Hugging Face Introduces SmolLM: High-Performance Small Language Models
Hugging Face has recently released SmolLM, a family of state-of-the-art small models designed to provide powerful performance in a compact form. The SmolLM models are available in three sizes: 135M, 360M, and 1.7B parameters, making them suitable for various applications while maintaining efficiency and performance.
Practical Solutions and Value
SmolLM is a new series of small language models developed by Hugging Face, aimed at delivering high performance with lower computational costs and improved user privacy. These models are trained on a meticulously curated high-quality dataset, SmolLM-Corpus, which includes diverse educational and synthetic data sources. The three models in the SmolLM family are designed to cater to different levels of computational resources while maintaining state-of-the-art performance.
The SmolLM models are built on the SmolLM-Corpus, a dataset comprising various high-quality sources such as Cosmopedia v2, Python-Edu, and FineWeb-Edu. This dataset ensures a broad coverage of topics and prompts, improving the diversity and quality of the training data.
For the 1.7B parameter model, Hugging Face used 1 trillion tokens from the SmolLM-Corpus, while the 135M and 360M parameter models were trained on 600 billion tokens. The training process employed a trapezoidal learning rate scheduler with a cooldown phase, ensuring efficient and effective model training.
SmolLM models were evaluated across benchmarks, testing common sense reasoning and world knowledge. The models demonstrated impressive performance, outperforming others in their respective size categories.
One of the significant advantages of the SmolLM models is their ability to run efficiently on various hardware configurations, including smartphones and laptops.
AI Solutions for Your Company
If you want to evolve your company with AI, stay competitive, and use Hugging Face’s SmolLM models:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
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