51.22	63.86	61.00	64.25	8.57	7.06	-22.15	39.36	51.81
ZIP	50K	77.63	63.00	52.54	35.98	65.00	61.00	59.19	6.71	6.64	-32.10	35.69	47.44
IFD	50K	75.93	63.56	61.03	49.39	64.39	53.60	61.32	12.30	7.03	-20.20	40.83	51.08
#InsTag	50K	72.54	64.80	69.83	48.17	63.50	65.99	64.14	6.58	6.84	-20.70	38.21	51.17
DEITA	50K	78.98	66.11	74.07	49.39	64.00	64.33	66.15	10.19	6.83	-19.95	39.50	52.83
CaR	50K	78.98	69.04	71.42	52.44	65.15	56.75	65.63	12.55	6.95	-20.67	40.57	53.10
QDIT	50K	79.66	65.42	70.74	53.05	65.06	57.30	65.21	15.78	6.76	-20.56	41.03	53.12
MIG	50K	80.00	66.39	72.02	57.93	64.44	65.06	67.64	14.66	7.32	-17.77	42.99	55.32

Citation

@article{chen2025mig,
  title={MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space},
  author={Chen, Yicheng and Li, Yining and Hu, Kai and Ma, Zerun and Ye, Haochen and Chen, Kai},
  journal={arXiv preprint arXiv:2504.13835},
  year={2025}
}