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
1.5 KiB
Swift
51 lines
1.5 KiB
Swift
// Simple test to verify multimodal inference works
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import MarkBase
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let modelDir = "/Users/accusys/MarkBaseEngine/models/E4B-MarkBase"
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print("Loading model...")
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let engine = try E4BEngine(autoCompile: true)
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let model = try E4BModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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let tokenizer = try TokenizerFactory.load(modelDir: modelDir)
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let sampler = Sampler()
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print("✓ Model loaded")
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print(" Layers: \(model.numHiddenLayers)")
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print(" Vocab: \(model.vocabSize)")
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// Test text generation
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print("\n=== Test 1: Text Generation ===")
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let prompt = "Hello"
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let tokens = tokenizer.encode(text: prompt)
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print("Prompt: '\(prompt)'")
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print("Tokens: \(tokens)")
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// Reset KV cache
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for cache in model.kvCaches {
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cache.reset()
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}
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// Generate
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print("Generating...")
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var generated = tokens
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for i in 0..<10 {
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let pos = generated.count - 1
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let logits = try model.forward(tokenId: generated.last!, position: pos)
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let nextToken = sampler.sample(logits: logits, temperature: 0.7, topK: 50, topP: 0.95, filterUnusedTokens: true)
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generated.append(nextToken)
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let tokenStr = tokenizer.decode(tokens: [nextToken])
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print(" \(i+1): \(nextToken) '\(tokenStr)'")
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}
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print("\nOutput: '\(tokenizer.decode(tokens: generated))'")
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// Test multimodal
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print("\n=== Test 2: Multimodal ===")
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let mm = try MultimodalModel(modelDir: modelDir, engine: engine, maxContextLength: 512)
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print("Multimodal model loaded")
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if let tower = mm.visionTowerFull {
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print("Vision tower: \(tower.config.numHiddenLayers) layers")
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}
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