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138 lines
8.6 KiB
Markdown
138 lines
8.6 KiB
Markdown
# AutoGPTQ: Guardrails and Fix Patterns
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **LocalDeploy_Inference**.
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> To reorient, go back here:
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>
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> - [**LocalDeploy_Inference** — on-prem deployment and model inference](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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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.
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This page maps the common failure modes when deploying AutoGPTQ and provides structural fixes with measurable targets.
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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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- Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- Chunk schema and stability: [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 deployment: [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md), [Predeploy 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 to the target section
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- λ remains convergent across three paraphrases and two seeds
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- E_resonance stable across quantized vs full-precision runs
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---
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## Typical AutoGPTQ breakpoints and the right fix
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| Symptom | Likely cause | Fix |
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|---------|--------------|-----|
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| Model loads but outputs garbage tokens | Misaligned quantization config (bits, group size) | Rebuild with correct group size; validate with ΔS probes |
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| GPU memory still OOM despite quantization | Offloading not configured or weights pinned to VRAM | Enable `device_map=auto`, verify shard placement |
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| Drastic accuracy drop vs FP16 baseline | Quantization schema mismatch or bad calibration | Run small calibration dataset; enforce consistent tokenizer |
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| Inference stalls or crashes | CUDA/driver mismatch, kernels not compiled | Rebuild kernels for your GPU arch; fallback to CPU for test |
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| Wrong snippet chosen during RAG | Retrieval mismatch amplified by quantized logits | Apply [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) + rerankers |
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---
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## Fix in 60 seconds
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1. **Quantization check**
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Verify config: `bits`, `group_size`, `sym/asym`. Run ΔS on 10 QA pairs.
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2. **GPU memory probe**
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Monitor memory before/after load. If OOM persists, enforce CPU/GPU split.
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3. **Calibration**
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Use a gold dataset (100–500 samples). Ensure ΔS gap between FP16 and INT4 ≤ 0.10.
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4. **Inference stability**
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Run 3 paraphrases × 2 seeds. λ must stay convergent.
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---
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## Deep diagnostics
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- **Entropy vs precision**: If entropy collapses earlier in quantized runs, enable double-check rerankers.
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- **Traceability**: Log both FP16 and INT4 snippet selections. Divergence >20% means schema fix needed.
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- **Anchor triangulation**: Compare ΔS on FP16 vs INT4 to the same section. If drift >0.15, retrain quantizer.
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---
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## Copy-paste config snippet
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```python
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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quantize_config = BaseQuantizeConfig(
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bits=4,
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group_size=128,
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desc_act=False
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)
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model = AutoGPTQForCausalLM.from_pretrained(
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"your-model",
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quantize_config=quantize_config,
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device_map="auto"
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)
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````
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*Checklist*: After loading, test with ΔS probe and λ convergence.
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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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