WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/gpt4all.md

GPT4All: 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.

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 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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