Small but Mighty - The Rise of SMLs 🤖
DataBites #17
Hey you all!
It’s Josep here, one more week! 👋🏻
Today we’ll have a brief discussion about a new trend in the AI field…
So stay with me for 4 minutes —trust me, it’ll be worth your time!
The most important news of the week?
Tomorrow 10 pm (Central European time) I’ll be hosting an X space all about Pandas and its power in Data Analysis!
So don’t be shy and join us in the following link 😉
Now that we’re caught up, let’s get into the important stuff! 👨🏻💻
From Big to Small: A Paradigm Shift in AI
Large Language Models like OpenAI's GPT-4 or Google’s Gemini have revolutionized how we interact with technology. Boasting hundreds of billions of parameters, they’ve captivated industries and users alike.
However, the AI spotlight is shifting.
Smaller, specialized models, known as Small Language Models (SLMs), are stepping up, challenging the dominance of their larger counterparts.
In this issue, we dive into why smaller might be the future of AI and explore how SLMs are carving out their niche.
What Are Small Language Models?
SLMs are compact AI models designed to perform specific tasks with high accuracy. Unlike LLMs, which handle vast amounts of diverse data, SLMs are optimized for efficiency and specialization.
This difference allows them to:
Run on less hardware: Suitable for edge devices like smartphones and IoT gadgets.
Lower costs: Both in training and deployment.
Specialize: Achieve higher accuracy in niche domains.
Conserve energy: Making strides towards sustainable, green AI.
By leveraging targeted training data, SLMs reduce "hallucinations" (producing inaccurate outputs), enabling consistent and reliable results.
Why SLMs Matter
There are six key reasons why SLMs are rapidly becoming industry favorites:
1. Accessibility
SLMs don’t demand high-end computational resources. This opens up opportunities for smaller businesses and developers to integrate AI without breaking the bank.
2. Efficiency:
SMLs require less training data and computational resources compared to LLMs, making them faster to train and lower operational costs.
3. Specialization Brings Precision
Fine-tuned to handle specific tasks, SLMs outperform general-purpose models in accuracy within their niche applications.
4. Reduction of hallucinations:
Related to the previous one, by using a more focused set of training data, the model reduces the risk of generating inaccurate outputs.
5. Cost-Effectiveness
Training and deploying SLMs are significantly cheaper—up to 30x lower operational expenses compared to LLMs.
6. Eco-Friendly
The lower power consumption of SLMs aligns with the growing push for sustainable technology solutions.
The Rising Stars of SLMs
Here are some examples of SLMs making waves:
Mistral 7B
A 7-billion parameter model excelling in various benchmarks, Mistral 7B competes with larger models like Llama 2 and often outperforms them.Independent tests show Mistral 7B, a freely available large language model, outperforms r Llama 2 13B and 34B models on all tasks.
Image from MistralAI official website. Google’s Gemma
Available in compact 2B and 7B configurations, Gemma offers advanced AI capabilities optimized for mobile and edge devices.When compared to bigger models like LlaMa2 13B, it performs better.
Microsoft’s Phi-3 Series
Tailored for mobile environments, Phi-3 models combine efficiency with power, catering to diverse hardware platforms.
Applications of SLMs
Edge Computing
SLMs thrive in scenarios where local data processing is essential—think wearable devices, autonomous vehicles, and smart home systems.
Personalization
Their ability to quickly adapt to individual user preferences makes SLMs invaluable for virtual assistants and recommendation systems.
Content Creation
Whether it’s drafting text, translating languages, or summarizing content, SLMs prove to be agile creators.
What Lies Ahead for SLMs?
As research progresses, we’re likely to see further refinement in SLM architecture and expanded use cases.
From healthcare to entertainment, SLMs are poised to unlock innovation by balancing efficiency with performance.
The era of massive AI isn’t over, but the rise of smaller, smarter models signals a more sustainable, accessible, and specialized future.
Still with me? 🧐
As fellow data enthusiast, I’m sure you’d be eager to help me shape some impactful KPIs and take this newsletter to the next level!
So here’s how you can help:
👉🏻 I want this newsletter to be truly valuable for you, so please share your feedback!
Additionally, you can let me know any preference for future content or any idea you think might add more value to you!
Before you go, tap the 💚 button at the bottom of this email to show your support—it really helps and means a lot!
My latest articles 📝
Using Pandas and SQL Together for Data Analysis in KDnuggets.
Getting started with Python 🐍 in DataBites
The Transformers Architecture - What’s the magic behind LLMs? in AIgents.
Nice articles (my weekly favs)! ♥
My Favorite Resources! 👨🏻💻
I strongly recommend following AIgents roadmaps if you want to become a full Data Scientist, ML Engineer, or Data Engineer,
You can follow the Data Engineer Zoomcap on GitHub to become a fully proficient Data Engineer.
Want to learn GCP? You can follow The Cloud Girl and learn using her intuitive illustrations!
Want to get more of my content? 🙋🏻♂️
Reach me on:
LinkedIn, X (Twitter), or Threads to get daily posts about Data Science.
My Medium Blog to learn more about Data Science, Machine Learning, and AI.
Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓
Remember now that DataBites has an official X (Twitter) account and LinkedIn page. Follow us there to stay updated and help spread the word! 🙌🏻
Thank you for the shoutout Josep! There are quite a lot of great insights packed in one article. I’ll save it and reread it later.