/**
 * @copyright Sister Software
 * @license AGPL-3.0
 * @author Teffen Ellis, et al.
 *
 *   Int8-quantize the #244 coarse-placer (milestone 3). The placer is a linear classifier, so the
 *   only weight is a dense [class][feature] fp32 matrix (12×65536 = 3.0 MB). Per-CLASS symmetric
 *   int8 quantization — `scale[c] = max(|W[c]|) / 127`, `q = round(W / scale)` clamped to [-127,
 *   127] — shrinks it to 0.75 MB (4×) while preserving the linear math exactly up to rounding (the
 *   logit is `bias[c] + scale[c] * Σ int8`, dequantized on load by `CoarsePlacer.fromArtifactDir`).
 *   Per-class scales matter because class weight magnitudes differ (OTHER's outlier-exposure rows
 *   push bigger weights than the in-map countries).
 *
 *   Verify the accuracy cost with `mailwoman placer eval quant-compare` (target: within ~1pp).
 *
 *   Run: `mailwoman placer quantize [--in <fp32 dir>] [--out <int8 dir>]`
 */
/**
 * Options for {@linkcode quantizeCoarsePlacer}.
 */
export interface QuantizeCoarsePlacerOptions {
    /**
     * Fp32 artifact dir. Default `$MAILWOMAN_DATA_ROOT/coarse-placer/model`.
     */
    in?: string;
    /**
     * Int8 output dir. Default `$MAILWOMAN_DATA_ROOT/coarse-placer/model-int8`.
     */
    out?: string;
}
/**
 * Result of {@linkcode quantizeCoarsePlacer}.
 */
export interface QuantizeCoarsePlacerResult {
    outDir: string;
    fp32Bytes: number;
    int8Bytes: number;
    maxAbsErr: number;
    rmse: number;
}
/**
 * Coarse-placer int8 quantizer — see the module doc.
 */
export declare function quantizeCoarsePlacer(options?: QuantizeCoarsePlacerOptions, report?: (line: string) => void): Promise<QuantizeCoarsePlacerResult>;
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