Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
Authors: Akide Liu, Jing Liu, Zizheng Pan, Yefei He, Reza Haffari, Bohan Zhuang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a comprehensive evaluation of Mini Cache utilizing various models including LLa MA-2, LLa MA-3, Phi-3, Mistral, and Mixtral across multiple benchmarks, demonstrating its exceptional performance in achieving superior compression ratios and high throughput. |
| Researcher Affiliation | Academia | Akide Liu1 Jing Liu1 Zizheng Pan1 Yefei He2 Gholamreza Haffari1 Bohan Zhuang1,2 1ZIP Lab, Monash University, Australia 2ZIP Lab, Zhejiang University, China |
| Pseudocode | Yes | Algorithm 1: The Mini Cache Inference Algorithm; Algorithm 2: The Mini Cache Prefill & Decoding Compression Algorithm |
| Open Source Code | Yes | Project is available at https://minicache.vmv.re . |
| Open Datasets | Yes | We conduct extensive experiments with representative LLMs, including Mixtral-8x7B [22], Phi3-Mini [23], and LLa MA-3 [6] 8B and 70B, respectively. Our method is benchmarked across a diverse range of question answering and generation datasets [24, 25, 26, 27, 28, 29, 30, 31] using the lm-eval-harness [32]. Additionally, we evaluate our results on Long Bench [33] for long-sequence generation. |
| Dataset Splits | Yes | Based on LLa MA-3-70B [6], we conduct zero-shot inference on the validation sets of three widely recognized benchmarks: COQA [71], GSM8K [10] and Truthful QA [72]. |
| Hardware Specification | Yes | For sequential loading of large models, we utilize NVIDIA 4 A100 80GB GPUs, more details refers to Appendix D. ... Using the LLa MA-2-7B model on a single 80GB NVIDIA A100 GPU, we benchmark our method in a batch-serving scenario... |
| Software Dependencies | No | Our method is benchmarked across a diverse range of question answering and generation datasets [24, 25, 26, 27, 28, 29, 30, 31] using the lm-eval-harness [32]. |
| Experiment Setup | Yes | For the proposed Mini Cache, we set the interpolation parameter t to 0.6, indicating that the merged results have a smaller rotation angle to the next layer. Furthermore, we set the token retention threshold γ to 0.05, according to the statistics of unmergeable tokens across multiple datasets. |