Nyun AI

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Paper - arxiv.org/pdf/2402.01799… Code - github.com/nyunAI/Faster-…

We thoroughly benchmark LLAMA, LLAMA2 on an A100 GPU machine with multiple runtimes and compression methods. We believe that this work will help make informed decisions on LLM serving. We discuss possible novel methods to improve the latency and memory consumption of LLMs.

Our Team at @Nyun_AI_ recently compiled an experimental survey with a thorough codebase - Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward. We provide a comprehensive overview of multiple LLM compression methodologies along with system-level approaches.

Nice paper to help make informed decisions on LLM serving - improve the latency and memory consumption. Performance comparison of compressed variants of LLaMA2-7B using various inference engines, quantized for different predictions and across different hardwares, with multiple llm compression methodologies Paper - "Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward"

Check our paper 'Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures' at arxiv.org/abs/2402.12418. @ArnavChavan6

(1/2) At @Nyun_AI_, we believe efficient LLMs and LVMs of tomorrow will be made by growing smaller networks for desired hardwares rather than first building the large ones and then compressing them. Our paper accepted as notable oral at ICLR 2024 is the first step towards it.

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