A solvable model of neural scaling laws
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This paper proposes a solvable model that explains the empirical neural scaling laws observed in large language models.
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper proposes a solvable model that explains the empirical neural scaling laws observed in large language models.
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MiniCache introduces a novel KV cache compression method that operates along the depth dimension of large language models, achieving significant memory savings.
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A comprehensive survey of speculative decoding techniques for efficient large language model inference.
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Intactkv improves large language model quantization by preserving pivot tokens, achieving state-of-the-art results.
Jinuk Kim, Marwa El Halabi, Wonpyo Park, et al.
GuidedQuant improves post-training quantization of large language models by using end-loss guidance to better preserve model accuracy.
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This paper introduces Llmc, a versatile toolkit for benchmarking LLM quantization methods, enabling standardized evaluation and comparison.
Tianyi Zhang, Anshumali Shrivastava
Leanquant introduces a loss-error-aware grid quantization method for LLMs that achieves accurate and scalable compression by minimizing quantization error with respect to the model's loss function.
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Ostquant improves LLM quantization by applying orthogonal and scaling transformations to better fit weight distributions, reducing accuracy loss.
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This survey systematically reviews knowledge distillation techniques for transferring capabilities from large proprietary LLMs to smaller models.
Songyue Han, Mingyu Wang, Jialong Zhang, et al.
A comprehensive survey of LLMs covering architectures, key technologies, interdisciplinary integrations, optimization, applications, and challenges.
Hao Liang, Qihan Lin, Zhaoyang Han, et al.
Introduces K12-KGraph, a curriculum-aligned knowledge graph from Chinese textbooks, with benchmark and training data to improve LLMs' curriculum cognition.
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LoRA freezes pre-trained model weights and injects trainable low-rank matrices to enable efficient fine-tuning of large language models.