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Mixed-Precision Deployment

Keep the few fragile layers in full precision and squeeze everything else.

Key Insight​

This project leaves the most sensitive parts of the model — the attention output projections and the final output layer (lm_head) — in 16-bit bfloat16 while quantizing everything else, then measures how much quality this recovers and what it costs in memory.

Why This Matters​

A handful of layers cause most of the quality loss when quantized, so spending extra bits only on them often recovers nearly all the accuracy for very little memory — a far better deal than quantizing every layer uniformly or leaving the whole model in full precision.