Shypd.aiThe largest open index of AI toolsSepLLM is an AI Frameworks & Infra tool that accelerates large language models by compressing segments into separators. It offers an easy-to-use native sparse attention baseline method, significantly reducing KV cache and processing sequences up to 4 million tokens.
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SepLLM is a plug-and-play framework designed to accelerate large language models (LLMs) by addressing their computational demands and inference speed challenges. It leverages a key observation that certain separator tokens, like punctuation, disproportionately contribute to attention scores. SepLLM condenses information from segments between these separators into the separator tokens themselves, effectively compressing segments and eliminating redundant tokens without significant information loss. The framework also includes efficient kernels for training acceleration. Experimental results demonstrate its effectiveness across training-free, training-from-scratch, and post-training settings. For instance, using a Llama-3-8B backbone, SepLLM achieves over a 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. In streaming settings, it can process sequences of up to 4 million tokens or more, ensuring consistent language modeling capabilities. SepLLM also provides SepCache, a portable and efficient Cache class available on HuggingFace, supporting Llama 3.1 series and newer transformers.
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