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
45 lines
1.4 KiB
Swift
45 lines
1.4 KiB
Swift
import Foundation
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public struct VisionConfig: 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 headDim: Int
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public let globalHeadDim: Int
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public let intermediateSize: Int
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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 patchSize: Int
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public let imageSize: Int
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public init(
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hiddenSize: Int = 768,
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numAttentionHeads: Int = 12,
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numHiddenLayers: Int = 12,
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headDim: Int = 64,
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globalHeadDim: Int = 64,
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intermediateSize: Int = 3072,
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hiddenAct: String = "gelu_pytorch_tanh",
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rmsNormEps: Float = 1e-6,
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outputProjDims: Int = 1536,
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patchSize: Int = 14,
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imageSize: Int = 224
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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.headDim = headDim
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self.globalHeadDim = globalHeadDim
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self.intermediateSize = intermediateSize
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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.patchSize = patchSize
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self.imageSize = imageSize
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}
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public var numPatches: Int {
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(imageSize / patchSize) * (imageSize / patchSize)
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}
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} |