KoboldCPP: 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.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

KoboldCPP is a lightweight local inference runtime for LLaMA-based models, focused on CPU (with AVX/AVX2/AVX512) and GPU acceleration (CUDA, Metal, OpenCL). It is popular in hobbyist and low-VRAM deployments but frequently shows drift when paired with RAG, agents, or long-context reasoning. This page defines stability checks and exact WFGY fixes.

Open these first

Visual recovery map: RAG Architecture & Recovery
End-to-end knobs: Retrieval Playbook
Ordering and deploy fences: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md
Trace schema: retrieval-traceability.md, data-contracts.md

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 on target snippet
λ remains convergent across 3 paraphrases × 2 seeds
Stable behavior across quantization (Q4_K_M, Q5_K, Q8_0)

Common KoboldCPP breakpoints

Symptom	Likely Cause	Fix
ΔS jumps when switching quantization modes	Embedding contract not enforced	embedding-vs-semantic.md, chunking-checklist.md
Context drift after ~4–6k tokens	KV cache fragmentation	context-drift.md, entropy-collapse.md
Tool JSON output invalid	No enforced schema	prompt-injection.md, logic-collapse.md
Model boots but hangs on first query	Lazy allocator / boot fence missing	bootstrap-ordering.md
Agent handoff loops with role drift	Memory namespace not separated	Multi-Agent_Problems.md

Fix in 60 seconds

Run a warm-up batch (10–20 tokens) before first live query.
Force citation schema: snippet_id, section_id, offsets must be present.
λ probe: run three paraphrases at k=5,10,20. Require convergence.
Rotate cache every 8k tokens to prevent entropy collapse.
Verify with coverage ≥ 0.70, ΔS ≤ 0.45.

Diagnostic prompt (copy-paste)

I am running KoboldCPP with quant={mode}, context={n_tokens}, and agents={on/off}.  
Question: "{user_question}"  

Please output:
- ΔS(question, retrieved)
- λ across 3 paraphrases × 2 seeds
- Cache stability (max tokens before drift)
- JSON schema compliance
- Minimal WFGY fix page if ΔS ≥ 0.60

🔗 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

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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