Text Generation
Transformers
PyTorch
English
Chinese
llama
text-generation-inference
Inference Endpoints

MiniMA-2-1B

πŸ“‘ arXiv | πŸ‘» GitHub | πŸ€— HuggingFace-MiniMA-3B | πŸ€— HuggingFace-MiniChat-3B | πŸ€– ModelScope-MiniMA-3B | πŸ€– ModelScope-MiniChat-3B | πŸ€— HuggingFace-MiniChat-1.5-3B | πŸ€— HuggingFace-MiniMA-2-3B | πŸ€— HuggingFace-MiniChat-2-3B | πŸ€— HuggingFace-MiniMA-2-1B | πŸ€— HuggingFace-MiniLoong-3B | πŸ€— HuggingFace-MiniMix-2/4x3B

❗ Must comply with LICENSE of LLaMA-2 since it is derived from LLaMA-2.

teaser_a

Standard Benchmarks

Method TFLOPs MMLU (5-shot) CEval (5-shot) DROP (3-shot) HumanEval (0-shot) BBH (3-shot) GSM8K (8-shot)
Mamba-2.8B 4.6E9 25.58 24.74 15.72 7.32 29.37 3.49
ShearedLLaMA-2.7B 0.8E9 26.97 22.88 19.98 4.88 30.48 3.56
BTLM-3B 11.3E9 27.20 26.00 17.84 10.98 30.87 4.55
StableLM-3B 72.0E9 44.75 31.05 22.35 15.85 32.59 10.99
Qwen-1.8B 23.8E9 44.05 54.75 12.97 14.02 30.80 22.97
Phi-2-2.8B 159.9E9 56.74 34.03 30.74 46.95 44.13 55.42
LLaMA-2-7B 84.0E9 46.00 34.40 31.57 12.80 32.02 14.10
MiniMA-3B 4.0E9 28.51 28.23 22.50 10.98 31.61 8.11
MiniMA-2-1B 6.3E9 31.34 34.92 20.08 10.37 31.16 7.28
MiniMA-2-3B 13.4E9 40.14 44.65 23.10 14.63 31.43 8.87
MiniMix-2/4x3B 25.4E9 44.35 45.77 33.78 18.29 33.60 21.61
MiniChat-3B 4.0E9 38.40 36.48 22.58 18.29 31.36 29.72
MiniChat-2-3B 13.4E9 46.17 43.91 30.26 22.56 34.95 38.13

Bibtex

@article{zhang2023law,
    title={Towards the Law of Capacity Gap in Distilling Language Models},
    author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
    year={2023},
    url={https://arxiv.org/abs/2311.07052}
}
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