Quantization Aware Distillation available on TPN Bench.

True Performance Network@TPN_Labs

Quantization Aware Distillation available on TPN Bench. Users can quantize their models with TPN Bench toolkit, and can choose to do so at 3 different levels: Standard, I-Matrix and Quantization Aware Distillation. Here's how each work and what makes QAD a valuable tool for devs, labs and independent users. Standard quantization: The model’s parameters are stored at 16 bit precision. Quantizing rounds them down to 8 bit, or lower, like a zip file version of the model, making it smaller. I-matrix: Same idea, but it looks at the model first and finds the regions that appear most complex, assuming that complexity is where the capability lives. Those regions keep higher precision while the rest gets rounded harder. Quantization Aware Distillation: This method quantize the model, then treat the original model as a teacher and the quantized version as a student, training the student back toward its parent’s behaviour. It repairs what the rounding broke, needs inference and back propagation, meaning GPU work, which is why most people don’t do it, and why it’s the one worth having a service for. Quantize LLMs with Bench

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