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 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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