🧭 Quick Return to Map
You are in a sub-page of LocalDeploy_Inference.
To reorient, go back here:
- LocalDeploy_Inference — on-prem deployment and model inference
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.
BitsAndBytes provides 8-bit and 4-bit optimizers and quantized linear layers for large language models.
It enables training and inference under constrained VRAM, but introduces specific stability and semantic risks.
This page maps common bnb issues to structural fixes in the WFGY Problem Map with measurable acceptance gates.
- Visual map and recovery: RAG Architecture & Recovery
- End-to-end retrieval knobs: Retrieval Playbook
- Embedding vs meaning: Embedding ≠ Semantic
- Chunk schema: Chunking Checklist
- Collapse and entropy: Logic Collapse, Entropy Collapse
- Ordering and boot issues: Bootstrap Ordering, Pre-deploy Collapse
- ΔS drift between FP16 and bnb ≤ 0.12
- Coverage ≥ 0.70 for target section
- λ convergent across 3 paraphrases and 2 seeds
- Optimizer variance < 5% vs FP16 baseline after 1k steps
| Symptom | Likely cause | Open this |
|---|---|---|
| Model loads but output ΔS drifts > 0.20 | Incorrect bnb_4bit_compute_dtype or bnb_4bit_quant_type |
Embedding ≠ Semantic, Retrieval Traceability |
| Optimizer unstable, NaNs appear | Adam8bit variance clamp missing | Logic Collapse, Entropy Collapse |
| GPU memory savings not visible | Linear modules not replaced, or prepare_model_for_kbit_training skipped |
Bootstrap Ordering |
| Synthesis diverges at long steps | Quantization noise accumulates | Entropy Collapse, Rerankers |
| JSON outputs break format | Schema loose, minor errors amplified | Data Contracts |
-
Measure ΔS
Compare FP16 baseline with bnb quantized run on 10 QA pairs.
Acceptable drift ≤ 0.12. -
Probe λ_observe
Vary retrieval k. If λ flips divergent, lock schema order and apply BBAM. -
Apply the module
- Retrieval drift → BBMC + Retrieval Traceability
- Optimizer instability → switch to Adam8bit with variance clamp
- Long-chain collapse → BBPF + rerankers
-
Verify
Coverage ≥ 0.70, λ convergent, entropy stable on 3 paraphrases.
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="bfloat16"
)
model_id = "your-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")from transformers import Adam8bit
optimizer = Adam8bit(model.parameters(), lr=2e-5)
# Clamp variance manually if drift detected- Always run FP16 vs bnb ΔS/λ regression before production
- Verify VRAM usage with
torch.cuda.memory_allocated() - Track entropy growth vs sequence length
- Clamp gradient norms at optimizer if instability appears
| 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>” |
| TXT OS (plain-text OS) | TXTOS.txt | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
| 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 |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.
