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
82 lines
4.0 KiB
Swift
82 lines
4.0 KiB
Swift
import XCTest
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@testable import MarkBase
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class Model12BVisionCheckTest: XCTestCase {
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func test12BVisionWeights() throws {
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" 12B Standard Vision Weights 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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// Check shard 2 (VisionTower12B.load uses model-00002-of-00002.safetensors)
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let shard2Path = "\(modelPath)/model-00002-of-00002.safetensors"
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guard FileManager.default.fileExists(atPath: shard2Path) else {
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print("⚠ Shard 2 not found")
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return
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}
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let reader = try SafeTensorsReader(path: shard2Path)
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let tensors = reader.allTensors
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print("1. Shard 2 Total Tensors: \(tensors.count)")
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// Check vision_embedder weights
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print("\n2. Vision Embedder Weights:")
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let visionEmbedderTensors = tensors.filter { $0.name.contains("vision_embedder") }
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print(" vision_embedder tensors: \(visionEmbedderTensors.count)")
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for tensor in visionEmbedderTensors {
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print(" \(tensor.name): shape=\(tensor.shape), dtype=\(tensor.dtype)")
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}
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// Check embed_vision weights
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print("\n3. Embed Vision Weights:")
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let embedVisionTensors = tensors.filter { $0.name.contains("embed_vision") }
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print(" embed_vision tensors: \(embedVisionTensors.count)")
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for tensor in embedVisionTensors {
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print(" \(tensor.name): shape=\(tensor.shape), dtype=\(tensor.dtype)")
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}
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// Check audio weights
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print("\n4. Audio Weights:")
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let audioTensors = tensors.filter { $0.name.contains("audio") }
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print(" audio tensors: \(audioTensors.count)")
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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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// Summary
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print("\n═══════════════════════════════════════════════════════════════════")
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print(" Vision Weights Summary")
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print("═══════════════════════════════════════════════════════════════════\n")
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if visionEmbedderTensors.count > 0 {
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print("✓ 12B HAS VISION EMBEDDER: \(visionEmbedderTensors.count) tensors")
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} else {
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print("✗ 12B NO VISION EMBEDDER")
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}
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if embedVisionTensors.count > 0 {
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print("✓ 12B HAS EMBED VISION: \(embedVisionTensors.count) tensors")
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} else {
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print("✗ 12B NO EMBED VISION")
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}
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if audioTensors.count > 0 {
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print("✓ 12B HAS AUDIO: \(audioTensors.count) tensors")
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} else {
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print("✗ 12B NO AUDIO")
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}
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print("\nVision Capability: \(visionEmbedderTensors.count > 0 && embedVisionTensors.count > 0 ? "YES ✓" : "NO ✗")")
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print("Audio Capability: \(audioTensors.count > 0 ? "YES ✓" : "NO ✗")")
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print("\n═══════════════════════════════════════════════════════════════════")
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}
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} |