MarkBase Admin
ac75faa0cc
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
86 lines
4.1 KiB
Swift
86 lines
4.1 KiB
Swift
import XCTest
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@testable import MarkBase
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class Model12BArchitectureTest: XCTestCase {
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func testModel12BFullArchitecture() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" 12B Standard Complete Architecture Check")
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print("═══════════════════════════════════════════════════════════════════\n")
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let modelPath = "/Users/accusys/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit/snapshots/73bcf09092aa277861d5a191b989b666f7f32e8f"
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guard FileManager.default.fileExists(atPath: modelPath) else {
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print("⚠ Model not found")
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return
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}
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// Load tensors
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let index = try SafeTensorsIndex(modelDir: modelPath)
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var readers: [String: SafeTensorsReader] = [:]
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for shardFile in Set(index.weightMap.values) {
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readers[shardFile] = try SafeTensorsReader(path: "\(modelPath)/\(shardFile)")
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}
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let allTensors = readers.values.flatMap { $0.allTensors }
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print("1. Total Tensors: \(allTensors.count)")
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// Audio tower analysis
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print("\n2. Audio Tower:")
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let audioTensors = allTensors.filter { $0.name.contains("audio_tower") || $0.name.contains("audio_model") }
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print(" Audio tensors: \(audioTensors.count)")
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if !audioTensors.isEmpty {
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print(" Sample tensors:")
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for tensor in audioTensors.prefix(10) {
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print(" \(tensor.name): shape=\(tensor.shape)")
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}
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}
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// Vision tower analysis
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print("\n3. Vision Tower:")
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let visionTensors = allTensors.filter { $0.name.contains("vision_tower") || $0.name.contains("vision_model") }
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print(" Vision tensors: \(visionTensors.count)")
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if !visionTensors.isEmpty {
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print(" Sample tensors:")
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for tensor in visionTensors.prefix(10) {
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print(" \(tensor.name): shape=\(tensor.shape)")
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}
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}
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// TEXT model analysis
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print("\n4. TEXT Model:")
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let textTensors = allTensors.filter { $0.name.contains("language_model") || $0.name.contains("layers.") }
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print(" TEXT-related tensors: \(textTensors.count)")
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// Embed tokens
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let embedTokens = allTensors.filter { $0.name.contains("embed_tokens") }
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print(" Embed tokens: \(embedTokens.count)")
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for tensor in embedTokens.prefix(5) {
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print(" \(tensor.name): shape=\(tensor.shape)")
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}
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// Summary
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Architecture Summary")
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print("═══════════════════════════════════════════════════════════════════\n")
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if audioTensors.count > 0 {
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print("✓ 12B HAS AUDIO TOWER: \(audioTensors.count) tensors")
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} else {
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print("✗ 12B NO AUDIO TOWER")
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}
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if visionTensors.count > 0 {
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print("✓ 12B HAS VISION TOWER: \(visionTensors.count) tensors")
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} else {
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print("✗ 12B NO VISION TOWER")
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}
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print("✓ TEXT Model: \(textTensors.count) tensors")
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print("\nModel Type: \(audioTensors.count > 0 || visionTensors.count > 0 ? "Multimodal" : "Pure TEXT")")
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print("\n═══════════════════════════════════════════════════════════════════")
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}
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} |