We build small, fast, production-ready models that punch above their weight. No bloat. No nonsense. Runs on your laptop, your phone, your API — not just someone's A100 cluster.
Most AI labs race to make models bigger. We race to make them smaller without losing what matters.
Every model we ship is purpose-built for a specific task — not a general-purpose blob that tries to do everything mediocrely. Specialization wins.
Everything is trained and tested on Google Colab T4 GPUs. If it doesn't run there, it doesn't ship. Your hardware shouldn't be a barrier to good AI.
No paywalls. No gated weights. No "available upon request." Every model, dataset, and training notebook is public, forkable, and yours to build on.
Smaller models mean less compute, less energy, less waste. Good ML shouldn't require a power plant. We take that seriously.
Every model trained on free-tier hardware. No excuses, no shortcuts — just efficient engineering.
The AI industry measures progress in billions of parameters. We measure it in milliseconds and zero-dollar bills.
Every model we release must clear all three bars. No partial credit.
If it doesn't run on a Colab T4, it doesn't ship. Deployable by anyone means deployable by everyone — students, indie devs, researchers with no budget.
Size is not an excuse for quality. Every TinyModel must outperform or match models with significantly higher parameter counts on its target benchmark. Efficiency is the craft.
A model nobody can use is worthless. Every release comes with a complete model card, working code examples, and honest evaluation numbers — no cherry-picked benchmarks.
We take the work seriously. Not always ourselves.
GPT-4 walks into a bar. The bartender says: "We don't serve models with 1.8 trillion parameters here." GPT-4 says: "That's fine, I'll just hallucinate a better bar."
A researcher asks a 70B model and a 141M model the same question. The 70B model takes 4 seconds and gives a three-paragraph answer. The 141M model answers in 22ms and says: "Same thing, shorter."
The democratization of AI is not a marketing phrase — it is an obligation. When the tools to build intelligent systems are locked behind compute budgets that only large organizations can afford, the people who could benefit most from this technology are exactly the ones who cannot access it. A student in a resource-constrained environment with a free Colab account deserves the same quality of inference as a team running enterprise GPU clusters.
TinyModels exists because we believe that making something smaller and faster is not a compromise — it is a discipline. Every parameter saved, every millisecond cut, every megabyte reduced is a deliberate act of engineering that makes this technology more portable, more sustainable, and more inclusive. We will keep building that way.
All TinyModels share the same interface. Learn once, use everywhere.
TinyModels is a free, open community. No gatekeeping, no paywalls. Follow the org, contribute models, datasets, or ideas — all are welcome.