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AutoGPTQ: Guardrails and Fix Patterns
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Think of this page as a desk within a ward.
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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.
🔗 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>” |
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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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