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+# 🌌 Welcome to OpenModel!
+
+**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.**
+
 ---
-title: README
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
-sdk: static
-pinned: false
----
 
-# 👋 Welcome to OpenModel!
+## 🧠 Introducing the OpenModel-1 Series
+
+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.
+
+### 🚀 Meet the family
+
+<details>
+*The series currently has three named size variants and one potential future variant whose specifications remain undecided.*
+
+### ☄️ OpenModel-1 Meteor — 4B
 
-**Building AI differently, one experiment at a time.**
+**The 4-billion-parameter variant.**
 
-Hey! We're OpenModel, an independent AI research initiative exploring new ideas in language models, neural network architectures, training methods, and the broader future of artificial intelligence.
+Meteor is the 4B member of the OpenModel-1 Series, providing a foundation for experimenting with language modeling at a moderate parameter scale.
 
-We're here because we think there's still so much left to discover about how AI models learn, reason, generate, and solve problems. The field is moving incredibly quickly, but there are always new questions to ask, different approaches to try, and ideas that haven't been explored enough.
+Its development gives us an opportunity to explore architecture, training strategies, instruction following, and the balance between model size and practical inference requirements.
 
-That's what OpenModel is about: experimenting, building, learning, and pushing our understanding of AI forward.
+- Parameters: 4 billion
+- Series: OpenModel-1
+- Variant: Meteor
+- Focus: Language modeling and architectural experimentation
 
-We're not here to pretend we have everything figured out. Research involves trial and error, experiments that don't work as expected, and plenty of things to learn along the way. We believe that's part of what makes it exciting.
+### ☄️ OpenModel-1 Comet — 7B
 
-## 🧠 Our first model: OpenModel-1
+**The 7-billion-parameter variant.**
 
-Our first release, **OpenModel-1**, is a 4-billion-parameter language model built around our own architectural and training ideas.
+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.
 
-It's an important first step for OpenModel, giving us a foundation to experiment with model design, evaluate different approaches, and explore what we can achieve with future iterations.
+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.
 
-But OpenModel-1 isn't the end goal. It's the beginning.
+- Parameters: 7 billion
+- Series: OpenModel-1
+- Variant: Comet
+- Focus: Larger-scale language modeling and experimentation
 
-We want to keep learning from our experiments, identifying what works, understanding what doesn't, and using those lessons to guide our next steps. Every release is an opportunity to improve our methods, explore new possibilities, and build on what came before.
+### ✨ OpenModel-1 Nano — 250M
+
+**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.
+
+- Parameters: 250 million
+- Series: OpenModel-1
+- Variant: Nano
+- Focus: Compact language modeling and efficiency
+
+### 🌌 OpenModel-1 Galaxy — Undecided
+
+**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.
+
+- Parameters: Undecided
+- Series: OpenModel-1
+- Variant: Galaxy
+- Status: Under consideration
+
+</details>
+
+---
 
 ## 🔬 What we're exploring
 
-Our work is centered around a few core areas:
+Our work focuses on several areas of AI research and development.
 
-- **New architectures.** Exploring alternative ways to structure neural networks, process information, and build efficient language models.
-- **Training experiments.** Investigating different training strategies, objectives, data approaches, and optimization techniques to understand how they affect model performance.
-- **Reasoning and problem-solving.** Exploring how models can become better at following instructions, working through challenging tasks, and producing useful answers.
-- **Efficiency and scalability.** Looking for ways to make models more practical to train, run, and improve without losing sight of their capabilities.
-- **Evaluation and iteration.** Testing our ideas, examining results, learning from limitations, and using evidence to guide future experiments.
-- **Open research and collaboration.** Sharing our work where possible, learning from the wider AI community, and encouraging others to explore ideas of their own.
+### 🧩 Neural network architectures
 
-These are areas we're interested in developing over time, not a claim that every problem has already been solved. Our approach is to experiment, measure, learn, and keep improving.
+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 methods
+
+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.
+
+### 🧠 Reasoning and problem-solving
+
+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.
+
+### ⚡ Efficiency and scalability
+
+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.
+
+### 📊 Evaluation and iteration
+
+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.
+
+---
 
 ## 🌱 Why OpenModel exists
 
-AI research shouldn't be interesting only when a huge company announces its next model.
+AI research shouldn't be interesting only when a major company releases its next model.
 
-Some of the most exciting parts of building AI happen when someone asks an unusual question, tries an unconventional approach, or decides to investigate an idea simply because they want to understand it better.
+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 a place for that kind of curiosity.
+We want OpenModel to be built around that spirit of curiosity.
 
-A place where architectural experiments are worth exploring, where unexpected results can teach us something, and where building a model is about more than choosing a parameter count and running a training script.
+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 process behind the model: the decisions, the experiments, the engineering, the trade-offs, and the lessons that emerge along the way.
+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 takes more than an interesting idea. It takes careful implementation, meaningful evaluation, responsible development, and a willingness to acknowledge limitations.
+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 bring to OpenModel as the project grows.
+That's the mindset we want to carry forward as the OpenModel-1 Series develops.
 
-## 🛠️ Built through experimentation
+---
 
-OpenModel is an evolving research project. Our ideas will change, our methods will improve, and our priorities may develop as we learn more.
+## 🛠️ Our approach
 
-Some experiments may turn into new releases. Others may lead us in a completely different direction. Either way, we want to use what we discover to make better decisions about what to build next.
+OpenModel is an evolving project. Our ideas, methods, and priorities may change as we learn more.
 
-We're interested in understanding *why* something works, not just whether it works once.
+We want to approach development through a cycle of experimentation and improvement:
 
-That means paying attention to model behavior, evaluating results, comparing approaches, and being honest about the difference between an interesting hypothesis and a demonstrated improvement.
+1. **Ask questions.** Identify problems, limitations, or ideas worth exploring.
+2. **Design experiments.** Develop approaches that can be implemented and evaluated.
+3. **Build and train.** Turn promising ideas into working models and training runs.
+4. **Evaluate results.** Examine model behavior and measure performance where suitable benchmarks are available.
+5. **Learn and iterate.** Use the results to improve existing approaches and guide future work.
+6. **Share what we learn.** Publish useful details and findings when possible.
 
-We don't believe progress comes from making the biggest claims. We believe it comes from doing the work, examining the evidence, and continuing to learn.
+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.
+
+---
 
 ## 🌍 An invitation to the AI community
 
-OpenModel is for people who are curious about how AI works and what it could become.
+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 language model, a student learning about neural networks, or simply someone fascinated by artificial intelligence, we hope you'll find something interesting here.
+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.
 
-You don't need to have all the answers to be curious. You don't need to work at a major research lab to experiment with ideas. And you don't need to agree with every approach we take to find our work useful.
+We value thoughtful feedback, technical discussions, constructive criticism, and new perspectives.
 
-We welcome thoughtful feedback, technical discussions, questions, suggestions, 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.
 
-If you find something interesting in our work, tell us what you think. If you spot a limitation, let us know. If you have an idea that could improve an experiment, we'd love to hear about it.
+We believe the AI community benefits when people share knowledge, challenge assumptions, and learn from one another.
 
-The AI community moves forward 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.
+
+---
 
 ## 🚀 Looking ahead
 
-OpenModel-1 is our starting point, and we're excited about the possibilities ahead.
+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.
 
-As the project develops, we want to explore more architectural ideas, improve our training and evaluation methods, experiment with new capabilities, and build a stronger understanding of what makes language models effective.
+Future decisions will depend on what we learn, what we can implement, and which directions show the most promise.
 
-We want future releases to reflect what we learn rather than follow a roadmap just for the sake of having one.
+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.
 
-That means staying curious, being willing to revise our assumptions, and focusing on meaningful progress over hype.
+There is plenty left to explore, plenty to improve, and plenty of questions we haven't answered yet.
 
-There's a lot we haven't explored yet, and that's exactly why we're excited to keep going.
+That's what makes the journey exciting.
+
+---
 
 ## 💙 Let's build something different
 
-OpenModel is a journey of research, experimentation, and discovery.
+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, try things that might work, learn from the things that don't, and gradually turn ideas into real models and useful research.
+We're here to ask questions, test ideas, learn from failures, celebrate improvements, and turn promising concepts into real projects.
 
-We have plenty to learn, plenty to build, and plenty of questions left to investigate.
+We have a lot to learn, a lot to build, and a lot of possibilities ahead.
 
-**This is just the beginning.**
+**This is only the beginning.**
 
 Welcome to OpenModel.
 
-Let's experiment. Let's learn. Let's build.
+**Let's experiment. Let's learn. Let's build.**
 
-**— The OpenModel team**
+— The OpenModel team