🧭 Quick Return to Map
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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.
- 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
- 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
| 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 |
- Measure ΔS at batch=1 and batch=32. If ΔS rises >0.60 only at scale → async batching issue.
- Probe λ across 3 paraphrases. If flips, apply BBAM.
- Enforce contracts: citations must include
snippet_id,offsets. - GPU warm-up: preload with a dummy batch before first live call.
- Verify throughput stability with replay test (2 seeds, same dataset).
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 | 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 |
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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