Topic: What is a Small Language Model (SLM)?
I used to think that any model with fewer than X million parameters was "small."
It turns out that there is no universally accepted definition.
What really makes a model "small"?
👉 Researchers often look at two factors:
1️⃣ Parameter Count – Usually <100M, but context matters.
2️⃣ Deployment Footprint – Can it run on a CPU? Edge device? Even a phone?
In today's post, I explore:
How we built two small storytelling models:
🔹 GPT-based Children’s Stories (30M params)
🔹 DeepSeek Children’s Stories (15M params)
Why building SLMs makes sense for cost, speed, and edge use-cases
And the real limitations of going small: shallow reasoning, hallucinations, short context windows, etc.
💡 The takeaway: Small doesn’t mean simple. It means focused.
Over the next 49 days, I’ll walk through everything, from tokenization to distillation to deployment, building efficient models that actually run on real-world hardware.
🔗 Full blog post: https://www.ideaweaver.ai/blog/day1.html
🌟 If you're into SLMs, on-device inference, or domain-specific LLMs, follow along. This journey is just getting started.
If you’re looking for a one-stop solution for AI model training, evaluation, and deployment, with advanced RAG capabilities and seamless MCP (Model Context Protocol) integration, check out IdeaWeaver.
🚀 Train, fine-tune, and deploy language models with enterprise-grade features.
📚 Docs: https://ideaweaver-ai-code.github.io/ideaweaver-docs/
💻 GitHub: https://github.com/ideaweaver-ai-code/ideaweaver
If you find IdeaWeaver helpful, a ⭐ on the repo would mean a lot!
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