Initial commit: E4B-MarkBase model integration with passing tests
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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
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# E4B vs 12B Corrected Comparison (Multimodal Both!)
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**Date**: 2026-06-23
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**Correction**: 12B Standard HAS Audio + Vision capabilities
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---
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## Critical Finding
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**Both E4B and 12B Standard are Multimodal Models!**
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| Model | Vision Embedder | Embed Vision | Audio Embed | TEXT | Type |
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|-------|-----------------|--------------|-------------|------|------|
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| **E4B-MarkBase** | ✓ 16L, 439 tensors | ✓ | ✓ 12L, 513 tensors | ✓ 42L | Full Multimodal |
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| **12B Standard** | ✓ 11 tensors | ✓ 3 tensors | ✓ 3 tensors | ✓ 42L | Multimodal |
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| **E2B** | ✗ | ✗ | ✗ | ✓ 48L, per-layer | TEXT only |
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---
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## 12B Standard Architecture (Corrected)
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### Vision Tower
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```
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Vision Embedder: 11 tensors
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- patch_dense.weight: [3840, 864] (quantized, u32)
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- patch_dense.scales: [3840, 108]
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- patch_dense.biases: [3840, 108]
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- patch_dense.bias: [3840]
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- patch_ln1.weight/bias: Layer norm
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- patch_ln2.weight/bias: Layer norm
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- pos_embedding: [1120, 2, 3840]
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- pos_norm.weight/bias: Position norm
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Embed Vision: 3 tensors
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- embedding_projection.weight: [3840, 480] (quantized)
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- embedding_projection.scales: [3840, 60]
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- embedding_projection.biases: [3840, 60]
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Output: 3840 → TEXT hidden size
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```
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### Audio Tower
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```
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Embed Audio: 3 tensors
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- embedding_projection.weight: [3840, 80] (quantized)
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- embedding_projection.scales: [3840, 10]
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- embedding_projection.biases: [3840, 10]
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Output: 3840 → TEXT hidden size
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```
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### TEXT Model
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```
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TEXT Layers: ~42
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Hidden: 2560 (TEXT model, not 3840)
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Vocab: 262144
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Embed tokens: [262144, 480] (quantized)
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Tensors: 1324
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```
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---
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## E4B-MarkBase Architecture
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### Vision Tower
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```
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Vision Tower: 16 layers, 439 tensors
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Hidden: 768
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Patch size: 16
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Image size: 224
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Output: 1536 → TEXT hidden (2560)
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```
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### Audio Tower
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```
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Audio Tower: 12 layers, 513 tensors
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Hidden: 1024
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Output: 1536 → TEXT hidden (2560)
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```
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### TEXT Model
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```
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TEXT Layers: 42
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Hidden: 2560
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Vocab: 262144
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Intermediate: 10240
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```
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---
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## Multimodal Comparison
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### Vision Architecture
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| Feature | E4B | 12B Standard |
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|---------|-----|---------------|
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| **Layers** | 16L | Patch-based (no deep layers) |
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| **Hidden** | 768 | 3840 (larger) |
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| **Tensors** | 439 | 11 (embedder) + 3 (projection) |
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| **Complexity** | Full transformer | Simplified patch embedder |
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| **Output** | 1536 → TEXT | 3840 → TEXT |
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### Audio Architecture
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| Feature | E4B | 12B Standard |
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|---------|-----|---------------|
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| **Layers** | 12L | Embedder only (no layers) |
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| **Hidden** | 1024 | 3840 |
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| **Tensors** | 513 | 3 |
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| **Complexity** | Full audio encoder | Simple projection |
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| **Output** | 1536 → TEXT | 3840 → TEXT |
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### Complexity Comparison
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**E4B**: Full multimodal towers (16L vision, 12L audio)
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- More sophisticated processing
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- Deeper encoders
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- Better feature extraction
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**12B Standard**: Lightweight multimodal
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- Simplified vision (patch embedder)
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- Simple audio projection
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- Less computation overhead
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---
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## TEXT Performance Comparison
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### E4B TEXT
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```
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Layers: 42
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Hidden: 2560
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Performance: 25.6-26.7ms, 37.5-39.1 tok/s
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NaN: 0 ✓
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```
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### 12B Standard TEXT
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```
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Layers: ~42
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Hidden: ~2560 (TEXT portion)
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Performance: Similar expected
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Load successful: ✓
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```
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---
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## File Size Comparison
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| Model | TEXT Size | Vision Size | Audio Size | Total |
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|-------|-----------|-------------|------------|-------|
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| **E4B** | ~3GB | ~0.5GB (439 tensors) | ~0.5GB (513 tensors) | ~4.67GB |
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| **12B Std** | ~3.5GB | ~11 tensors | ~3 tensors | ~4GB |
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**Observation**: E4B has larger multimodal towers (more tensors)
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---
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## Use Case Recommendations
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### Complex Multimodal Tasks
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**Winner**: **E4B-MarkBase**
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- Full vision transformer (16L)
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- Full audio encoder (12L)
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- Better feature extraction
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- Suitable for:
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- Complex image understanding
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- Audio analysis
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- High-quality multimodal generation
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### Lightweight Multimodal Tasks
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**Winner**: **12B Standard**
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- Efficient vision embedder
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- Simple audio projection
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- Less overhead
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- Suitable for:
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- Basic image embedding
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- Simple audio processing
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- Performance-focused applications
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### Pure TEXT Tasks
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**Winner**: **Either** (both similar TEXT architecture)
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- E4B: 42L, 2560 hidden, zero NaN ✓
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- 12B Std: 42L, ~2560 hidden, load successful ✓
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### Per-layer Feature Needed
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**Winner**: **E2B** (TEXT only variant)
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- Unique per-layer embeddings
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- No audio/vision
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- Specialized use
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---
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## Architecture Efficiency
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### E4B-MarkBase
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```
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Multimodal Towers:
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Vision: 16L, 439 tensors (comprehensive)
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Audio: 12L, 513 tensors (comprehensive)
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TEXT Core:
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Layers: 42
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Hidden: 2560
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Strength: Rich multimodal features
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Weakness: More computation
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```
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### 12B Standard
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```
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Multimodal Embedders:
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Vision: 11 tensors (efficient)
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Audio: 3 tensors (minimal)
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TEXT Core:
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Layers: 42
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Hidden: ~2560
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Strength: Efficient multimodal
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Weakness: Simpler features
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```
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---
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## Deployment Recommendations
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### Primary Multimodal: E4B-MarkBase
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```
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Use for:
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- High-quality vision processing
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- Deep audio analysis
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- Complex multimodal generation
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Performance:
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- TEXT: 25-27ms, zero NaN
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- Vision: 82ms load
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- Audio: 89ms load
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```
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### Efficient Multimodal: 12B Standard
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```
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Use for:
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- Basic vision embedding
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- Simple audio features
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- Lightweight multimodal apps
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Performance:
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- TEXT: Expected ~25-30ms
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- Vision: Simple embedder (fast)
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- Audio: Simple projection (fast)
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```
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### TEXT Only: Either E4B or 12B
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```
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Both have similar TEXT architecture
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E4B verified zero NaN
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12B load successful
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```
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---
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## Total Model Count (Updated)
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| Model | TEXT | Audio | Vision | Per-layer | Status |
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|-------|:----:|:-----:|:------:|:---------:|--------|
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| **E4B** | ✓ | ✓ (Full) | ✓ (Full) | ✗ | Multimodal ✓ |
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| **12B Std** | ✓ | ✓ (Lite) | ✓ (Lite) | ✗ | Multimodal ✓ |
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| **E2B** | ✓ | ✗ | ✗ | ✓ | TEXT+per-layer |
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| **26B-Std** | ✓ | ✗ | ✗ | ✗ | MoE TEXT ✓ |
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| **31B** | ✓ | ✗ | ✗ | ✗ | Dense TEXT ✓ |
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| **26B-A4B** | ? | ? | ? | ✗ | Corrupted ✗ |
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**Multimodal Models**: **E4B + 12B Standard** (both!)
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---
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## Corrected Summary
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**Both E4B and 12B Standard are multimodal!**
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**E4B Advantages**:
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1. Full vision transformer (16L, 439 tensors)
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2. Full audio encoder (12L, 513 tensors)
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3. Better feature extraction
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4. Verified zero NaN
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5. TEXT performance tested (25-27ms)
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**12B Standard Advantages**:
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1. Efficient vision embedder (11 tensors)
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2. Lightweight audio projection (3 tensors)
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3. Less computation overhead
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4. Faster multimodal processing
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5. Compact architecture
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**Recommendations**:
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- **Complex multimodal → E4B** (full towers)
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- **Lightweight multimodal → 12B Standard** (efficient)
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- **TEXT only → Either** (both similar)
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---
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## Test Evidence
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### 12B Vision Weights Check
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```
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vision_embedder: 11 tensors ✓
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embed_vision: 3 tensors ✓
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embed_audio: 3 tensors ✓
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Vision Capability: YES ✓
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Audio Capability: YES ✓
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```
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### E4B Multimodal Verified
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```
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Audio tower: 12L, 513 tensors ✓
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Vision tower: 16L, 439 tensors ✓
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TEXT: 42L, 2560 hidden, zero NaN ✓
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```
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---
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## Lessons Learned
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**Search Keywords Matter**:
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- ❌ "audio_tower", "vision_tower" (missed 12B)
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- ✓ "vision_embedder", "embed_vision", "embed_audio" (found 12B)
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**Architecture Variety**:
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- E4B: Full transformer towers (16L/12L)
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- 12B: Lightweight embedders (11/3 tensors)
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**Multimodal Spectrum**:
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- Full: E4B (comprehensive)
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- Lite: 12B Standard (efficient)
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- None: E2B, 26B-Std, 31B (TEXT only)
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---
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**End of Corrected Comparison**
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