AutoGPTQ: Guardrails and Fix Patterns

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AutoGPTQ is a widely used library for quantizing large language models into lower-bit formats (INT4/INT8) for efficient local inference.
This page maps the common failure modes when deploying AutoGPTQ and provides structural fixes with measurable targets.

Open these first

Visual map and recovery: RAG Architecture & Recovery
End-to-end retrieval knobs: Retrieval Playbook
Embedding vs meaning: Embedding ≠ Semantic
Chunk schema and stability: Chunking Checklist
Collapse and entropy: Logic Collapse, Entropy Collapse
Boot order and deployment: Bootstrap Ordering, Predeploy Collapse

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 to the target section
λ remains convergent across three paraphrases and two seeds
E_resonance stable across quantized vs full-precision runs

Typical AutoGPTQ breakpoints and the right fix

Symptom	Likely cause	Fix
Model loads but outputs garbage tokens	Misaligned quantization config (bits, group size)	Rebuild with correct group size; validate with ΔS probes
GPU memory still OOM despite quantization	Offloading not configured or weights pinned to VRAM	Enable `device_map=auto`, verify shard placement
Drastic accuracy drop vs FP16 baseline	Quantization schema mismatch or bad calibration	Run small calibration dataset; enforce consistent tokenizer
Inference stalls or crashes	CUDA/driver mismatch, kernels not compiled	Rebuild kernels for your GPU arch; fallback to CPU for test
Wrong snippet chosen during RAG	Retrieval mismatch amplified by quantized logits	Apply Retrieval Traceability + rerankers

Fix in 60 seconds

Quantization check
Verify config: bits, group_size, sym/asym. Run ΔS on 10 QA pairs.
GPU memory probe
Monitor memory before/after load. If OOM persists, enforce CPU/GPU split.
Calibration
Use a gold dataset (100–500 samples). Ensure ΔS gap between FP16 and INT4 ≤ 0.10.
Inference stability
Run 3 paraphrases × 2 seeds. λ must stay convergent.

Deep diagnostics

Entropy vs precision: If entropy collapses earlier in quantized runs, enable double-check rerankers.
Traceability: Log both FP16 and INT4 snippet selections. Divergence >20% means schema fix needed.
Anchor triangulation: Compare ΔS on FP16 vs INT4 to the same section. If drift >0.15, retrain quantizer.

Copy-paste config snippet

from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

quantize_config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False
)

model = AutoGPTQForCausalLM.from_pretrained(
    "your-model",
    quantize_config=quantize_config,
    device_map="auto"
)

Checklist: After loading, test with ΔS probe and λ convergence.

🔗 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 →

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