Smollm2 - amosproj/amos2025ss04-ai-driven-testing GitHub Wiki
Important Facts
- Available with 135m, 360m und 1,7b parameters.
- The small models respond very fast with acceptable quality
- Small download size because of only few parameters
- Scored 30% on the Lm_eval (On one run with 10 tests)
- Released under the Apache 2.0 License.
🔍 Overview
SmolLM2-360M, developed by Hugging Face, is a testament to efficient language modeling, offering a powerful yet compact solution. This model prioritizes speed and reduced computational overhead while maintaining strong performance on a variety of language tasks. Its smaller size makes it particularly suitable for scenarios with limited resources or where rapid inference is crucial.
🔧 Key Features
- Efficiency and Compactness: Optimized for lightweight deployment and faster inference times due to its smaller parameter count.
- General Purpose Language Model: Capable of handling a wide range of natural language processing tasks.
- Open-Source Availability: Released under the Apache 2.0 License, promoting accessibility and community contributions.
- Foundation for Research: Its compact nature makes it an excellent base for research into efficient model architectures and deployment strategies.
🧠 Architecture
SmolLM2-360M utilizes a standard transformer-based architecture, a widely adopted and proven design for large language models. While specific details on its pre-training dataset and context window are not readily available in public summaries, its design focuses on achieving high performance within a constrained parameter budget. This makes it an ideal choice for applications where model size and inference speed are critical considerations.