WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/vllm.md

vLLM: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of LocalDeploy_Inference.
To reorient, go back here:

• LocalDeploy_Inference — on-prem deployment and model inference
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Field guide for stabilizing vLLM-based local inference pipelines. Use these checks when models serve correctly on API providers but fail under high-throughput GPU serving with vLLM.

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Open these first

• Visual recovery: RAG Architecture & Recovery
• Retrieval tuning knobs: Retrieval Playbook
• Snippet traceability: retrieval-traceability.md
• Ordering & race fixes: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md
• Embedding issues: embedding-vs-semantic.md

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for target section
• λ remains convergent across 3 paraphrases and 2 seeds
• Throughput scaling does not shift retrieved citations

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Typical vLLM breakpoints and fix

Symptom	Likely cause	Fix
Works at batch=1 but fails at scale	Context window fragmentation / GPU memory swap	context-drift.md, entropy-collapse.md
Citations disappear at high load	Async batch merge drops offsets	retrieval-traceability.md, data-contracts.md
Different answers run-to-run	λ flips with batch ordering	logic-collapse.md, rerankers.md
Index correct but retrieval unstable	Embedding vs metric mismatch in store	embedding-vs-semantic.md, vectorstore-fragmentation.md
GPU OOM / crash at warm-up	Preload sequence too large, missing fence	bootstrap-ordering.md

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Fix in 60 seconds

1. Measure ΔS at batch=1 and batch=32. If ΔS rises >0.60 only at scale → async batching issue.
2. Probe λ across 3 paraphrases. If flips, apply BBAM.
3. Enforce contracts: citations must include snippet_id, offsets.
4. GPU warm-up: preload with a dummy batch before first live call.
5. Verify throughput stability with replay test (2 seeds, same dataset).

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Copy-paste test prompt

I am running vLLM locally.  
Models served with async batching.  
Question: "{user_question}"  

Please return:
1. ΔS at batch=1 and batch=32  
2. λ across 3 paraphrases  
3. Whether citations preserved (snippet_id, offsets)  
4. Minimal structural fix if ΔS ≥ 0.60  

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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