Model Quantization

Model quantization reduces the memory footprint and inference latency of DNA language models by representing weights with lower-precision data types.

Overview

Quantization converts model weights from 32-bit floating point (FP32) to lower precision formats such as:

• FP16 (16-bit float): Halves memory usage with minimal accuracy loss
• INT8 (8-bit integer): Further reduces memory and enables faster inference on supported hardware
• INT4 (4-bit integer): Maximum compression, useful for very large models

When to Use Quantization

Consider quantization when:

• GPU memory is insufficient for the full-precision model
• Inference throughput is a bottleneck
• Deploying to edge devices with limited resources

Trade-offs

Precision	Memory	Speed	Accuracy Impact
FP32	100%	Baseline	None
FP16	50%	~1.5-2x	Minimal
INT8	25%	~2-4x	Small
INT4	12.5%	~3-8x	Moderate

Using Quantization in DNALLM

DNALLM supports quantization through the underlying inference framework. Set the torch_dtype parameter when loading models:

from dnallm.models import load_model_and_tokenizer

# Load model in FP16
model, tokenizer = load_model_and_tokenizer("model_name", torch_dtype="float16")

# Load model in INT8 (requires bitsandbytes)
model, tokenizer = load_model_and_tokenizer("model_name", load_in_8bit=True)

Hardware Requirements

• FP16: Requires GPU with compute capability ≥ 5.3 (Pascal+)
• INT8: Requires GPU with compute capability ≥ 6.1 (Pascal+) or CPU with AVX2
• INT4: Requires GPU with compute capability ≥ 8.0 (Ampere+) for optimal performance

Best Practices

1. Validate accuracy: Always benchmark quantized models against the full-precision baseline on your specific task
2. Start with FP16: It offers the best accuracy-to-speed trade-off for most DNA LM tasks
3. Use calibration data: For INT8/INT4, provide representative sequences for calibration
4. Monitor perplexity: Track sequence prediction quality as a proxy for model fidelity

Troubleshooting

Out of Memory During Quantization

Quantization itself requires loading the full model first. If you run out of memory:

• Use CPU offloading during quantization
• Quantize layer-by-layer
• Use smaller calibration batches

Accuracy Degradation

If quantized model performance drops significantly:

• Try FP16 instead of INT8/INT4
• Increase calibration dataset size
• Use quantization-aware fine-tuning (QAT)
• Keep critical layers (embeddings, attention) in higher precision