MarkBase Admin
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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
51 lines
1.7 KiB
Swift
51 lines
1.7 KiB
Swift
import Foundation
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public struct AudioConfig: Codable {
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public let hiddenSize: Int
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public let numAttentionHeads: Int
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public let numHiddenLayers: Int
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public let convKernelSize: Int
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public let attentionChunkSize: Int
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public let attentionContextLeft: Int
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public let attentionContextRight: Int
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public let attentionLogitCap: Float
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public let hiddenAct: String
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public let rmsNormEps: Float
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public let outputProjDims: Int
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public let subsamplingConvChannels: [Int]
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public let residualWeight: Float
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public init(
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hiddenSize: Int = 1024,
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numAttentionHeads: Int = 8,
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numHiddenLayers: Int = 12,
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convKernelSize: Int = 5,
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attentionChunkSize: Int = 12,
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attentionContextLeft: Int = 13,
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attentionContextRight: Int = 0,
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attentionLogitCap: Float = 50.0,
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hiddenAct: String = "silu",
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rmsNormEps: Float = 1e-6,
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outputProjDims: Int = 1536,
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subsamplingConvChannels: [Int] = [128, 32],
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residualWeight: Float = 0.5
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) {
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self.hiddenSize = hiddenSize
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self.numAttentionHeads = numAttentionHeads
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self.numHiddenLayers = numHiddenLayers
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self.convKernelSize = convKernelSize
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self.attentionChunkSize = attentionChunkSize
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self.attentionContextLeft = attentionContextLeft
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self.attentionContextRight = attentionContextRight
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self.attentionLogitCap = attentionLogitCap
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self.hiddenAct = hiddenAct
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self.rmsNormEps = rmsNormEps
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self.outputProjDims = outputProjDims
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self.subsamplingConvChannels = subsamplingConvChannels
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self.residualWeight = residualWeight
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}
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public var headDim: Int {
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hiddenSize / numAttentionHeads
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}
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} |