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

LAYER_LOADING_ANALYSIS.md

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+# Layer Construction Performance Analysis
+
+## Current Observations
+
+From test results:
+```
+31B Total Load: 64s
+  Shard Loading: 1.3ms ✓✓✓ (极快)
+  Layer Construction: 63s ←  Bottleneck
+  
+  Layer Breakdown:
+  - 60 layers
+  - Each layer ~1.05s
+  - MoE layers: 128 experts × ~1.05s = 134.4s (major bottleneck!)
+  
+## Analysis
+
+The bottleneck is clearly in **layer construction**, not shard loading.
+
+**Key Operations**:
+1. **Weight Reading** - File IO operations
+   - Each weight requires reading from disk
+   - MoE: 128 experts × 3 files per expert
+   - Sequential reads are major bottleneck
+   
+2. **Buffer Creation** - Memory allocation
+   - MTLBuffer creation is relatively fast
+   - But needs to allocate large buffers
+   
+3. **Layer Initialization** - Object creation
+   - Creating E4BLayer objects
+   - Setting up quantization parameters
+   
+## Next Steps
+
+**Priority 1: Parallel Weight Loading**
+- Goal: Reduce weight loading from ~63s to ~20s
+- Approach:
+  1. Pre-identify all weights needed for layer construction
+  2. Use DispatchGroup to load weights in parallel
+  3. Store weights in temporary arrays
+  4. Build layers after all weights loaded
+  
+**Expected Improvement**: 3x speedup (63s → 20s)
+
+**Priority 2: MoE Expert Loading Optimization**
+- Goal: Reduce MoE expert loading from 134s to 30s
+- Approach:
+  1. Parallel expert loading
+  2. Batch expert creation
+  3. Optimize expert weight reading
+  
+**Expected Improvement**: 4.5x speedup (134s → 30s)
+
+**Priority 3: Memory Allocation Optimization**
+- Goal: Optimize MTLBuffer creation
+- Approach:
+  1. Pre-allocate large buffers
+  2. Reuse buffers across layers
+  3. Minimize buffer copies
+  
+**Expected Improvement**: 10-15% speedup
+
+## Implementation Priority
+
+**Phase 1** (Immediate): Parallel Weight Loading
+- Highest ROI (3x speedup)
+- Easiest to implement
+- Quick verification
+
+**Phase 2** (Short-term): MoE Expert Loading
+- Medium ROI (4.5x speedup)
+- More complex
+- Requires careful coordination
+
+**Phase 3** (Long-term): Memory Optimization
+- Lower ROI (10-15%)
+- Most complex
+- Requires architecture changes
+
+## Decision
+
+Starting with **Phase 1**: Parallel Weight Loading
+- Quick wins
+- Clear bottleneck
+- Easy to measure and verify