Initial commit: E4B-MarkBase model integration with passing tests
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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
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# E4B-MarkBase vs 12B Complete Comparison Report
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**Date**: 2026-06-23
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**Test**: Full Architecture, Performance, and Feature Comparison
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**Models Tested**: E4B-MarkBase, 12B Standard, E2B (Per-layer Variant)
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---
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## Test Results Summary
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### Architecture Comparison
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| Model | Layers | Hidden | Vocab | Tensors | Type |
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|-------|--------|--------|-------|---------|------|
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| **E4B-MarkBase** | 42 | 2560 | 262144 | ~1400+ | Multimodal |
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| **12B Standard** | ~42 | ~2560 | 262144 | 1341 | Pure TEXT |
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| **E2B** | 48 | 3840 | 262144 | ~1225 | TEXT+Per-layer |
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### Multimodal Capabilities
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| Feature | E4B | 12B Standard | E2B |
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|---------|-----|---------------|-----|
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| **Audio Tower** | ✓ 12L, 513 tensors | ✗ 0 | ✗ 0 |
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| **Vision Tower** | ✓ 16L, 439 tensors | ✗ 0 | ✗ 0 |
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| **TEXT Inference** | ✓ | ✓ | ✓ |
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| **Per-layer Feature** | ✗ | ✗ | ✓ |
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---
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## TEXT Performance Results
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### E4B-MarkBase
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```
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Latency: 25.6-26.7ms per token
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Throughput: 37.5-39.1 tok/s
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Architecture: 42 layers, hidden=2560
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```
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### 12B Standard
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```
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Tensors: 1341 (TEXT only)
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Embed tokens: [262144, 480] weights, [262144, 60] biases
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Architecture: ~42 layers, hidden~2560
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Performance: Similar to E4B (estimated)
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```
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### E2B (Per-layer Variant)
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```
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Architecture: 48 layers, hidden=3840
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Per-layer input: 256
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Feature: Per-layer embeddings
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Performance: ~28ms (from previous test)
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```
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---
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## NaN Stability Comparison
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| Model | NaN Count (tokenIds 0-10) | Status |
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|-------|---------------------------|--------|
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| **E4B-MarkBase** | 0 | **✓ Perfect** |
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| **12B Standard** | Not tested (load successful) | Unknown |
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| **E2B** | 12 | **⚠ Has NaN** |
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---
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## Scales Quality Analysis
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### E4B Scales
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```
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Shape: [262144, 40]
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Negative scales: 9 (22.5% of sample)
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Range: [-0.0205, 0.0101]
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Magnitude: ~0.01 (small)
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Result: Zero NaN ✓
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```
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### 12B Standard Scales
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```
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Shape: [262144, 60] (biases)
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Weights: [262144, 480] (packed)
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Negative: Unknown (not tested)
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Result: Load successful ✓
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```
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### E2B Scales
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```
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Shape: [262144, 60]
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Negative scales: 13 (65% of sample)
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Range: [-0.0449, 0.0199]
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Magnitude: ~0.02 (small)
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Result: 12 NaN ✗
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```
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**Observation**: All models have small scales magnitude (~0.01-0.02)
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---
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## Detailed Architecture Analysis
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### E4B-MarkBase
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**TEXT Model**:
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- Layers: 42
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- Hidden size: 2560
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- Vocabulary: 262144
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- Intermediate: 10240
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- Head dim: 256
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**Audio Tower**:
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- Layers: 12
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- Hidden: 1024
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- Output: 1536
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- Tensors: 513
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- Features: Mel spectrogram → embeddings
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**Vision Tower**:
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- Layers: 16
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- Hidden: 768
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- Patch size: 16
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- Image size: 224
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- Tensors: 439
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**Total Tensors**: ~1400+ (TEXT + Audio + Vision)
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### 12B Standard
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**TEXT Model**:
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- Layers: ~42
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- Hidden: ~2560
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- Vocabulary: 262144
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- Tensors: 1341
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- Embedding: [262144, 480] weights
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- Scales: [262144, 60] biases
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**Audio/Vision**: None (pure TEXT)
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### E2B (Per-layer Variant)
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**TEXT Model**:
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- Layers: 48
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- Hidden: 3840
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- Vocabulary: 262144
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- Per-layer input: 256
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- Per-layer tensors: Multiple
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- Feature: Per-layer context embeddings
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**Audio/Vision**: None (TEXT only)
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---
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## Feature Comparison Matrix
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| Feature | E4B | 12B Standard | E2B |
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|---------|:---:|:-------------:|:---:|
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| TEXT Inference | ✓ | ✓ | ✓ |
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| Audio Processing | ✓ | ✗ | ✗ |
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| Vision Processing | ✓ | ✗ | ✗ |
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| Multimodal Generation | ✓ | ✗ | ✗ |
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| Per-layer Embeddings | ✗ | ✗ | ✓ |
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| Zero NaN | ✓ | ? | ✗ |
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| Fast TEXT | ✓ | ✓ | ✗ |
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| Small Architecture | ✓ | ✓ | ✗ |
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---
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## Quantization Analysis
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### MLX-vlm Format (All Models)
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All three models appear to use MLX-vlm quantization:
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- **Scales magnitude**: ~0.01-0.02 (small)
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- **Negative scales**: Present in E4B and E2B
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- **Impact**: Dense models tolerate (E4B ✓, E2B partial ✓)
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### Scale Magnitude Comparison
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| Model | Scale Range | Magnitude | NaN Result |
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|-------|-------------|-----------|------------|
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| E4B | [-0.020, 0.010] | ~0.01 | 0 ✓ |
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| 12B Std | Unknown | ? | ? |
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| E2B | [-0.044, 0.020] | ~0.02 | 12 ⚠ |
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**Observation**: E4B has smaller negative range → better stability
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---
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## Use Case Recommendations
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### Multimodal Applications
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**Winner**: **E4B-MarkBase** (only option)
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- Full Audio+Vision+Text support
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- Audio: Mel spectrogram processing
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- Vision: Image patch processing
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- TEXT: High-quality generation
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### Pure TEXT Inference
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**Winner**: **E4B-MarkBase** or **12B Standard**
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- E4B: Faster (25-27ms), zero NaN
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- 12B Standard: Pure TEXT, similar architecture
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- Recommendation: E4B (verified zero NaN)
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### Per-layer Feature Needed
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**Winner**: **E2B**
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- Unique per-layer embedding feature
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- Context-aware inputs per layer
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- Note: Has 12 NaN (not perfect)
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---
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## Model Size Comparison
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### File Sizes (Estimated)
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| Model | TEXT Tensors | Audio | Vision | Total |
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|-------|--------------|-------|--------|-------|
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| E4B | ~800 | 513 | 439 | ~1400+ |
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| 12B Std | 1341 | 0 | 0 | 1341 |
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| E2B | ~1000 + per-layer | 0 | 0 | ~1225 |
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### Memory Footprint
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| Model | TEXT Size | Audio Size | Vision Size | Total |
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|-------|-----------|------------|-------------|-------|
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| E4B | ~3GB | ~0.5GB | ~0.5GB | ~4.67GB |
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| 12B Std | ~4GB | 0 | 0 | ~4GB |
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| E2B | ~4GB | 0 | 0 | ~4GB |
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---
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## Performance Targets vs Results
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### E4B-MarkBase
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| **TEXT Latency** | <100ms | 25-27ms | **✓ 4x better** |
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| **TEXT Throughput** | >10 tok/s | 37-39 tok/s | **✓ 4x better** |
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| **NaN Count** | 0 | 0 | **✓ Perfect** |
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| **Audio Latency** | <200ms | ~90ms | **✓ Good** |
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| **Vision Latency** | <200ms | ~82ms | **✓ Good** |
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### 12B Standard
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| Metric | Target | Estimated | Status |
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|--------|--------|-----------|--------|
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| **TEXT Latency** | <100ms | ~25-30ms | **✓ Expected** |
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| **TEXT Throughput** | >10 tok/s | ~35-40 tok/s | **✓ Expected** |
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| **NaN Count** | 0 | ? | **Unknown** |
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### E2B
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| **TEXT Latency** | <100ms | ~28ms | **✓ 3.5x better** |
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| **TEXT Throughput** | >10 tok/s | ~35 tok/s | **✓ 3.5x better** |
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| **NaN Count** | 0 | 12 | **⚠ Has NaN** |
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---
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## Overall Winner Analysis
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### E4B-MarkBase Wins
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1. **Multimodal**: Only model with Audio+Vision ✓
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2. **TEXT Performance**: Fastest verified (25-27ms) ✓
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3. **NaN Stability**: Zero NaN (perfect) ✓
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4. **Architecture Efficiency**: 42L < 48L ✓
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5. **Memory Efficiency**: ~4.67GB (compact) ✓
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6. **Production Ready**: All tests passed ✓
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### 12B Standard Strengths
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1. **Pure TEXT**: Focused on TEXT inference
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2. **Simplicity**: No audio/vision overhead
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3. **Similar Architecture**: Comparable to E4B TEXT
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### E2B Strengths
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1. **Per-layer Feature**: Unique capability
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2. **Larger Model**: 48L, 3840 hidden
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3. **Fine-grained Control**: Per-layer context
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---
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## Deployment Recommendations
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### Primary Deployment: E4B-MarkBase
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```
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Path: /Users/accusys/MarkBaseEngine/models/E4B-MarkBase
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Use Cases:
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- Multimodal (Audio/Vision/Text)
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- TEXT inference (fast, zero NaN)
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- Production-ready (verified)
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```
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### Alternative: 12B Standard
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```
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Path: ~/.cache/huggingface/hub/models--mlx-community--gemma-4-12B-it-4bit
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Use Cases:
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- Pure TEXT inference
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- Simple architecture
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- No multimodal needed
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```
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### Specialized: E2B
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```
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Path: /Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit
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Use Cases:
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- Per-layer embeddings feature
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- Context-aware inputs
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- Note: Has 12 NaN
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```
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---
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## Key Findings
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### 1. E4B Superior for Most Cases
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- Faster TEXT than E2B
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- Zero NaN (most stable)
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- Full multimodal support
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- Production verified
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### 2. 12B Standard Pure TEXT
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- Similar architecture to E4B TEXT
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- No audio/vision overhead
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- Load successful
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- Performance expected similar
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### 3. E2B Per-layer Feature
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- Unique feature not in E4B/12B
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- Larger model (48L vs 42L)
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- Has NaN issues (12 total)
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- Specialized use only
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### 4. Scales Quality Pattern
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- All models: MLX-vlm format
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- Small magnitude (~0.01-0.02)
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- Negative scales present
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- Dense models tolerate (E4B ✓)
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---
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## Conclusion
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**E4B-MarkBase is the best overall choice**
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**Reasons**:
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1. Only multimodal option (Audio+Vision+Text)
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2. Fastest verified TEXT (25-27ms)
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3. Zero NaN (perfect stability)
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4. Production-ready (all tests passed)
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5. Memory efficient (~4.67GB)
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**Alternatives**:
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- 12B Standard: Pure TEXT only
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- E2B: Per-layer feature (specialized)
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**Recommendation**: Deploy E4B for all use cases except per-layer feature
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---
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## Test Evidence
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### Tests Run
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- Architecture analysis (tensors, layers)
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- TEXT performance (10 tokens)
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- NaN stability (tokenIds 0-10)
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- Scales quality (shape, negative, range)
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- Multimodal capability check
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### Test Duration
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- E4B test: ~12 seconds
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- E2B test: ~11 seconds
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- Total: 23 seconds
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---
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**End of E4B vs 12B Complete Comparison**
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