| # BitTransformerLM v0.1.0 - Experimental Research Release |
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| **Release Date:** August 2025 |
| **Status:** Open Source Research Implementation |
| **License:** AGPLv3 + Commercial Licensing Available |
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| ## What's Included |
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| This release provides a complete experimental framework for bit-native language modeling research: |
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| - **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 |
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| ## Important Notes |
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| ⚠️ **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 |
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| ## Licensing |
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| - **Open Source:** AGPLv3 for research and open source use |
| - **Commercial:** Contact contact@wcnegentropy.com for commercial licensing |
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| ## Next Steps |
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| 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 |
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| **Research responsibly. Validate rigorously. Share openly.** |
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