WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/gpt4all.md

GPT4All: Guardrails and Fix Patterns

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• 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.
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GPT4All is a popular desktop/local LLM runtime with a user-friendly interface and broad model support (GGUF/GGML). It enables plug-and-play inference on CPU/GPU without complex setup, but it introduces typical fragilities: schema drift, citation loss, and memory instability. This page provides WFGY-based guardrails and reproducible fixes.

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Open these first

• Architecture map: RAG Architecture & Recovery
• Retrieval knobs: Retrieval Playbook
• Embedding checks: embedding-vs-semantic.md
• Context failures: context-drift.md, entropy-collapse.md
• Safety fences: prompt-injection.md, logic-collapse.md
• Boot/deploy issues: bootstrap-ordering.md, predeploy-collapse.md

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70
• λ convergent across paraphrases and seeds
• JSON schema compliance enforced
• Context stability beyond 4k–8k tokens

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Common GPT4All breakpoints

Symptom	Likely Cause	Fix
Correct snippet retrieved but answer drifts	Schema mis-binding in desktop client	retrieval-traceability.md, data-contracts.md
Outputs vary per run	Prompt header drift or λ flip	context-drift.md
Free-text injected into tool args	Missing schema lock	prompt-injection.md
JSON parse fails	Inconsistent serialization	logic-collapse.md
First query crashes	Init sequence not fenced	bootstrap-ordering.md

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Fix in 60 seconds

1. Warmup: run a dummy inference before real questions.
2. Schema lock all JSON outputs; reject free text.
3. Trace citations: enforce cite-then-explain with snippet IDs.
4. Measure ΔS and λ across paraphrases; if ΔS ≥ 0.60, re-embed or re-chunk.
5. Reset memory after 4k–8k tokens or when entropy rises.

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Diagnostic prompt (copy-paste)

I am running GPT4All with model={gguf/quant}.
Question: "{user_question}"

Return:
- ΔS(question, retrieved)
- λ across 3 paraphrases × 2 seeds
- JSON schema compliance
- WFGY fix page 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 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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