ExLLaMA: Guardrails and Fix Patterns

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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ExLLaMA (and its fork ExLLaMA2/ExLLaMA-HF) is a highly optimized CUDA inference backend used under TextGen WebUI and custom pipelines. It can run very large models (65B+) on limited VRAM, but often shows instability when sharded, quantized, or paired with retrieval layers. This guide stabilizes ExLLaMA with structural guardrails.

Open these first

Visual recovery map: RAG Architecture & Recovery
Retrieval and eval knobs: Retrieval Playbook
Boot and ordering: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md
Snippet and trace schema: retrieval-traceability.md, data-contracts.md

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 against anchor snippet
λ convergent across 3 paraphrases × 2 seeds
E_resonance flat across quantization modes (int4, int8)

Common ExLLaMA breakpoints

Symptom	Cause	Fix
First run slower or unstable than warm cache	Lazy CUDA graph compile, missing warm-up fence	bootstrap-ordering.md
ΔS spikes when using quantized weights	Tokenizer drift vs chunked embeddings	embedding-vs-semantic.md, chunking-checklist.md
Memory corruption after long runs	Fragmented KV cache, no eviction strategy	context-drift.md, entropy-collapse.md
API or WebUI tool schema breaks	JSON schema not enforced at inference layer	prompt-injection.md, logic-collapse.md
Multi-shard mismatch on large models	Rank-order desync across GPUs	deployment-deadlock.md

Fix in 60 seconds

Always warm-up: run a 10-token dummy batch before production queries.
Schema lock: enforce snippet_id, section_id, tokens in every trace.
λ probe: measure stability under 2 quant modes (int4 vs int8).
Cache rotation: reset KV cache every N tokens (e.g., 8192) to prevent drift.
Verify: coverage ≥ 0.70, ΔS ≤ 0.45 across three paraphrase probes.

Diagnostic prompt (copy-paste)

I am running ExLLaMA backend with quant={mode}, shards={n}, extensions={list}.  
Question: "{user_question}"  

Please output:
- ΔS vs retrieved snippet
- λ over 3 paraphrases × 2 seeds
- Quantization impact (int4 vs int8)
- Cache stability (tokens until drift)
- 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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

👑 Early Stargazers: See the Hall of Fame ⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.

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