/**
 * export/tensors.ts — the canonical named-tensor view of an EvermindLM.
 *
 * Every export format (safetensors / ONNX / GGUF / HF) consumes the SAME named,
 * shaped tensor list — defined ONCE here so the formats never disagree about a
 * weight's name, shape, or order. The runtime is left untouched: we read the
 * model purely through its public {@link EvermindLM.config} and
 * {@link EvermindLM.parameters} (the AdamW-ordered flat buffers), and re-attach
 * the names/shapes that order implies.
 *
 * Parameter order (must mirror EvermindLM.parameters / SharedExpertMoE.parameters):
 *   emb,
 *   per layer l: conv[l], normConv[l], normMoe[l],
 *                moe: router, shared.{w1,b1,w2,b2}, expert{e}.{w1,b1,w2,b2}…
 */
import type { EvermindLM } from "../lm/evermind_lm.js";
/** A named, shaped weight tensor (row-major, flat f32). */
export interface NamedTensor {
    name: string;
    /** Row-major dimensions, e.g. [vocab, dModel]. */
    shape: number[];
    data: Float32Array;
}
/** The flat numeric architecture an export needs (a superset of the LM config). */
export interface EvermindArch {
    vocabSize: number;
    dModel: number;
    numLayers: number;
    convKernel: number;
    hiddenDim: number;
    numExperts: number;
    topK: number;
}
/** Pull the architecture out of a live LM (or any config-shaped object). */
export declare function archOf(lm: EvermindLM): EvermindArch;
/** A weight name + its expected shape, in canonical parameter order. */
export interface TensorSpec {
    name: string;
    shape: number[];
}
/** The name+shape of every parameter, in EvermindLM.parameters() order. */
export declare function evermindTensorSpec(a: EvermindArch): TensorSpec[];
/**
 * The full named/shaped tensor list of a trained LM, zipping the deterministic
 * {@link evermindTensorSpec} against the live {@link EvermindLM.parameters}. The
 * data buffers are the model's own (no copy) — treat them as read-only.
 */
export declare function namedTensors(lm: EvermindLM): NamedTensor[];
/** Total trainable scalar parameters (for sizing / model-card display). */
export declare function paramCount(lm: EvermindLM): number;
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