WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/llamacpp.md

Llama.cpp: Guardrails and Fix Patterns

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Llama.cpp is the most widely used local inference runtime for GGML/GGUF models. It enables CPU/GPU inference across diverse hardware but often introduces fragile states: mismatched quantization, KV-cache drift, and long-context instability. This page defines reproducible WFGY-based guardrails and direct fixes.

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Open these first

• Visual recovery map: RAG Architecture & Recovery
• Retrieval knobs: Retrieval Playbook
• Embedding alignment: embedding-vs-semantic.md
• Context stability: context-drift.md, entropy-collapse.md
• Schema and injection fences: prompt-injection.md, logic-collapse.md
• Deploy fences: bootstrap-ordering.md, predeploy-collapse.md

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70
• λ convergent across three paraphrases × two seeds
• KV cache stability for context >8k tokens
• JSON schema compliance enforced when using tools

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Common Llama.cpp breakpoints

Symptom	Likely Cause	Fix
Wrong answers despite high similarity	Embedding metric mismatch with GGUF/quant variant	embedding-vs-semantic.md
Model slows or collapses after 8–16k tokens	KV cache drift	context-drift.md, entropy-collapse.md
Output alternates between runs	Prompt header drift	retrieval-traceability.md
Invalid JSON or schema drift	Missing tool schema lock	prompt-injection.md, logic-collapse.md
Crash at first inference call	Boot order not fenced	bootstrap-ordering.md
Segfault when mixing quantized weights	Pre-deploy mismatch	predeploy-collapse.md

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Fix in 60 seconds

1. Pre-flight warmup: run a dummy prompt (e.g., "hello") to allocate memory.
2. Schema lock all JSON tool outputs; reject free text where structured arguments expected.
3. Measure ΔS across 3 paraphrases, require ≤0.45.
4. Rotate cache or reset every 8–16k tokens.
5. Ensure quantization match between build and model weights (GGUF flags).

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Diagnostic prompt (copy-paste)

I am running Llama.cpp with model={gguf/quant}, context={n}.
Question: "{user_question}"

Return:
- ΔS(question, retrieved)
- λ states across 3 paraphrases × 2 seeds
- KV cache drift beyond 8k tokens
- JSON schema compliance
- Minimal WFGY page to open 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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