AWQ (Activation-aware Weight Quantization): Guardrails and Fix Patterns

AWQ/AutoAWQ applies activation-aware quantization to compress weights into 4/8-bit, aiming for higher throughput on local inference with minimal accuracy loss.
This page maps typical AWQ failure modes to structural fixes in the WFGY Problem Map and defines measurable acceptance gates.

Open these first

Visual map and recovery: RAG Architecture & Recovery
End-to-end retrieval knobs: Retrieval Playbook
Why this snippet: Retrieval Traceability
Embedding vs meaning: Embedding ≠ Semantic
Chunk schema: Chunking Checklist
Collapse and entropy: Logic Collapse, Entropy Collapse
Boot order and deploy: Bootstrap Ordering, Pre-deploy Collapse

Core acceptance

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 of the target section
λ remains convergent across 3 paraphrases and 2 seeds
Compared to FP16 baseline, ΔS drift ≤ 0.10, and entropy curve remains stable on long sequences

Typical AWQ breakpoints → exact fix

Symptom	Likely cause	Open this
PPL rises significantly after quantization, answers drift	Calibration dataset mismatch, wrong `q_group_size`	Retrieval Playbook, Chunking Checklist
Snippets retrieved correctly but synthesis drifts	Logit jitter from quantization, unstable header ordering	Retrieval Traceability, Logic Collapse
GPU still OOM or no throughput gain	Layer fusion disabled, device map misaligned	Bootstrap Ordering
Long-chain reasoning collapses earlier	Faster entropy accumulation, no rerank or bridge	Entropy Collapse, Rerankers
JSON tool outputs unstable	Small errors amplified, schema too loose	Data Contracts

Fix in 60 seconds

Measure ΔS
Run 10 QA pairs with FP16 and AWQ. Compare ΔS(question, retrieved) and ΔS(retrieved, anchor).
If ΔS drift > 0.10, recalibrate.
Probe λ_observe
Vary k = 5/10/20, shuffle headers.
If λ flips, lock header ordering and apply BBAM variance clamp.
Apply the module
- Retrieval drift → BBMC + Retrieval Traceability
- Reasoning collapse → BBCR + BBAM
- Long-chain dead ends → BBPF alternative path + reranker
Verify
Three paraphrases × two seeds. Coverage ≥ 0.70, λ convergent.

Deep diagnostics

Calibration set sanity
Use a calibration set that matches production distribution. If only short or domain-limited data is used, quantized model will misbehave.
Anchor triangulation
Compare ΔS to anchor section and adjacent distractor. If gap ≤ 0.05, semantic boundaries are flattened → redo calibration or adjust w_bit, q_group_size.
Entropy vs length
Plot entropy per step on long sequences. If AWQ rises earlier than FP16, enable deterministic sampling, raise temperature floor, and add reranker.

Copy-paste recipes

A) Load a pre-quantized AutoAWQ model

from autoawq import AutoAWQForCausalLM, AutoTokenizer

model_id = "your-awq-model"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoAWQForCausalLM.from_pretrained(
    model_id,
    fuse_layers=True,
    safetensors=True,
    device_map="auto"
)
# Run ΔS/λ regression tests vs FP16 baseline

B) Online quantization with custom config

from autoawq import AutoAWQForCausalLM, AutoTokenizer

base_id = "your-fp16-model"
tokenizer = AutoTokenizer.from_pretrained(base_id, use_fast=True)
model = AutoAWQForCausalLM.from_pretrained(base_id, device_map="auto")

quant_config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}
model.quantize(tokenizer, quant_config)
# Export and cache, then immediately run ΔS regression

Ops checklist

Verify driver + compute capability before loading kernels
Always test single-GPU ΔS/λ vs FP16 before scaling parallelism
Track VRAM + throughput together with ΔS, λ, coverage metrics

🔗 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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