Independent AI research. New ideas. One experiment at a time.
Hey! We're OpenModel, an independent AI research initiative exploring new approaches to language models, neural network architectures, training methods, and the future of artificial intelligence.
We believe there's still so much to discover about how AI models learn, reason, understand information, and solve problems. The field is evolving rapidly, but there are always new questions to ask, different approaches to explore, and ideas worth investigating.
That's what OpenModel is about: building models, experimenting with architectures, learning from results, and pushing our understanding of AI forward.
We're not here to pretend we have everything figured out. Research is a process of experimentation, iteration, and discovery. Some ideas work, others don't, and sometimes the most valuable outcome of an experiment is learning what to try next.
OpenModel is a journey of building, learning, and improving. And we're just getting started.
The OpenModel-1 Series is our family of language models, designed to give us a foundation for exploring model architecture, training strategies, efficiency, and capabilities at different parameter scales.
Rather than treating OpenModel-1 as a single model, we're developing a series of variants with different sizes and potential use cases.
From compact models designed for lightweight applications to larger models offering more room for experimentation, the series represents our approach to exploring AI across different scales.
Each variant has its own place in the family, and each gives us an opportunity to investigate different engineering and research challenges.
The 4-billion-parameter variant.
Meteor is the 4B member of the OpenModel-1 Series, providing a foundation for experimenting with language modeling at a moderate parameter scale.
Its development gives us an opportunity to explore architecture, training strategies, instruction following, and the balance between model size and practical inference requirements.
The 7-billion-parameter variant.
Comet is the larger of our currently specified variants, giving us another scale at which to investigate model behavior, training approaches, and potential capability improvements.
With more parameters than Meteor, Comet provides a useful opportunity to study the trade-offs associated with model size, training requirements, inference costs, and performance.
Small model. Big curiosity.
Nano is the compact 250-million-parameter member of the family.
Smaller models are especially interesting because they introduce different engineering constraints. Efficiency, model size, inference requirements, and the quality of training data become important considerations when working within a limited parameter budget.
Nano gives us a way to explore compact language models and investigate what can be achieved at a much smaller scale.
A possible future member of the family.
Galaxy is a potential addition to the OpenModel-1 Series, but its specifications have not been decided yet.
Its final parameter count, architecture, objectives, and development plans remain open questions. We want those decisions to be informed by the direction of the project and what we learn from our experiments.
For now, Galaxy remains an idea under consideration rather than a finalized model specification.
Our work focuses on several areas of AI research and development.
We're interested in exploring how different architectural choices affect the way language models process information and learn patterns.
This includes investigating alternative design approaches, examining trade-offs, and looking for ideas that could make future models more effective or efficient.
Training is central to building a useful language model.
We want to experiment with different training strategies, data approaches, optimization techniques, and learning objectives to understand how they influence model behavior.
Our goal is to make decisions based on experiments and evidence rather than assumptions alone.
We are interested in how language models can become better at following instructions, solving challenging problems, handling multi-step tasks, and producing useful responses.
These are ongoing areas of exploration, and meaningful progress requires careful testing rather than relying on impressive-looking outputs alone.
Different model sizes introduce different challenges.
Nano, Meteor, and Comet give us distinct parameter scales to consider when thinking about memory usage, inference speed, training requirements, and practical deployment.
We want to understand these trade-offs and use that knowledge to guide future development.
Building a model is only part of the process. We also need ways to understand how well it performs.
We value systematic evaluation, reproducible experiments where possible, and honest reporting of limitations.
When results become available, they should help us understand what works, what needs improvement, and where our next experiments should focus.
AI research shouldn't be interesting only when a major company releases its next model.
Some of the most exciting discoveries begin with a simple question, an unconventional idea, or an experiment that challenges an existing assumption.
We want OpenModel to be built around that spirit of curiosity.
A place where architectural experiments are worth exploring, unexpected results can teach us something, and developing language models is about more than choosing a parameter count and launching a training run.
We care about the engineering and research behind the models: the design decisions, the training process, the evaluation, the limitations, and the lessons learned along the way.
We also recognize that building useful AI requires more than enthusiasm. It requires careful implementation, realistic goals, thoughtful evaluation, and a willingness to acknowledge when an approach needs improvement.
That's the mindset we want to carry forward as the OpenModel-1 Series develops.
OpenModel is an evolving project. Our ideas, methods, and priorities may change as we learn more.
We want to approach development through a cycle of experimentation and improvement:
Not every experiment will lead to a new release, and not every idea will produce an improvement. That's part of research.
What matters is continuing to investigate, learning from evidence, and building on what we discover.
OpenModel is for people who are curious about artificial intelligence and want to understand how language models are built.
Whether you're an experienced researcher, a developer experimenting with your first model, a student learning about neural networks, or simply someone fascinated by AI, you're welcome to explore our work.
We value thoughtful feedback, technical discussions, constructive criticism, and new perspectives.
If you discover a limitation, have a suggestion, or see a way an experiment could be improved, we'd love to hear about it.
We believe the AI community benefits when people share knowledge, challenge assumptions, and learn from one another.
As the project develops, we hope to make more of our work useful to other developers and researchers through model releases, technical documentation, and research findings where possible.
The OpenModel-1 Series is our starting point.
With Nano, Meteor, and Comet, we have three defined parameter scales around which to organize our model development. Galaxy remains a potential future direction whose specifications are still undecided.
As we continue experimenting, we want to improve our understanding of model architecture, training methods, reasoning, efficiency, and evaluation.
Future decisions will depend on what we learn, what we can implement, and which directions show the most promise.
We don't want to make promises simply to fill a roadmap. We'd rather make progress, document what we learn, and let the work speak for itself.
There is plenty left to explore, plenty to improve, and plenty of questions we haven't answered yet.
That's what makes the journey exciting.
OpenModel is about research, experimentation, engineering, and discovery.
The OpenModel-1 Series is our first step toward building a growing family of language models and exploring new ideas along the way.
We're here to ask questions, test ideas, learn from failures, celebrate improvements, and turn promising concepts into real projects.
We have a lot to learn, a lot to build, and a lot of possibilities ahead.
This is only the beginning.
Welcome to OpenModel.
Let's experiment. Let's learn. Let's build.
— The OpenModel team