AutoGPTQ: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of LocalDeploy_Inference.
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.

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	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

If this repository helps, starring it improves discovery for other builders.

6.3 KiB Raw Blame History Unescape Escape