David Stout

Modest intelligence is arriving faster than most people realize.

Today, we're open-sourcing TwIL-8B.

Just 8.8 billion parameters, yet approaching frontier-model scores on several difficult benchmarks:

• 93% of frontier performance on coding

• 92% on instruction following

• 90% on contest mathematics

All from a model small enough to run on your own hardware.

But the benchmarks aren't the most important part of this announcement.

For years, the AI industry has focused on building the most capable model, regardless of the cost required to run it.

I believe that equation is changing.

The next era of AI will be defined by the cost of completing work. Not simply the intelligence of the model, but how efficiently that intelligence can accomplish the task.

If a model with 8 billion parameters can reliably complete the same work as one with hundreds of billions, why pay for the larger model?

As smaller models become more capable, intelligence becomes something we can distribute, specialize, and run across the hardware we already own.

That's the future we're building at @thewebAI

TwIL-8B is available today. Open weights.

And this is just the first of several announcements we're making today.

HF Link: huggingface.co/webAI-Official…

Pretty amazing, proud of the @thewebAI intelligence team; just getting started.

Twil 8b is not a perfect model, but it’s a big step towards frontier intelligence on your devices.

What I have found it to be great at is controlled local coding tasks, tool use, and reasoning entailments for decision models.

@thewebAI 🤝 @huggingface

Just days after we released TwIL-8B, the Hugging Face research team is already pushing its capabilities further.

Today, @huggingface published new research exploring how reinforcement learning and lightweight LoRA adapters can turn reasoning models into capable AI agents.

They included @thewebAI TwIL-8B alongside models from Liquid AI.

The early results are encouraging.

Despite never being explicitly trained for agentic tool use, TwIL-8B is already outperforming the smaller LFM3-1.7B across all three agent harnesses in preliminary baseline evaluations.
Now they're exploring how much further those capabilities can improve through specialized reinforcement learning.

One base model. Many specialized capabilities.

This is a direction we strongly believe in at webAI. Intelligence should be adaptable, efficient, and increasingly capable without requiring ever-larger models.
But what excites me most is the collaboration happening across the open research community.

We built TwIL to push the boundaries of what smaller models can accomplish. Seeing the Hugging Face team build on that work almost immediately is exactly why open research matters.

Huge appreciation to the Hugging Face team for including our work and advancing this research.

The future of AI will be built by more than a handful of labs.
And we're excited to help build it.

Research: lnkd.in/gV7m_-qc
Live training: lnkd.in/g6KAgAAg