Individual skill
optimization-bitsandbytes
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.
skills-imported/optimization-bitsandbytes/SKILL.md
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- Last checked
- Nov 8, 2026
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- Repository last updated
- Oct 10, 2026
- License
- MIT
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- agent-skills Repository topic
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- optimization Declared skill tag
- bitsandbytes Declared skill tag
- quantization Declared skill tag
- 8-bit Declared skill tag
- 4-bit Declared skill tag
- memory-optimization Declared skill tag
- qlora Declared skill tag
- nf4 Declared skill tag
- int8 Declared skill tag
- huggingface Declared skill tag
- efficient-inference Declared skill tag
- engineering Derived category
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Read excerpt
# 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