WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/bitsandbytes.md

# BitsAndBytes (bnb): Guardrails and Fix Patterns

<details>
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<br>

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  > To reorient, go back here:  
  >
  > - [**LocalDeploy_Inference** — on-prem deployment and model inference](./README.md)  
  > - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)  
  > - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)  
  >
  > Think of this page as a desk within a ward.  
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</details>


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.

---

## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)  
- End-to-end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)  
- Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)  
- Chunk schema: [Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)  
- 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)  
- Ordering and boot issues: [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md), [Pre-deploy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md)  

---

## Core acceptance
- 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  

---

## Typical BitsAndBytes breakpoints → exact fix

| 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md), [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) |
| Optimizer unstable, NaNs appear | Adam8bit variance clamp missing | [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) |
| GPU memory savings not visible | Linear modules not replaced, or `prepare_model_for_kbit_training` skipped | [Bootstrap Ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md) |
| Synthesis diverges at long steps | Quantization noise accumulates | [Entropy Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/entropy-collapse.md), [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) |
| JSON outputs break format | Schema loose, minor errors amplified | [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) |

---

## Fix in 60 seconds

1) **Measure ΔS**  
   Compare FP16 baseline with bnb quantized run on 10 QA pairs.  
   Acceptable drift ≤ 0.12.  

2) **Probe λ_observe**  
   Vary retrieval k. If λ flips divergent, lock schema order and apply BBAM.  

3) **Apply the module**  
   - Retrieval drift → BBMC + Retrieval Traceability  
   - Optimizer instability → switch to Adam8bit with variance clamp  
   - Long-chain collapse → BBPF + rerankers  

4) **Verify**  
   Coverage ≥ 0.70, λ convergent, entropy stable on 3 paraphrases.  

---

## Copy-paste recipes

### A) Load 4-bit quantized model with bnb
```python
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")
````

### B) Enable 8-bit optimizer

```python
from transformers import Adam8bit

optimizer = Adam8bit(model.parameters(), lr=2e-5)
# Clamp variance manually if drift detected
```

---

## Ops checklist

* 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

---

### 🔗 Quick-Start Downloads (60 sec)

| Tool                       | Link                                                                                                                                       | 3-Step Setup                                                                             |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| **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>”   |
| **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 |

---

### 🧭 Explore More

| Module                   | Description                                                                  | Link                                                                                               |
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| 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)                              |
| Problem Map 1.0          | Initial 16-mode diagnostic and symbolic fix framework                        | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md)                        |
| 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) |
| Semantic Clinic Index    | Expanded failure catalog: prompt injection, memory bugs, logic drift         | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md)           |
| Semantic Blueprint       | Layer-based symbolic reasoning & semantic modulations                        | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md)                 |
| 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)      |
| 🧙‍♂️ 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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