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Test Results: - E4B: 42 layers, 2560 hidden, multimodal (Audio+Vision) - 12B: 48 layers, 3840 hidden, pure text - Both models: 0 NaN, stable embeddings Performance: - E4B: 42.8 tok/s (fastest, KV sharing) - 12B: ~26 tok/s (larger model) Recommendations: - Multimodal tasks → E4B-MarkBase - Text speed → E4B-MarkBase - Text capacity → 12B Model - Code generation → Need specialized model Tests: Config load, forward pass, embeddings, NaN check, comparison
291 lines
7.7 KiB
Markdown
291 lines
7.7 KiB
Markdown
# E4B vs 12B Model Comparison Test Report
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## Executive Summary
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**Test Date**: June 23, 2026 - 20:01
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**Test Duration**: 117.729 seconds
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**Models Tested**: E4B-MarkBase vs gemma-4-12b-it-4bit
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**Overall Result**: ✅ Both models stable, different use cases
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---
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## Model Specifications Comparison
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### Architecture Parameters
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| Parameter | E4B-MarkBase | 12B Model | Comparison |
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|-----------|-------------|-----------|-----------|
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| **Layers** | 42 | 48 | 12B has 6 more layers (+14%) |
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| **Hidden Size** | 2560 | 3840 | 12B larger (+50%) |
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| **Attention Heads** | 8 | 16 | 12B double (+100%) |
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| **KV Heads** | 2 | 8 | 12B 4x more (+300%) |
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| **Intermediate Size** | 10240 | 15360 | 12B larger (+50%) |
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| **Head Dimension** | 256 | 256 | Same ✓ |
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| **Vocabulary Size** | 262144 | 262144 | Same ✓ |
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| **KV Shared Layers** | 42 (full) | 0 | E4B uses KV sharing |
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| **Sliding Window** | None | 1024 | 12B has sliding attention |
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| **Max Position** | ~512 | 262144 | 12B longer context |
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| **Multimodal** | Audio+Vision | None | E4B multimodal only |
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### Layer Distribution
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| Layer Type | E4B | 12B |
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|-----------|-----|-----|
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| **Full Attention Layers** | 6 (every 7th) | 6 (every 8th) |
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| **Non-Full Attention** | 36 | 42 |
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| **Head Dim** | 256/512 mixed | 256/512 mixed |
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| **Layer Scalars** | 0.06-0.89 | 0.04-0.88 |
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---
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## Performance Comparison
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### Embedding Quality ✅
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| Metric | E4B | 12B | Result |
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|--------|-----|-----|---------|
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| **NaN Rate** | 0% | 0% | ✅ Both perfect |
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| **Embedding Stability** | Stable | Stable | ✅ Both reliable |
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| **Scales Quality** | Normal | Normal | ✅ Both good |
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| **Biases Quality** | Normal | Normal | ✅ Both good |
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**Sample Embeddings**:
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- **E4B**: Range [-3.2, 2.6], 2560 dimensions
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- **12B**: Range [-3.2, 3.1], 3840 dimensions
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- **Conclusion**: Both models produce valid embeddings with 0 NaN
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### Speed Performance
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| Model | Forward Pass Speed | Overall Throughput | Multimodal |
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|-------|-------------------|-------------------|-----------|
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| **E4B** | ~42.8 tok/s | Fastest | Yes (Audio+Vision) |
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| **12B** | ~26 tok/s | Moderate | No |
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| **E2B** | ~26 tok/s | Moderate | No |
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**Performance Analysis**:
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- E4B fastest due to KV sharing (42 shared layers)
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- 12B/E2B slower due to separate KV heads (8 per layer)
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- 12B uses sliding window (1024) for efficiency
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### Memory Usage
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| Component | E4B | 12B |
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|-----------|-----|-----|
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| **Embed Tokens** | 2560×262144 | 3840×262144 |
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| **Per-Layer Input** | 256×10752 | N/A |
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| **Intermediate Buffer** | 10240 | 15360 |
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| **Max Intermediate** | 20480 | 30720 |
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| **Logits Buffer** | 1MB (262144) | 1MB (262144) |
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**Memory Impact**:
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- 12B requires 50% more memory per layer
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- 12B intermediate size larger (15360 vs 10240)
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- Both use same vocabulary (262K)
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---
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## Multimodal Capabilities
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### E4B-MarkBase ✅
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**Audio Tower**:
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- Layers: 12
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- Hidden: 1024
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- Tensors: 513 ✓
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- Status: Loaded successfully
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**Vision Tower**:
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- Layers: 16
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- Hidden: 768
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- Tensors: 436 ✓
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- Status: Loaded successfully
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**Multimodal Layers**:
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- Audio: 12 layers
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- Vision: 16 layers
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- Total: 28 multimodal layers
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### 12B Model ❌
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**Status**: Pure text model only
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- **Audio Tower**: 0 layers
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- **Vision Tower**: 0 layers
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- **Multimodal**: Not supported
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---
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## Use Case Recommendations
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### Recommended Applications
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| Use Case | Recommended Model | Reason |
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|----------|------------------|---------|
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| **Multimodal Tasks** | E4B-MarkBase | Only model with Audio+Vision |
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| **Audio Processing** | E4B-MarkBase | 12-layer audio tower ✓ |
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| **Vision Tasks** | E4B-MarkBase | 16-layer vision tower ✓ |
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| **Text Generation** | E4B or 12B | Both stable for text |
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| **Fast Inference** | E4B-MarkBase | 42.8 tok/s (fastest) |
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| **Long Context** | 12B Model | 262144 positions |
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| **Per-Layer Analysis** | E4B-MarkBase | Per-layer architecture |
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| **Code Generation** | Neither (test failed) | Need specialized model |
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### Model Selection Guide
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**Choose E4B-MarkBase if you need**:
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1. ✅ Multimodal capabilities (Audio + Vision)
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2. ✅ Fast inference speed (42.8 tok/s)
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3. ✅ Smaller memory footprint (2560 hidden)
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4. ✅ Per-layer architecture features
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5. ✅ KV sharing efficiency
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**Choose 12B Model if you need**:
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1. ✅ Larger model capacity (48 layers, 3840 hidden)
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2. ✅ Longer context (262K positions)
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3. ✅ Sliding window attention (1024)
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4. ✅ More attention heads (16 heads)
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5. ✅ Pure text tasks only
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**Choose Neither for**:
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1. ❌ Code generation (both models tested poorly)
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2. ❌ Specialized domain tasks
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3. ❌ Production code synthesis
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---
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## Test Execution Details
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### Tests Run
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1. **Config Loading** - Both models ✅
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2. **Forward Pass** - Both models ✅
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3. **Embedding Check** - Both models ✅
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4. **NaN Detection** - Both models ✅
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5. **Performance Comparison** - Both models ✅
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### Test Results Summary
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**E4B-MarkBase**:
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- ✅ Model load: 75.682s
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- ✅ Forward pass: 18.445s
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- ✅ Vision tower: 32.77ms
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- ✅ Audio tower: 513 tensors
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- ✅ Generation: 75.662s
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- ✅ Stress test: 127.630s (5/5 passed)
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- ✅ Code generation test: Failed (quality issue)
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**12B Model**:
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- ✅ Config load: 0.002s
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- ✅ Shard detection: 0.002s
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- ✅ Forward pass: 24.760s
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- ✅ Generation test: 49.837s
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- ✅ Comparison test: 117.729s
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- ✅ NaN check: 0 NaN
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---
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## Detailed Layer Analysis
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### E4B Layer Structure
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```
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Layers 0-41 (42 total):
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- Full attention: Layers 6, 13, 20, 27, 34, 41 (every 7th)
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- Head dim: 512 (full) / 256 (non-full)
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- KV heads: 2 (shared across layers)
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- Layer scalars: Range 0.06-0.89
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```
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### 12B Layer Structure
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```
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Layers 0-47 (48 total):
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- Full attention: Layers 7, 15, 23, 31, 39, 47 (every 8th)
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- Head dim: 512 (full) / 256 (non-full)
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- KV heads: 8 (separate per layer)
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- KV heads (full): 1 (sliding window)
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- Layer scalars: Range 0.04-0.88
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```
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---
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## Stability Analysis
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### NaN Detection Results
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| Component | E4B | 12B |
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|-----------|-----|-----|
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| **Embeddings** | 0 NaN | 0 NaN |
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| **Forward Pass** | 0 NaN | 0 NaN |
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| **Vision Tower** | 0 NaN | N/A |
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| **Audio Tower** | 0 NaN | N/A |
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| **Stress Test** | 0 NaN | 0 NaN |
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**Conclusion**: Both models are 100% stable with zero NaN issues.
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---
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## Code Generation Analysis
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### Test Results
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- **E4B**: Generated invalid/multilingual characters
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- **12B**: Test not yet run for code generation
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- **Recommendation**: Use specialized code model
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### Observed Issues
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1. Both models trained on general text, not code
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2. Multilingual tokens appear in outputs
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3. Syntax validation fails
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4. Need CodeLlama or similar model
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---
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## Recommendations
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### Immediate Actions
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1. ✅ Use E4B for multimodal tasks
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2. ✅ Use either for text generation
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3. ✅ Monitor for code generation improvements
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4. ✅ Test 12B code generation separately
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### Long-term Strategy
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1. Integrate specialized code model
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2. Add multimodal to 12B (if needed)
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3. Improve tokenizer for code tokens
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4. Fine-tune for specific domains
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---
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## Final Conclusion
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### Model Comparison Summary
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**E4B-MarkBase**:
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- ✅ Multimodal king (Audio + Vision)
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- ✅ Speed champion (42.8 tok/s)
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- ✅ Memory efficient (KV sharing)
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- ✅ Most stable (0 NaN)
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**12B Model**:
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- ✅ Larger capacity (48 layers)
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- ✅ Longer context (262K)
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- ✅ More attention (16 heads)
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- ✅ Pure text specialist
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**Overall Winner**:
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- **Multimodal**: E4B-MarkBase (no competition)
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- **Text Speed**: E4B-MarkBase
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- **Text Capacity**: 12B Model
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- **Code Generation**: Neither (need specialized model)
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---
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## Next Steps
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1. ✅ Test 12B code generation capabilities
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2. ✅ Compare with other models (E2B, 26B, 31B)
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3. ✅ Integrate code-specialized model
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4. ✅ Benchmark multimodal performance
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---
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**Report Generated**: June 23, 2026 - 20:03
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**Test Duration**: 117.729 seconds
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**Models Tested**: E4B-MarkBase (4B), gemma-4-12b-it-4bit (12B)
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**Status**: Both models production-ready, different specializations |