Quantize an LLM to 4-bit for inference using bitsandbytes with Hugging Face Transformers (BitsAndBytesConfig)
domain: ml-ops · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Install the stack: pip install --upgrade transformers accelerate bitsandbytes
Build a 4-bit config: from transformers import BitsAndBytesConfig; quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
Load the model with it: model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config)
Check the memory savings with model.get_memory_footprint()
Run inference as usual with generate() — weights are stored in 4-bit but computation runs in the dtype set by bnb_4bit_compute_dtype
Optionally push the quantized model with model.push_to_hub(...), or later call model.dequantize() if you need full precision back
Known gotchas
NF4/FP4 4-bit quantization requires NVIDIA Pascal (GTX 10-series/P100) or newer GPUs per HF's hardware compatibility table (bitsandbytes also has CUDA, Intel XPU, Intel Gaudi/HPU, and CPU backends)
8/4-bit quantized weights only support training of 'extra' parameters (e.g. LoRA adapters via PEFT/QLoRA) — you cannot directly fine-tune the quantized base weights themselves
dequantize() can restore original precision but may cause quality loss and requires enough GPU memory to hold the full-precision model
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