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Quantize a 7B Model End-to-End

Shrink the model to a quarter of its size — then prove it still answers just as well.

Key Insight​

This project takes a 7B model through the full serving-quantization pipeline: pick AWQ, calibrate it on ~128 real-looking prompts, serve it with vLLM, and only ship it if it passes a quality gate.

Why This Matters​

Quantization is the biggest single lever on inference cost, but a careless one quietly degrades answers. Doing every step end-to-end — calibration and the gate — is how teams cut memory roughly 4× without secretly shipping a worse model.