| # BitTransformerLM v0.1.0 - Experimental Research Release | |
| **Release Date:** August 2025 | |
| **Status:** Open Source Research Implementation | |
| **License:** AGPLv3 + Commercial Licensing Available | |
| ## What's Included | |
| This release provides a complete experimental framework for bit-native language modeling research: | |
| - **Core Architecture:** 57 Python files implementing bit-native transformer with reversible layers | |
| - **Safety Systems:** Real-time K/C/S telemetry and monitoring | |
| - **Research Tools:** Interactive dashboard, distributed training, comprehensive testing | |
| - **Documentation:** Professional model card, research status, and validation reports | |
| ## Important Notes | |
| ⚠️ **Experimental Status:** This is research code requiring rigorous baseline validation | |
| ⚠️ **Not Production Ready:** Needs extensive evaluation vs standard transformers | |
| ⚠️ **Research Use Only:** Intended for academic investigation and experimentation | |
| ## Licensing | |
| - **Open Source:** AGPLv3 for research and open source use | |
| - **Commercial:** Contact contact@wcnegentropy.com for commercial licensing | |
| ## Next Steps | |
| The research community is invited to: | |
| 1. Conduct rigorous baseline comparisons vs standard transformers | |
| 2. Evaluate on established language modeling benchmarks | |
| 3. Validate (or refute) claimed memory efficiency benefits | |
| 4. Share findings openly to advance the field | |
| **Research responsibly. Validate rigorously. Share openly.** | |