Initial commit: E4B-MarkBase model integration with passing tests

- 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

test_simple_generation.swift

@@ -0,0 +1,33 @@
+import Foundation
+@testable import MarkBase
+
+print("\n═══════════════════════════════════════")
+print("Simple MoE Generation Test with Debug")
+print("═══════════════════════════════════════\n")
+
+let modelDir = "/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit"
+
+do {
+    print("Loading model...")
+    let engine = try E4BEngine(autoCompile: true)
+    let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 128)
+    print("✓ Model loaded (\(model.numHiddenLayers) layers)")
+    
+    let tokenizer = try TokenizerFactory.load(modelDir: modelDir)
+    let generator = StreamingGenerator(model: model, tokenizer: tokenizer, engine: engine)
+    
+    print("\nAttempting single token generation...")
+    let prompt = "Hello"
+    let config = GenerationConfig(maxTokens: 1, temperature: Float(0.7))
+    
+    let start = Date()
+    print("Calling generateComplete...")
+    let response = try generator.generateComplete(prompt: prompt, config: config)
+    let elapsed = Date().timeIntervalSince(start)
+    
+    print("\n✓ Generated in \(String(format: "%.3f", elapsed))s")
+    print("Response: '\(response)'")
+    
+} catch {
+    print("\n❌ Error: \(error)")
+}