SmolLM is a project dedicated to the development of small language models. It focuses on training and fine-tuning compact models that maintain high performance while utilizing fewer parameters.
The project emphasizes efficient AI inference and on-device text generation, aiming to enable the deployment of lightweight models on edge devices with limited memory and processing power. It utilizes synthetic data generation to produce artificial datasets that improve the reasoning and training of these AI systems.
The system supports a variety of optimization and training capabilities, including weight quantization, parameter-efficient fine-tuning, and mixed-precision compute. It also covers multilingual text processing and the management of long context windows.