WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/ctransformers.md

CTransformers: 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.

CTransformers is a lightweight Python/C++ binding for GGML/GGUF models. It is widely used in minimal local inference setups (often with quantized LLaMA/GPTQ models) but introduces specific risks: unstable JSON tool output, KV cache drift, and library mismatch across versions. This page defines reproducible guardrails and WFGY-based 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
• JSON tool calls must validate against schema

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Common CTransformers breakpoints

Symptom	Likely Cause	Fix
Wrong answers despite valid retrieval	Embedding mis-match with GGUF build	embedding-vs-semantic.md
Model runs but crashes on long context (>4k)	KV cache fragmentation	context-drift.md, entropy-collapse.md
Invalid JSON from tool calls	No enforced schema	prompt-injection.md, logic-collapse.md
Version mismatch across wheels	Pre-deploy collapse	predeploy-collapse.md
First call after import hangs	Boot order not fenced	bootstrap-ordering.md

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Fix in 60 seconds

1. Pre-flight check: after import, run model.generate("hello") to warm up allocator.
2. Force contract schema for all RAG payloads: snippet_id, section_id, offsets.
3. Measure ΔS on at least 2 seeds × 3 paraphrases. Require ΔS ≤ 0.45.
4. Rotate cache every 4–6k tokens.
5. Validate JSON output with strict schema and fail fast on injection.

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Diagnostic prompt (copy-paste)

I am running CTransformers with model={gguf/ggml}, quant={mode}, context={n}.
Question: "{user_question}"

Please output:
- ΔS(question, retrieved)
- λ across 3 paraphrases × 2 seeds
- KV cache stability (max tokens)
- JSON schema compliance
- Minimal 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 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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