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ProblemMap/GlobalFixMap/LocalDeploy_Inference/awq.md
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# AWQ (Activation-aware Weight Quantization): Guardrails and Fix Patterns
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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.
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This page maps typical AWQ failure modes to structural fixes in the WFGY Problem Map and defines measurable acceptance gates.
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---
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## Open these first
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- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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- End-to-end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- Why this snippet: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- Chunk schema: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- Collapse and entropy: [Logic Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/logic-collapse.md), [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md)
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- Boot order and deploy: [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md), [Pre-deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md)
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---
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## Core acceptance
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- ΔS(question, retrieved) ≤ 0.45
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- Coverage ≥ 0.70 of the target section
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- λ remains convergent across 3 paraphrases and 2 seeds
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- Compared to FP16 baseline, ΔS drift ≤ 0.10, and entropy curve remains stable on long sequences
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---
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## Typical AWQ breakpoints → exact fix
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| Symptom | Likely cause | Open this |
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|---|---|---|
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| PPL rises significantly after quantization, answers drift | Calibration dataset mismatch, wrong `q_group_size` | [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md), [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) |
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| Snippets retrieved correctly but synthesis drifts | Logit jitter from quantization, unstable header ordering | [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md), [Logic Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/logic-collapse.md) |
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| GPU still OOM or no throughput gain | Layer fusion disabled, device map misaligned | [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) |
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| Long-chain reasoning collapses earlier | Faster entropy accumulation, no rerank or bridge | [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md), [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) |
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| JSON tool outputs unstable | Small errors amplified, schema too loose | [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) |
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---
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## Fix in 60 seconds
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1) **Measure ΔS**
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Run 10 QA pairs with FP16 and AWQ. Compare ΔS(question, retrieved) and ΔS(retrieved, anchor).
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If ΔS drift > 0.10, recalibrate.
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2) **Probe λ_observe**
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Vary k = 5/10/20, shuffle headers.
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If λ flips, lock header ordering and apply BBAM variance clamp.
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3) **Apply the module**
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- Retrieval drift → BBMC + [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Reasoning collapse → BBCR + BBAM
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- Long-chain dead ends → BBPF alternative path + reranker
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4) **Verify**
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Three paraphrases × two seeds. Coverage ≥ 0.70, λ convergent.
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---
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## Deep diagnostics
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- **Calibration set sanity**
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Use a calibration set that matches production distribution. If only short or domain-limited data is used, quantized model will misbehave.
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- **Anchor triangulation**
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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`.
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- **Entropy vs length**
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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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---
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## Copy-paste recipes
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### A) Load a pre-quantized AutoAWQ model
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```python
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from autoawq import AutoAWQForCausalLM, AutoTokenizer
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model_id = "your-awq-model"
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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model = AutoAWQForCausalLM.from_pretrained(
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model_id,
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fuse_layers=True,
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safetensors=True,
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device_map="auto"
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)
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# Run ΔS/λ regression tests vs FP16 baseline
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````
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### B) Online quantization with custom config
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```python
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from autoawq import AutoAWQForCausalLM, AutoTokenizer
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base_id = "your-fp16-model"
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tokenizer = AutoTokenizer.from_pretrained(base_id, use_fast=True)
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model = AutoAWQForCausalLM.from_pretrained(base_id, device_map="auto")
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quant_config = {
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"zero_point": True,
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"q_group_size": 128,
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"w_bit": 4,
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"version": "GEMM"
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}
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model.quantize(tokenizer, quant_config)
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# Export and cache, then immediately run ΔS regression
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```
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---
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## Ops checklist
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* Verify driver + compute capability before loading kernels
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* Always test single-GPU ΔS/λ vs FP16 before scaling parallelism
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* Track VRAM + throughput together with ΔS, λ, coverage metrics
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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