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Found 2 papers, 1 papers with code
Lillama: Large Language Models Compression via Low-Rank Feature Distillation
Current LLM structured pruning methods typically involve two steps: (1) compression with calibration data and (2) costly continued pretraining on billions of tokens to recover lost performance.
BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels.
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