WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/exllamaV2.md

ExLlamaV2: Guardrails and Fix Patterns

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ExLlamaV2 is a specialized inference backend for LLaMA-family models with optimized 4-bit quantization.
It provides faster throughput and lower VRAM usage compared to generic backends, but introduces new risks in accuracy, schema drift, and numerical stability.
This page maps those issues to WFGY structural fixes with measurable acceptance targets.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Embedding vs meaning: Embedding ≠ Semantic
• Chunk schema: Chunking Checklist
• Collapse and entropy: Logic Collapse, Entropy Collapse
• Ordering and boot issues: Bootstrap Ordering, Pre-deploy Collapse

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Core acceptance

• ΔS drift vs FP16 baseline ≤ 0.10
• Coverage ≥ 0.70 for target section
• λ convergent across 3 paraphrases and 2 seeds
• Latency improvement ≥ 25% with accuracy loss ≤ 5%

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Typical ExLlamaV2 breakpoints → exact fix

Symptom	Likely cause	Open this
Text fluency high, citations missing	Schema loosened in quantized path	Data Contracts, Retrieval Traceability
Wrong snippet despite high similarity	Index mismatch after quantization	Embedding ≠ Semantic, Vectorstore Fragmentation
JSON breaks frequently	Quantization noise amplifies schema drift	Logic Collapse, Data Contracts
Long chain divergence after 20–40 steps	Numerical error accumulation	Entropy Collapse, Context Drift
Deployment mismatch	Torch vs ExLlama kernels version skew	Bootstrap Ordering, Pre-deploy Collapse

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Fix in 60 seconds

1. Measure ΔS
   Run 20 QA pairs on FP16 baseline vs ExLlamaV2.
   Acceptable drift ≤ 0.10.

2. Probe λ_observe
   Increase retrieval k. If λ flips divergent, apply BBAM schema lock.

3. Apply the module

   • Retrieval drift → BBMC + Retrieval Traceability
   • Reasoning collapse → BBCR + BBAM clamp
   • Long-chain instability → BBPF alternate paths

4. Verify
   Coverage ≥ 0.70, λ convergent, ΔS ≤ 0.10.

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Minimal setup

from transformers import AutoTokenizer
from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Tokenizer

model_path = "your-llama-model"

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Initialize ExLlamaV2
model = ExLlamaV2(model_path, quant="4bit", gpu_split="auto")
cache = ExLlamaV2Cache(model)

prompt = "Hello, world!"
tokens = tokenizer.encode(prompt, return_tensors="pt").cuda()

output = model.generate(tokens, max_new_tokens=128, cache=cache)
print(tokenizer.decode(output[0]))

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Ops checklist

• Always compare ΔS/λ vs FP16 baseline before shipping
• Pin ExLlama kernels to version matching torch/cuBLAS build
• Log coverage and citation schema at runtime
• Guard JSON outputs with schema validators

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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

Module	Description	Link
WFGY Core	Canonical framework entry point	View
Problem Map	Diagnostic map and navigation hub	View
Tension Universe Experiments	MVP experiment field	View
Recognition	Where WFGY is referenced or adopted	View
AI Guide	Anti-hallucination reading protocol for tools	View

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