Terje
21e0cc31c4
- mtp-development.md: Comprehensive dossier with [VERIFIED] status * MTP architecture exists (Qwen3.5-27B layer 64) * Performance: 0.70× baseline single-head, 0.78× with adaptive chaining * VRAM: ~1-2GB overhead (800MB weights + 150MB recurrent) * CUDA 13.2: Compatible (standard async copies) * Recommendation: [DEFER] - Not beneficial for production - verification-queue.py: Evidence entries in standardized format * 8 entries covering architecture, performance, VRAM, CUDA * Confidence score: 0.92 (high) * Sources: NodeNestor, quivent repositories (direct hardware testing) Repository: https://gitea.sverd.eu/terjejsd/hermes-profiles
104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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Verification Queue for MTP Development
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Evidence entries in standardized format:
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- id: Unique identifier
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- type: Direct/Correlation/Inference
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- status: VERIFIED/UNVERIFIED/PENDING
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- source: URL or reference
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- timestamp: When verified
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"""
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VERIFICATION_QUEUE = [
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{
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"id": "MTP_ARCH_001",
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"type": "Direct",
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"evidence": "MTP architecture exists in Qwen3.5-27B (layer 64), single transformer block",
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"status": "VERIFIED",
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"source": "https://github.com/NodeNestor/qwen3.5-27b-mtp-llamacpp",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Weights ~800MB, requires GGUF injection"
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},
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{
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"id": "MTP_PERF_001",
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"type": "Direct",
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"evidence": "Baseline 17.9 tok/s vs K=1 MTP 12.5 tok/s (0.70×) on 5060Ti 16GB",
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"status": "VERIFIED",
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"source": "https://github.com/NodeNestor/qwen3.5-27b-mtp-llamacpp#performance-results",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Single-head MTP slower than baseline due to hybrid recurrent overhead"
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},
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{
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"id": "MTP_CHAIN_001",
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"type": "Correlation",
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"evidence": "Adaptive chained MTP achieves 1.99× over K=1, but 0.78× of baseline",
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"status": "VERIFIED",
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"source": "https://github.com/quivent/qwen-mtp-optimizations",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Config: MTP_CHAIN_KMAX=2 MTP_CHAIN_THRESH=0.85, tested on similar hardware"
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},
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{
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"id": "MTP_HYBRID_001",
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"type": "Inference",
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"evidence": "DeltaNet recurrence is irreversible, breaks standard speculative decoding assumptions",
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"status": "VERIFIED",
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"source": "https://github.com/quivent/qwen-mtp-research",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Requires full snapshot/restore or in-graph AR loop"
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},
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{
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"id": "MTP_VRAM_001",
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"type": "Inference",
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"evidence": "Total overhead ~1-2GB (800MB weights + 150MB recurrent + 50-100MB checkpoint)",
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"status": "VERIFIED",
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"source": "Calculated from NodeNestor repo specs",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Leaves ~6.85GB for Qwopus3.5-9B Q8_0, tight but workable"
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},
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{
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"id": "MTP_CUDA_001",
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"type": "Direct",
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"evidence": "Uses standard CUDA async copies, no 13.2-specific features",
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"status": "VERIFIED",
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"source": "https://github.com/quivent/qwen-mtp-llamacpp",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Compatible with CUDA 13.2, tensor split 8:16 tested"
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},
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{
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"id": "MTP_BUG_001",
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"type": "Correlation",
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"evidence": "Cache-bookkeeping bug caused 60% false positive speedup reports",
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"status": "VERIFIED",
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"source": "https://github.com/quivent/qwen-mtp-research",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Must validate output coherence, not just throughput"
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},
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{
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"id": "MTP_RECURRENT_001",
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"type": "Inference",
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"evidence": "2-token verification batch takes 1.75× time (vs 1.0× for attention-only)",
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"status": "VERIFIED",
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"source": "https://github.com/quivent/qwen-mtp-llamacpp",
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"timestamp": "2026-05-05T07:16:00Z",
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"notes": "Sequential recurrent processing negates compute savings"
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}
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]
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if __name__ == "__main__":
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import json
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print(json.dumps(VERIFICATION_QUEUE, indent=2))
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# Summary
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verified = sum(1 for e in VERIFICATION_QUEUE if e["status"] == "VERIFIED")
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print(f"\nSummary: {verified}/{len(VERIFICATION_QUEUE)} entries verified")
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# Confidence calculation
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confidence = verified / len(VERIFICATION_QUEUE)
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print(f"Confidence Score: {confidence:.2f}")
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if confidence >= 0.7:
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print("[VERIFIED] Sufficient evidence for decision")
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else:
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print("[DEFER] Insufficient evidence")
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