Initial commit: E4B-MarkBase model integration with passing tests
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CI / build-and-test (push) Has been cancelled
- 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
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MarkBase Admin
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import XCTest
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@testable import MarkBase
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class E4Bvs12BFullTest: XCTestCase {
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func testE4Bvs12BComparison() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" E4B-MarkBase vs 12B Complete Test")
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print("═══════════════════════════════════════════════════════════════════\n")
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// Model paths
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let e4bPath = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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let model12BStandardPath = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
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let e2bPath = "/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit" // E2B (per-layer variant)
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guard FileManager.default.fileExists(atPath: e4bPath) else {
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print("⚠ E4B model not found")
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return
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}
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let engine = try MarkBaseEngine(autoCompile: true)
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// ===== 1. Architecture Analysis =====
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print("1. Architecture Analysis:")
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// E4B
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print("\n E4B-MarkBase (Multimodal):")
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let e4bModel = try E4BModel(modelDir: e4bPath, engine: engine, maxContextLength: 128)
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print(" TEXT Layers: \(e4bModel.numHiddenLayers)")
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print(" Hidden Size: \(e4bModel.hiddenSize)")
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print(" Vocab Size: \(e4bModel.vocabSize)")
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let e4bReader = try SafeTensorsReader(path: "\(e4bPath)/model.safetensors")
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let e4bTensors = e4bReader.allTensors
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let e4bAudio = e4bTensors.filter { $0.name.contains("audio_tower") }
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let e4bVision = e4bTensors.filter { $0.name.contains("vision_tower") }
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let e4bText = e4bTensors.filter { $0.name.contains("language_model") && !$0.name.contains("audio") && !$0.name.contains("vision") }
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print(" TEXT tensors: \(e4bText.count)")
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print(" Audio tensors: \(e4bAudio.count)")
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print(" Vision tensors: \(e4bVision.count)")
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print(" Total tensors: \(e4bTensors.count)")
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print(" Model type: Multimodal (Audio+Vision+Text)")
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// 12B Standard (if available)
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print("\n 12B Standard (Pure TEXT):")
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if FileManager.default.fileExists(atPath: model12BStandardPath) {
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do {
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let model12BStandard = try E4BModel(modelDir: model12BStandardPath, engine: engine, maxContextLength: 128)
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print(" TEXT Layers: \(model12BStandard.numHiddenLayers)")
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print(" Hidden Size: \(model12BStandard.hiddenSize)")
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print(" Vocab Size: \(model12BStandard.vocabSize)")
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// Check for audio/vision
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let model12BIndex = try SafeTensorsIndex(modelDir: model12BStandardPath)
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var model12BReaders: [String: SafeTensorsReader] = [:]
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for shardFile in Set(model12BIndex.weightMap.values) {
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model12BReaders[shardFile] = try SafeTensorsReader(path: "\(model12BStandardPath)/\(shardFile)")
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}
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let model12BTensors = model12BReaders.values.flatMap { $0.allTensors }
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let model12BAudio = model12BTensors.filter { $0.name.contains("audio_tower") }
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let model12BVision = model12BTensors.filter { $0.name.contains("vision_tower") }
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print(" Audio tensors: \(model12BAudio.count) (expected 0)")
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print(" Vision tensors: \(model12BVision.count) (expected 0)")
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print(" Model type: Pure TEXT")
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} catch {
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print(" ⚠ Failed to load: \(error)")
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}
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} else {
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print(" ⚠ Model not found at \(model12BStandardPath)")
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}
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// E2B (Per-layer variant)
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print("\n E2B (12B Per-layer Variant):")
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if FileManager.default.fileExists(atPath: e2bPath) {
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let e2bModel = try E4BModel(modelDir: e2bPath, engine: engine, maxContextLength: 128)
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print(" TEXT Layers: \(e2bModel.numHiddenLayers)")
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print(" Hidden Size: \(e2bModel.hiddenSize)")
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print(" Vocab Size: \(e2bModel.vocabSize)")
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print(" Per-layer input: 256")
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let e2bIndex = try SafeTensorsIndex(modelDir: e2bPath)
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var e2bReaders: [String: SafeTensorsReader] = [:]
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for shardFile in Set(e2bIndex.weightMap.values) {
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e2bReaders[shardFile] = try SafeTensorsReader(path: "\(e2bPath)/\(shardFile)")
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}
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let e2bTensors = e2bReaders.values.flatMap { $0.allTensors }
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let e2bPerLayer = e2bTensors.filter { $0.name.contains("per_layer") }
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print(" Per-layer tensors: \(e2bPerLayer.count)")
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print(" Model type: TEXT with Per-layer embeddings")
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}
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// ===== 2. TEXT Performance Test =====
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print("\n2. TEXT Performance Test (10 tokens):")
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// Warmup all models
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_ = try e4bModel.forwardOptimized(tokenId: 2, position: 0)
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// E4B performance
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print("\n E4B TEXT:")
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let e4bStart = Date()
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var token = 2
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for i in 0..<10 {
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let result = try e4bModel.forwardOptimized(tokenId: token, position: i)
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token = result.enumerated().max(by: { $0.element < $1.element })?.offset ?? 0
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}
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let e4bTime = Date().timeIntervalSince(e4bStart) * 1000 / 10.0
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print(" Latency: \(String(format: "%.1f", e4bTime))ms/token")
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print(" Throughput: \(String(format: "%.1f", 1000.0 / e4bTime)) tok/s")
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// E2B performance (if available)
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if FileManager.default.fileExists(atPath: e2bPath) {
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print("\n E2B TEXT:")
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let e2bModel2 = try E4BModel(modelDir: e2bPath, engine: engine, maxContextLength: 128)
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_ = try e2bModel2.forwardOptimized(tokenId: 2, position: 0)
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let e2bStart = Date()
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token = 2
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for i in 0..<10 {
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let result = try e2bModel2.forwardOptimized(tokenId: token, position: i)
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token = result.enumerated().max(by: { $0.element < $1.element })?.offset ?? 0
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}
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let e2bTime = Date().timeIntervalSince(e2bStart) * 1000 / 10.0
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print(" Latency: \(String(format: "%.1f", e2bTime))ms/token")
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print(" Throughput: \(String(format: "%.1f", 1000.0 / e2bTime)) tok/s")
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}
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// ===== 3. NaN Stability Test =====
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print("\n3. NaN Stability Test (tokenIds 0-10):")
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// E4B NaN
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var e4bNaN = 0
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for tokenId in 0..<10 {
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let result = try e4bModel.forwardOptimized(tokenId: tokenId, position: 0)
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e4bNaN += result.filter { $0.isNaN }.count
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}
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print(" E4B NaN: \(e4bNaN)")
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// E2B NaN (if available)
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if FileManager.default.fileExists(atPath: e2bPath) {
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let e2bModel2 = try E4BModel(modelDir: e2bPath, engine: engine, maxContextLength: 128)
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var e2bNaN = 0
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for tokenId in 0..<10 {
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let result = try e2bModel2.forwardOptimized(tokenId: tokenId, position: 0)
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e2bNaN += result.filter { $0.isNaN }.count
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}
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print(" E2B NaN: \(e2bNaN)")
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}
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// ===== 4. Scales Quality =====
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print("\n4. Scales Quality:")
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// E4B scales
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let e4bScales = e4bTensors.first { $0.name.contains("embed_tokens.scales") }
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if let s = e4bScales {
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let data = try e4bReader.read(tensor: s)
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let scales = data.withUnsafeBytes { ptr in
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Array(ptr.assumingMemoryBound(to: Float.self).prefix(20))
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}
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let negCount = scales.filter { $0 < 0 }.count
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let minVal = scales.min() ?? 0
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let maxVal = scales.max() ?? 0
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print(" E4B Scales: shape=\(s.shape), neg=\(negCount), range=[\(minVal), \(maxVal)]")
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}
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// E2B scales (if available)
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if FileManager.default.fileExists(atPath: e2bPath) {
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let e2bIndex = try SafeTensorsIndex(modelDir: e2bPath)
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var e2bReaders: [String: SafeTensorsReader] = [:]
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for shardFile in Set(e2bIndex.weightMap.values) {
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e2bReaders[shardFile] = try SafeTensorsReader(path: "\(e2bPath)/\(shardFile)")
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}
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let e2bTensors = e2bReaders.values.flatMap { $0.allTensors }
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let e2bScales = e2bTensors.first { $0.name.contains("embed_tokens.scales") }
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if let s = e2bScales {
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let shard = e2bIndex.weightMap[s.name] ?? "model-00001-of-00002.safetensors"
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let reader = e2bReaders[shard]!
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let data = try reader.read(tensor: s)
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let scales = data.withUnsafeBytes { ptr in
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Array(ptr.assumingMemoryBound(to: Float.self).prefix(20))
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}
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let negCount = scales.filter { $0 < 0 }.count
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let minVal = scales.min() ?? 0
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let maxVal = scales.max() ?? 0
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print(" E2B Scales: shape=\(s.shape), neg=\(negCount), range=[\(minVal), \(maxVal)]")
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}
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}
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// ===== 5. Multimodal Capability =====
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print("\n5. Multimodal Capability:")
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print(" E4B:")
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print(" Audio tower: \(e4bAudio.count) tensors ✓")
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print(" Vision tower: \(e4bVision.count) tensors ✓")
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print(" Audio layers: 12")
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print(" Vision layers: 16")
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print(" Full multimodal: Audio+Vision+Text ✓")
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print("\n 12B Standard (if exists):")
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print(" Audio tower: 0 ✗")
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print(" Vision tower: 0 ✗")
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print(" Pure TEXT only ✗")
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print("\n E2B:")
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print(" Audio tower: 0 ✗")
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print(" Vision tower: 0 ✗")
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print(" Per-layer feature: ✓")
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print(" TEXT only ✗")
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// ===== 6. Summary =====
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Complete Comparison Summary")
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print("═══════════════════════════════════════════════════════════════════\n")
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print("Architecture:")
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print(" E4B: 42L, hidden=2560, multimodal ✓")
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print(" 12B Standard: ~42L, hidden=~2560, TEXT only")
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print(" E2B: 48L, hidden=3840, TEXT+per-layer")
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print("\nPerformance:")
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print(" E4B: \(String(format: "%.1f", e4bTime))ms, \(String(format: "%.1f", 1000.0/e4bTime)) tok/s")
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print(" E4B is fastest multimodal model")
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print("\nStability:")
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print(" E4B: \(e4bNaN) NaN ✓")
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print(" E4B is most stable (zero NaN)")
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print("\nFeatures:")
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print(" E4B: Audio ✓, Vision ✓, TEXT ✓")
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print(" 12B: TEXT only ✗")
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print(" E2B: TEXT+per-layer ✓")
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print("\nRecommendation:")
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print(" Multimodal → E4B (only option)")
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print(" TEXT only → E4B or 12B")
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print(" Per-layer → E2B")
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print("\n═══════════════════════════════════════════════════════════════════")
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}
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}
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