Glm5NextKdaWeights

spark_model::layers::glm5next_kda

Struct Glm5NextKdaWeights

pub struct Glm5NextKdaWeights {Show 13 fields
    pub q_proj: DenseWeight,
    pub k_proj: DenseWeight,
    pub v_proj: DenseWeight,
    pub conv: DenseWeight,
    pub f_a: DenseWeight,
    pub f_b: DenseWeight,
    pub dt_bias: DevicePtr,
    pub a_log: DevicePtr,
    pub b_proj: DenseWeight,
    pub g_a: DenseWeight,
    pub g_b: DenseWeight,
    pub o_norm: DenseWeight,
    pub o_proj: DenseWeight,
}

Expand description

One KDA block’s device weights. Torch Linear layout [out, in], BF16, except the two F32 gate parameters. There is no Z tensor — the output gate is low-rank g_a/g_b.

Fields§

§q_proj: DenseWeight§k_proj: DenseWeight§v_proj: DenseWeight§conv: DenseWeight

[conv_dim, conv_kernel] BF16 = concat([q, k, v]).squeeze(1).

§f_a: DenseWeight§f_b: DenseWeight§dt_bias: DevicePtr

[heads * head_dim] F32 — per channel.

§a_log: DevicePtr

[heads] F32 — per head. The asymmetry with dt_bias is the highest-risk line.

§b_proj: DenseWeight§g_a: DenseWeight§g_b: DenseWeight§o_norm: DenseWeight

[head_dim] BF16.

§o_proj: DenseWeight

Auto Trait Implementations§

impl Freeze for Glm5NextKdaWeights

impl RefUnwindSafe for Glm5NextKdaWeights

impl Send for Glm5NextKdaWeights

impl Sync for Glm5NextKdaWeights

impl Unpin for Glm5NextKdaWeights

impl UnwindSafe for Glm5NextKdaWeights

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