Quantize a Small LLM

Key Insight

Post-training quantization allows developers to compress large language models to int4 formats using techniques like GPTQ. Evaluating the quantized model's accuracy on benchmarks like MMLU and monitoring perplexity changes shows how aggressive bit-width reduction affects language understanding. Selecting the right balance between model compression and task quality is key to making models run on resource-constrained hardware.