WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/exllama.md

ExLLaMA: Guardrails and Fix Patterns

🧭 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.

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.

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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

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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)

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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

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Fix in 60 seconds

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

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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

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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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