WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/awq.md

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

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• LocalDeploy_Inference — on-prem deployment and model inference
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
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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.

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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

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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

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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

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Fix in 60 seconds

1. Measure ΔS
   Run 10 QA pairs with FP16 and AWQ. Compare ΔS(question, retrieved) and ΔS(retrieved, anchor).
   If ΔS drift > 0.10, recalibrate.

2. Probe λ_observe
   Vary k = 5/10/20, shuffle headers.
   If λ flips, lock header ordering and apply BBAM variance clamp.

3. Apply the module

   • Retrieval drift → BBMC + Retrieval Traceability
   • Reasoning collapse → BBCR + BBAM
   • Long-chain dead ends → BBPF alternative path + reranker

4. Verify
   Three paraphrases × two seeds. Coverage ≥ 0.70, λ convergent.

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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.

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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

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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

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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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