# Quantizing Models Bitsandbytes

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# bitsandbytes - LLM Quantization

## Quick start

bitsandbytes reduces LLM memory by 50% (8-bit) or 75% (4-bit) with <1% accuracy loss.

**Installation**:
```bash
pip install bitsandbytes transformers accelerate
```

**8-bit quantization** (50% memory reduction):
```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=config,
    device_map="auto"
)

# Memory: 14GB → 7GB
```

**4-bit quantization** (75% memory reduction):
```python
config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=config,
    device_map="auto"
)

# Memory: 14GB → 3.5GB
```

## Common workflows

### Workflow 1: Load large model in limited GPU memory

Copy this checklist:

```
Quantization Loading:
- [ ] Step 1: Calculate memory requirements
- [ ] Step 2: Choose quantization level (4-bit or 8-bit)
- [ ] Step 3: Configure quantization
- [ ] Step 4: Load and verify model
```

**Step 1: Calculate memory requirements**

Estimate model memory:
```
FP16 memory (GB) = Parameters × 2 bytes / 1e9
INT8 memory (GB) = Parameters × 1 byte / 1e9
INT4 memory (GB) = Parameters × 0.5 bytes / 1e9

Example (Llama 2 7B):
FP16: 7B × 2 / 1e9 = 14 GB
INT8: 7B × 1 / 1e9 = 7 GB
INT4: 7B × 0.5 / 1e9 = 3.5 GB
```

**Step 2: Choose quantization level**

| GPU VRAM | Model Size | Recommended |
|----------|------------|-------------|
| 8 GB | 3B | 4-bit |
| 12 GB | 7B | 4-bit |
| 16 GB | 7B | 8-bit or 4-bit |
| 24 GB | 13B | 8-bit or 70B 4-bit |
| 40+ GB | 70B | 8-bit |

**Step 3: Configure quantization**

For 8-bit (better accuracy):
```python
from transformers import BitsAndBytesConfig
import torch

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/10-optimization/bitsandbytes/SKILL.md