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AutoGPTQ: Guardrails and Fix Patterns
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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.
This page maps the common failure modes when deploying AutoGPTQ and provides structural fixes with measurable targets.
Open these first
- Visual map and recovery: RAG Architecture & Recovery
- End-to-end retrieval knobs: Retrieval Playbook
- Embedding vs meaning: Embedding ≠ Semantic
- Chunk schema and stability: Chunking Checklist
- Collapse and entropy: Logic Collapse, Entropy Collapse
- Boot order and deployment: Bootstrap Ordering, Predeploy Collapse
Core acceptance
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 to the target section
- λ remains convergent across three paraphrases and two seeds
- E_resonance stable across quantized vs full-precision runs
Typical AutoGPTQ breakpoints and the right fix
| Symptom | Likely cause | Fix |
|---|---|---|
| Model loads but outputs garbage tokens | Misaligned quantization config (bits, group size) | Rebuild with correct group size; validate with ΔS probes |
| GPU memory still OOM despite quantization | Offloading not configured or weights pinned to VRAM | Enable device_map=auto, verify shard placement |
| Drastic accuracy drop vs FP16 baseline | Quantization schema mismatch or bad calibration | Run small calibration dataset; enforce consistent tokenizer |
| Inference stalls or crashes | CUDA/driver mismatch, kernels not compiled | Rebuild kernels for your GPU arch; fallback to CPU for test |
| Wrong snippet chosen during RAG | Retrieval mismatch amplified by quantized logits | Apply Retrieval Traceability + rerankers |
Fix in 60 seconds
-
Quantization check
Verify config:bits,group_size,sym/asym. Run ΔS on 10 QA pairs. -
GPU memory probe
Monitor memory before/after load. If OOM persists, enforce CPU/GPU split. -
Calibration
Use a gold dataset (100–500 samples). Ensure ΔS gap between FP16 and INT4 ≤ 0.10. -
Inference stability
Run 3 paraphrases × 2 seeds. λ must stay convergent.
Deep diagnostics
- Entropy vs precision: If entropy collapses earlier in quantized runs, enable double-check rerankers.
- Traceability: Log both FP16 and INT4 snippet selections. Divergence >20% means schema fix needed.
- Anchor triangulation: Compare ΔS on FP16 vs INT4 to the same section. If drift >0.15, retrain quantizer.
Copy-paste config snippet
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
quantize_config = BaseQuantizeConfig(
bits=4,
group_size=128,
desc_act=False
)
model = AutoGPTQForCausalLM.from_pretrained(
"your-model",
quantize_config=quantize_config,
device_map="auto"
)
Checklist: After loading, test with ΔS probe and λ convergence.
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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 tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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