Can ReSSFormer's architectural innovations (R2MU + adaptive sparsity) be transferred to a code generation benc

Abstract

Abstract: We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.

Research Question

Can ReSSFormer's architectural innovations (R2MU + adaptive sparsity) be transferred to a code generation benchmark like HumanEval, and how does its pass@1 accuracy compare to standard decoder-only models with equal parameter counts?

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Provenance