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134 lines
6.9 KiB
Markdown
134 lines
6.9 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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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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