Unlocking Efficient AI: A Breakthrough in Quantization-Aware Distillation
Can you have your cake and eat it too? In the world of AI, it's often a trade-off between model size, speed, and accuracy. But what if you could achieve all three without sacrificing performance?
Our team has made a significant breakthrough in Quantization-Aware Distillation (QAD), a technique that allows developers to run large language models (LLMs) at a fraction of the memory and speed without the usual quality drop. We've released updated 4-bit checkpoints for LFM2.5 models, which retain an impressive 97% of their original accuracy.
What does this mean for you? With QAD, you can deploy high-performance LLMs on edge devices, enabling faster and more efficient AI applications. Our benchmarks show that QAD checkpoints match or even surpass the performance of native Q4_0 models, with a significant reduction in memory footprint.
Ready to get started? Our QAD GGUFs are available on Hugging Face, and we've provided a simple guide on how to use them with llama.cpp or any runtime that supports GGUF Q4_0 artifacts.