My Thesis: Small language models (SLM)— models so compact that you can run them on a computer with just 4GB of RAM — are the future. SLMs are efficient enough to be deployed on edge devices, while still maintaining enough intelligence to be useful.
Why?
- Privacy: Many use cases require confidentiality, and deploying models on edge devices means data doesn’t have to be sent to the cloud.
- Low Latency: With on-edge deployment, data processing is faster since it doesn’t require cloud transmission.
- Offline Capabilities: Edge devices enable models to operate independently of the cloud, thus supporting offline functionality.
- Cost-Effectiveness: Smaller models are less expensive, making them more accessible to a broader range of people, developers, companies, and applications.
So: The release of Llama 3.1 has been beneficial for the open-source community. You can host it on the cloud if necessary, opening up new use cases especially for companies that prefer not to depend heavily on proprietary models.
Llama 3.1 Family of Models:
- Llama 3.1 is available in 405B, 70B, and 8B versions.
- Performance: The 405B model performs comparably to the best proprietary models.
- Accessibility: Open and free weights and code, with a license that permits fine-tuning, distillation into other models, and deployment anywhere.
- Capabilities: Offers 128k context length, multilingual abilities, strong code generation performance, complex reasoning capabilities, and tool use.
- Integration: Features a Llama Stack API for easy integration.
- Ecosystem: Supported by over 25 partners, including AWS, NVIDIA, Databricks, Groq, Dell, Azure, and Google Cloud.
However, the real game-changer is the shift towards smaller, yet still powerful, models. This trend is evident, with even OpenAI moving in this direction through the release of GPT-4o-mini — a smaller, less expensive model that is still more useful than GPT-3.5-turbo.
OpenAI understands — the future consists of small language models that individuals and organizations can easily fine-tune for their custom tasks. Small is the new large, and open source is well-positioned to excel in this space.
OpenAI announces that it is possible to fine-tune GPT-4o mini
Performance Analysis: While Llama 3.1 models excel in performance, benchmark studies show that scaling laws are no longer exponential; we do not observe significant leaps in performance, indicating diminishing returns with larger models. Thus, scaling down is the logical path forward, with the next frontier being edge deployment.
Conclusion: It’s better to have a small language model you control than a large one you do not. Take control of your organizational intelligence; don’t let it be monopolized without your input.
