Llama.cpp: Guardrails and Fix Patterns

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.

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

Core acceptance

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

Common Llama.cpp breakpoints

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

Fix in 60 seconds

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).

Diagnostic prompt (copy-paste)

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

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