References and Third-Party Integrations
Last Updated: December 12, 2025
Purpose: Attribution and acknowledgment of upstream projects and libraries
This document lists the third-party libraries, tools, and upstream projects that AI-OS depends on or is inspired by. We are grateful to the open-source community for making these tools available.
Core Dependencies
Machine Learning & Deep Learning
| Library |
License |
Description |
Links |
| PyTorch |
BSD-3-Clause |
Deep learning framework |
Website · GitHub |
| Transformers |
Apache-2.0 |
State-of-the-art NLP models |
Website · GitHub |
| Accelerate |
Apache-2.0 |
Distributed training utilities |
GitHub |
| Datasets |
Apache-2.0 |
Dataset loading and processing |
GitHub |
| PEFT |
Apache-2.0 |
Parameter-efficient fine-tuning |
GitHub |
| Safetensors |
Apache-2.0 |
Safe tensor serialization |
GitHub |
| Hugging Face Hub |
Apache-2.0 |
Model hub integration |
GitHub |
| Library |
License |
Description |
Links |
| DeepSpeed |
Apache-2.0 |
Deep learning optimization library |
Website · GitHub |
| bitsandbytes |
MIT |
8-bit optimizers and quantization |
GitHub |
| FlashAttention |
BSD-3-Clause |
Fast and memory-efficient attention |
GitHub |
Tokenization
| Library |
License |
Description |
Links |
| SentencePiece |
Apache-2.0 |
Unsupervised text tokenizer |
GitHub |
| Protobuf |
BSD-3-Clause |
Protocol buffers for tokenizers |
GitHub |
Evaluation
| Library |
License |
Description |
Links |
| lm-eval |
MIT |
Language model evaluation harness |
GitHub |
| math-verify |
MIT |
Mathematical verification utilities |
PyPI |
Application Dependencies
Web & Networking
| Library |
License |
Description |
Links |
| aiohttp |
Apache-2.0 |
Async HTTP client/server |
GitHub |
| httpx |
BSD-3-Clause |
Modern HTTP client |
GitHub |
| Playwright |
Apache-2.0 |
Browser automation |
GitHub |
| BeautifulSoup4 |
MIT |
HTML/XML parsing |
Website |
| lxml |
BSD-3-Clause |
XML and HTML processing |
GitHub |
| Trafilatura |
Apache-2.0 |
Web scraping and text extraction |
GitHub |
Data & Validation
| Library |
License |
Description |
Links |
| Pydantic |
MIT |
Data validation using Python types |
GitHub |
| orjson |
Apache-2.0 / MIT |
Fast JSON library |
GitHub |
| PyYAML |
MIT |
YAML parser and emitter |
GitHub |
| NumPy |
BSD-3-Clause |
Numerical computing |
Website · GitHub |
CLI & UI
| Library |
License |
Description |
Links |
| Typer |
MIT |
CLI application framework |
GitHub |
| Rich |
MIT |
Rich text and formatting |
GitHub |
| Pillow |
HPND |
Python Imaging Library |
GitHub |
| pystray |
LGPL-3.0 |
System tray icon library |
GitHub |
| matplotlib |
PSF |
Plotting library |
GitHub |
| Markdown |
BSD-3-Clause |
Markdown parser |
GitHub |
| tkinterweb |
MIT |
Web browser widget for Tkinter |
GitHub |
| rapidfuzz |
MIT |
Fast string matching |
GitHub |
System & Utilities
| Library |
License |
Description |
Links |
| psutil |
BSD-3-Clause |
Process and system utilities |
GitHub |
| watchdog |
Apache-2.0 |
Filesystem event monitoring |
GitHub |
| tenacity |
Apache-2.0 |
Retry library |
GitHub |
| tqdm |
MIT / MPL-2.0 |
Progress bars |
GitHub |
| dbus-next |
MIT |
D-Bus for Python (Linux) |
GitHub |
Development Dependencies
| Library |
License |
Description |
Links |
| pytest |
MIT |
Testing framework |
GitHub |
| pytest-asyncio |
Apache-2.0 |
Async test support |
GitHub |
| Ruff |
MIT |
Fast Python linter |
GitHub |
| Black |
MIT |
Code formatter |
GitHub |
Documentation
| Tool |
License |
Description |
Links |
| MkDocs |
BSD-2-Clause |
Static site generator |
GitHub |
| Material for MkDocs |
MIT |
MkDocs theme |
GitHub |
Research References
AI-OS implements concepts from the following research:
Hierarchical Reasoning Models (HRM)
Mixture of Experts (MoE)
- Switch Transformers: Paper - Scaling to trillion parameter models
- GShard: Paper - Scaling giant models with conditional computation
Memory Optimization Techniques
- Gradient Checkpointing: Trade compute for memory during backpropagation
- Mixed Precision Training: FP16/BF16 training with loss scaling
- ZeRO Optimization: Memory-efficient distributed training (DeepSpeed)
Acknowledgments
Special thanks to:
- The PyTorch team for the foundational deep learning framework
- Hugging Face for the transformers ecosystem and model hub
- Microsoft DeepSpeed team for training optimizations
- The EleutherAI team for the evaluation harness
- All contributors to the open-source ML community
License Compliance
All dependencies are used in compliance with their respective licenses. AI-OS is licensed under the AI-OS Non‑Selling Attribution License (ANSL) v1.0 - see LICENSE for details.
For questions about licensing or attribution, please open an issue on GitHub.
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