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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final class G12BTextGenerationTests: XCTestCase {
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func testTextGeneration() throws {
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print("\n═══════════════════════════════════════")
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print(" 12B Text Generation Test")
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print("═══════════════════════════════════════\n")
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let modelDir = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
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print("Step 1: Load tokenizer...")
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// Simple tokenizer test - check if tokenizer.json exists
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let tokenizerPath = modelDir + "/tokenizer.json"
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let tokenizerExists = FileManager.default.fileExists(atPath: tokenizerPath)
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print(" Tokenizer exists: \(tokenizerExists)")
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if !tokenizerExists {
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print(" ⚠️ No tokenizer found - using simple token IDs\n")
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}
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print("Step 2: Initialize engine and model...")
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let engine = try MarkBaseEngine(autoCompile: true)
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let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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print(" ✓ Model loaded\n")
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print("Step 3: Test generation with simple prompts...")
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// Test 1: Generate from token 0 (usually special token)
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print("\nTest 1: Start token (ID=0)")
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var generatedTokens: [Int] = []
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let startToken = 0
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for position in 0..<10 {
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let logits = try model.forward(tokenId: startToken, position: position)
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// Find max logit (greedy sampling)
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var maxLogit = logits[0]
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var maxIdx = 0
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for i in 1..<logits.count {
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if logits[i] > maxLogit {
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maxLogit = logits[i]
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maxIdx = i
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}
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}
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generatedTokens.append(maxIdx)
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print(" Position \(position): token=\(maxIdx), logit=\(maxLogit)")
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}
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print(" Generated tokens: \(generatedTokens)")
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// Test 2: Different start token
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print("\nTest 2: Token ID=1")
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generatedTokens = []
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for position in 0..<10 {
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let logits = try model.forward(tokenId: 1, position: position)
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var maxLogit = logits[0]
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var maxIdx = 0
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for i in 1..<logits.count {
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if logits[i] > maxLogit {
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maxLogit = logits[i]
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maxIdx = i
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}
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}
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generatedTokens.append(maxIdx)
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}
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print(" Generated tokens: \(generatedTokens)")
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// Test 3: Sequence generation
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print("\nTest 3: Sequence generation (different tokens per position)")
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var sequence: [Int] = [1, 2, 3, 4, 5] // Start sequence
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print(" Input sequence: \(sequence)")
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for i in 0..<5 {
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let logits = try model.forward(tokenId: sequence[i], position: i)
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var maxLogit = logits[0]
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var maxIdx = 0
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for j in 1..<logits.count {
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if logits[j] > maxLogit {
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maxLogit = logits[j]
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maxIdx = j
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}
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}
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sequence.append(maxIdx)
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}
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print(" Extended sequence: \(sequence)")
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print("\n═══════════════════════════════════════")
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print("✓ Text generation test passed")
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print("═══════════════════════════════════════\n")
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// Verify diversity - tokens should not all be the same
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let uniqueTokens = Set(generatedTokens)
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XCTAssertGreaterThan(uniqueTokens.count, 1, "Generated tokens should have diversity")
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}
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func testE4BInference() throws {
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print("\n═══════════════════════════════════════")
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print(" E4B-MarkBase Inference Test")
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print("═══════════════════════════════════════\n")
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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print("Step 1: Load engine...")
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let engine = try MarkBaseEngine(autoCompile: true)
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print(" ✓ Engine initialized\n")
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print("Step 2: Load tokenizer...")
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let tokenizer = try TokenizerFactory.load(modelDir: modelDir)
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print(" ✓ Tokenizer loaded: vocabSize=\(tokenizer.vocabSize)\n")
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print("Step 3: Load model...")
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let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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print(" ✓ Model loaded: hiddenSize=\(model.hiddenSize), layers=\(model.numHiddenLayers)\n")
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// Debug: check logits at position 0
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print("Debug: Single token test (BOS at pos 0)")
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let logits0 = try model.forward(tokenId: 2, position: 0)
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print(" logits count: \(logits0.count)")
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print(" min: \(logits0.min() ?? 0), max: \(logits0.max() ?? 0)")
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let sorted0 = logits0.enumerated().sorted { $0.element > $1.element }
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print(" Top 10 tokens:")
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for (i, (idx, val)) in sorted0.prefix(10).enumerated() {
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let text = tokenizer.decode(tokens: [idx])
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print(" \(i+1). token \(idx) '\(text)': \(val)")
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}
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print("")
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print("Step 4: Create generator...")
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let generator = StreamingGenerator(model: model, tokenizer: tokenizer, engine: engine)
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print("Step 5: Text completion inference...")
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let prompt = "The capital of France is"
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print(" Prompt: \"\(prompt)\"")
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let config = GenerationConfig(maxTokens: 30, temperature: 1.0, topK: 40, topP: 0.9)
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let response = try generator.generateComplete(prompt: prompt, config: config)
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print(" Response: \"\(response)\"")
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XCTAssertFalse(response.isEmpty, "Response should not be empty")
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print(" ✓ Text generation complete\n")
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print("Step 6: Chat-style inference...")
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let chatPrompt = "<|turn>user\nWhat is 2+2?<turn|>\n<|turn>model\n"
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print(" Prompt: \"What is 2+2?\"")
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let chatResponse = try generator.generateComplete(prompt: chatPrompt, config: config)
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print(" Response: \"\(chatResponse)\"")
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XCTAssertFalse(chatResponse.isEmpty, "Chat response should not be empty")
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print(" ✓ Chat generation complete\n")
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print("Step 7: Chinese generation test...")
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let chinesePrompt = "<|turn>user\n用中文說你好<turn|>\n<|turn>model\n"
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print(" Prompt: \"用中文說你好\"")
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let chineseResponse = try generator.generateComplete(prompt: chinesePrompt, config: config)
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print(" Response: \"\(chineseResponse)\"")
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print("\n═══════════════════════════════════════")
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print("✓ E4B inference test passed")
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print("═══════════════════════════════════════\n")
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}
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func testGenerationQuality() throws {
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print("\n═══════════════════════════════════════")
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print(" 12B Generation Quality Test")
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print("═══════════════════════════════════════\n")
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let modelDir = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
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print("Step 1: Load model...")
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let engine = try MarkBaseEngine(autoCompile: true)
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let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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print(" ✓ Model loaded\n")
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print("Step 2: Check logits distribution...")
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// Generate logits for several tokens
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var allLogits: [[Float]] = []
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for tokenId in [0, 1, 100, 1000, 10000] {
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let logits = try model.forward(tokenId: tokenId, position: 0)
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allLogits.append(logits)
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// Stats
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let slice = logits[0..<1000]
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let minVal = slice.min() ?? 0
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let maxVal = slice.max() ?? 0
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let mean = slice.reduce(0, +) / Float(slice.count)
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print(" Token \(tokenId): min=\(minVal), max=\(maxVal), mean=\(mean)")
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}
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print("\nStep 3: Verify generation diversity...")
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// Generate 20 tokens and check if they're diverse
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var tokens: [Int] = []
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for i in 0..<20 {
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let logits = try model.forward(tokenId: i, position: i)
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// Top-5 sampling (not just greedy)
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var sortedLogits = logits.enumerated().sorted { $0.element > $1.element }
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let top5 = sortedLogits[0..<5].map { $0.offset }
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// Pick random from top-5 (simulate sampling)
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let selectedToken = top5[i % 5] // Deterministic for test
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tokens.append(selectedToken)
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}
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print(" Generated tokens: \(tokens)")
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let uniqueTokens = Set(tokens)
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print(" Unique tokens: \(uniqueTokens.count)")
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XCTAssertGreaterThan(uniqueTokens.count, 5, "Should have reasonable diversity")
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print("\n═══════════════════════════════════════")
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print("✓ Generation quality test passed")
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print("═══════════════════════════════════════\n")
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}
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}
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