LiquidAI shows that overloading a model context with many tools increases latency and selection errors. They applied LFM2.5-ColBERT-350M to rank 151 tools and keep the top five, reporting fast, reliable filtering even without fine-tuning. A live demo is on Hugging Face and a technical write-up is on their blog.
LiquidAI demonstrates that storing many tools in a model context window raises latency and increases risk of incorrect tool selection.
Storing too many tools in your context window increases latency and can lead to wrong tool selection.
In this demo, we used LFM2.5-ColBERT-350M as a filter to only select the five most relevant tools among 151 options.
It's fast and reliable, even without any specific fine-tuning.
Try the demo on @huggingface! huggingface.co/spaces/Liquid…
And learn more on our blog: liquid.ai/blog/lfm2-5-retrie…
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