README.md

<!-- KlusAI • Hugging Face Org Card -->

<p align="center">
  <strong>KlusAI</strong><br>
  <em>Where AI research meets real-world impact</em>
</p>

<p align="center">
  <a href="https://www.klusai.com">
    <img src="https://img.shields.io/badge/Website-klusai.com-blue?logo=google-chrome&logoColor=white" alt="Website">
  </a>
  <a href="https://github.com/klusai">
    <img src="https://img.shields.io/badge/GitHub-@klusai-black?logo=github" alt="GitHub">
  </a>
  <a href="https://x.com/klusai">
    <img src="https://img.shields.io/badge/X-@klusai-black?logo=x&logoColor=white" alt="X">
  </a>
  <a href="https://www.klusai.com/research/">
    <img src="https://img.shields.io/badge/Research-klusai.com-brightgreen?logo=beaker&logoColor=white" alt="Research">
  </a>
</p>

---

## 🔍 What We're About

KlusAI bridges the gap between cutting-edge AI research and production systems. We publish our datasets and models openly to advance the field — **9M+ synthetic training examples** and counting.

**Research Themes:**
- 🧬 **Synthetic Data Generation** — Large-scale training data without privacy concerns
- ⚡ **Efficient AI Systems** — Models that run on consumer hardware
- 🌍 **Multilingual NLP** — With deep Romanian language expertise

---

## 📄 Featured Publication

### Synthetic Data Generation Using Large Language Models
*Advances in Text and Code* — **IEEE Access, 2025**

Our comprehensive survey on generating training data using LLMs. How enterprises can generate training data at scale — reducing annotation costs, addressing data scarcity, and enabling fine-tuning without exposing sensitive data.

📖 [Read on IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/11080380) · 📝 [arXiv Preprint](https://arxiv.org/abs/2503.14023)

---

## 🔬 Flagship Project: TinyFabulist

**TinyFabulist** is our open research programme on large-scale synthetic narrative generation. We demonstrate that small, efficient models can produce high-quality training data at scale.

| Release | Description | Size |
|---------|-------------|------|
| **TinyFabulist v1** | Synthetic English Fables | ~3M examples |
| *Upcoming* | Multilingual extensions, evaluation benchmarks | — |

**Key principles:**
- 📊 **Scale** — 9M+ synthetic training examples generated
- 🔧 **Efficiency** — All content produced with ≤8B parameter models
- 🔓 **Openness** — Generation scripts, pipelines, and methodology shared publicly

📄 [Paper (arXiv)](https://arxiv.org/abs/2504.20605) · 💻 [Code (GitHub)](https://github.com/klusai/tinyfabulist)

---

## 📦 What You'll Find Here

- **Datasets** — Large-scale synthetic training corpora for fine-tuning and research
- **Models** — Efficient, instruction-tuned models optimized for specific tasks
- **Evaluation** — Benchmarks and tooling for synthetic data quality assessment

---

## 🤝 Work With Us

Beyond open research, we offer enterprise AI services:

| Service | Description |
|---------|-------------|
| **AI Strategy** | Define your AI roadmap and implementation plan |
| **Custom Development** | Bespoke AI solutions tailored to your domain |
| **Model Training** | Fine-tuning and deploying models for your use case |
| **MLOps & Infrastructure** | Scalable pipelines and production deployment |

**Need custom synthetic data or domain-specific models?** We partner with organizations on applied research challenges.

---

## 📫 Get in Touch

| Purpose | Contact |
|---------|---------|
| Research collaboration | [research@klusai.com](mailto:research@klusai.com) |
| Enterprise services | [services@klusai.com](mailto:services@klusai.com) |
| General inquiries | [hello@klusai.com](mailto:hello@klusai.com) |

> **Technical questions?** Open an issue on the relevant dataset or model repository.

---

<p align="center">
  <strong>Applied Research · AI Services · Ventures</strong><br>
  <a href="https://klusai.com">klusai.com</a> · <a href="https://github.com/klusai">GitHub</a> · <a href="https://x.com/klusai">X</a>
</p>