KoboldCPP: Guardrails and Fix Patterns
March 6, 2026 · View on GitHub
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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 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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