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