🎯 GPT-Neo Enhanced SPG: 450x Compression with Ablation Study

GPT-Neo Capabilities:

  • Max Sequence Length: 2048 tokens (full 2048 context)
  • Optimal Datasets: wikitext, openwebtext, pile, c4

Available Models:

  • GPT-Neo 125M: 12 layers, suitable for quick testing
  • GPT-Neo 1.3B: 24 layers, balanced size/performance
  • GPT-Neo 2.7B: 32 layers, largest open GPT-Neo model

STRICT COMPLIANCE MODE:

  • ✅ NO hardcoding - All from config
  • ✅ NO estimations - Measured only
  • ✅ NO fallbacks - Fail fast
  • ✅ NO fake results - Reproducible
  • ✅ Clean code - Full validation
  • ✅ Hardware validation - GPU memory checked
  • 🔬 NEW: Component Ablation Study
GPT-Neo Model Variant
Compression Methods
128 2048
5 50
1 5
Dataset
0.85 0.99
0.5 5
5 50
5 50
10 200
100 500
100 500
Head Retention
Magnitude Threshold

GPT-Neo Specific Settings:
1 48
0 8

405x+ Compression Settings (adjusted for GPT-Neo):
0.0001 0.001
0.0001 0.001

Ablation Study will test:
  • Baseline (no compression)
  • Stage 1 only
  • Stage 2 only
  • No head compression
  • No adaptive decomposition
  • No hybrid sparse attention
  • No SnapKV++
  • Conservative precision levels
  • Conservative magnitude threshold
  • No recent window protection
  • Reduced FP16 reserved heads
3 10
1 3
1 64
0 1
0.0001 0.01
1 16
1 10
50 600
100 1000

Compression Trade-off Analysis:
3 8

GPT-Neo 450x Compression Results

🔬 Ablation Study Details

Component Analysis: The ablation study systematically tests each component's contribution to achieving 450× compression:

  • Stage 1 (Permanent Eviction): Tests SnapKV++ and magnitude-guided token selection
  • Stage 2 (Multi-dimensional): Tests hybrid sparse attention and head compression
  • Precision Levels: Compares aggressive INT4 floor vs conservative FP16/INT8
  • Magnitude Thresholds: Tests extreme (0.1%) vs conservative (1%) thresholds
  • Position Awareness: Tests impact of recent window and sink token protection
  • Head Selection: Tests reserved FP16 heads for critical attention patterns

Metrics Evaluated:

  • Compression ratio achievement
  • Generation perplexity degradation
  • Memory reduction percentage
  • Decode speedup factor
  • End-to-end throughput gain
  • Component importance ranking

📬 GPT-Neo Architecture Details

Model Specifications:

  • GPT-Neo 125M: 12 layers, 768 hidden dim, 12 heads
  • GPT-Neo 1.3B: 24 layers, 2048 hidden dim, 16 heads
  • GPT-Neo 2.7B: 32 layers, 2560 hidden dim, 20 heads
  • Maximum Context: 2048 tokens (full 2048)

Memory Requirements:

  • 125M: Minimum 1GB VRAM
  • 1.3B: Minimum 6GB VRAM
  • 2.7B: Minimum 12GB VRAM (16GB+ recommended)

📦 Proving Protocol Features

Attestable Proof Bundle (.zip) contains:

  • Full environment and configuration
  • Per-sample raw measurements
  • Layer-level compression fingerprints
  • Exact package versions for reproducibility
  • Ablation study results (if enabled)

Verification:

  • Recomputes summary from raw records
  • Validates compression ratio achievement
  • Checks numerical tolerances
  • Hard-fails in CI if verification fails

This ensures research-grade reproducibility on GPT-Neo models with full 2048 token context and component analysis.