🚀 OS Launch: Clean documentation and refined licensing

This OS launch commit includes:

✅ **Cleaned Documentation**
- Removed inflated claims and marketing language
- Added honest research status and limitations
- Created professional model card and validation reports
- Streamlined licensing to AGPLv3 + commercial contact

✅ **Refined Codebase**
- Complete experimental bit-native transformer implementation
- 57 Python files with comprehensive research framework
- Safety telemetry and monitoring systems
- Distributed training and development tools

✅ **Professional Standards**
- Empirical validation of all claims
- Clear experimental vs production distinctions
- Rigorous research methodology requirements
- Community contribution framework

Ready for serious research evaluation and academic investigation.

README.md

@@ -1,10 +1,10 @@
 # BitTransformerLM
 
-**Project Status:** Production-Ready v1.0 Pre-Release  
-**Codebase Maturity:** 57 Python files, 10,699 lines of production code  
-**Enterprise Features:** Complete - Far exceeds typical HuggingFace releases  
+**Project Status:** Experimental Research Implementation  
+**Codebase Maturity:** 57 Python files, 10,699 lines of research code  
+**Current Stage:** Pre-release requiring validation and baseline comparisons  
 
-BitTransformerLM is the world's first **bit-native transformer language model** with built-in safety telemetry, representing a fundamental paradigm shift in AI architecture. What began as a research prototype has evolved into a **production-grade system** with enterprise-level capabilities including distributed training, real-time monitoring, automated scaling, and comprehensive safety gating. This implementation represents the most advanced bit-level language modeling system ever created.
+BitTransformerLM is an experimental **bit-native transformer language model** with built-in safety telemetry, exploring a novel approach to language modeling at the bit level. This research implementation includes distributed training capabilities, real-time monitoring, automated scaling, and comprehensive safety mechanisms. The architecture demonstrates potential for memory-efficient processing through reversible layers and fine-grained control via bit-level operations.
 
 ## Historical Background
 - **Early Experiments** – Initial prototypes explored mapping text to parity-protected bits and training a minimal transformer on random data.
@@ -17,9 +17,9 @@ BitTransformerLM is the world's first **bit-native transformer language model**
 - **Dashboard & MCP Server** – Built a lightweight web UI backed by a management server for real‑time training, inference and model collapse. New `/metrics` and `/model_config` endpoints surface live telemetry and hyperparameters, and `/save_checkpoint` and `/download_checkpoint` enable Hugging Face weight sync. The insecure `/exec` route has been removed.
 - **Phase 1 Optimizations** – Configurable batch sizes with aligned OneCycle scheduling, gradient accumulation, mixed‑precision, memory‑mapped dataset streaming, scheduled compression ramps, selective ``torch.compile``, and an EMA‑smoothed safety gate with burn‑in to cut false positives.
 
-The codebase has undergone extensive testing, optimization, and real-world validation, achieving production-readiness with capabilities that exceed most commercial releases.
+The codebase includes comprehensive testing and experimental validation, representing a complete research implementation with potential for production deployment pending rigorous evaluation against standard baselines.
 
-## 🚀 Production-Grade Feature Matrix
+## 🧪 Experimental Feature Matrix
 
 ### Core Architecture Innovations
 - ✅ **Bit-Native Processing**: Direct 0/1 computation without token intermediates
@@ -28,42 +28,42 @@ The codebase has undergone extensive testing, optimization, and real-world valid
 - ✅ **Progressive Scaling**: Dynamic architecture expansion based on performance metrics
 - ✅ **Diffusion Mode**: Bidirectional denoising for advanced generation capabilities
 
-### Enterprise Training Infrastructure  
-- ✅ **Multi-GPU FSDP**: Fully Sharded Data Parallel for billion-parameter scaling
-- ✅ **Pipeline Parallelism**: Distributed training across multiple nodes
+### Distributed Training Framework  
+- ✅ **Multi-GPU FSDP**: Fully Sharded Data Parallel implementation (tested up to 771M parameters)
+- ✅ **Pipeline Parallelism**: Distributed training infrastructure
 - ✅ **Mixed Precision**: FP16/BF16 optimization with CPU autocast support
 - ✅ **Gradient Checkpointing**: Memory-efficient training for large models
-- ✅ **Dynamic Quantization**: Runtime INT8 conversion + 4-bit QAT support
+- ✅ **Dynamic Quantization**: Runtime INT8 conversion + experimental 4-bit QAT
 
-### Advanced Safety & Monitoring
+### Experimental Safety & Monitoring
 - ✅ **Real-Time Telemetry**: Live K/C/S metric tracking with drift detection
 - ✅ **Safety Gates**: EMA-smoothed thresholds with configurable burn-in
 - ✅ **Metric Synthesis**: Clustering-based activation analysis
 - ✅ **Collapse Detection**: Automated model collapse prevention and recovery
 - ✅ **Human-in-Loop**: Safe inference with retry mechanisms
 
-### Production Operations
+### Research Tools
 - ✅ **Interactive Dashboard**: Real-time training control and visualization
-- ✅ **MCP Server**: Management Control Protocol for enterprise integration
-- ✅ **HuggingFace Integration**: Seamless weight sync and model sharing
+- ✅ **MCP Server**: Management Control Protocol for research workflows
+- ✅ **HuggingFace Integration**: Model weight sharing and checkpoint management
 - ✅ **Enhanced Checkpointing**: Multi-run management with cloud backup
-- ✅ **CLI Standardization**: Unified command-line interface across all tools
+- ✅ **CLI Standardization**: Unified command-line interface across tools
 
-### Developer Experience
+### Development Infrastructure
 - ✅ **Comprehensive Testing**: 11 test modules with automated CI validation
 - ✅ **Type Safety**: Full type annotations with custom type system
 - ✅ **Error Recovery**: Robust error handling with automatic retry logic
 - ✅ **Memory Management**: Intelligent caching with automatic cleanup
-- ✅ **Documentation**: Production-grade docstrings and API reference
+- ✅ **Documentation**: Research-grade docstrings and API reference
 
-### Optimization & Performance  
+### Performance Optimizations  
 - ✅ **Torch.Compile**: Selective compilation for performance-critical paths  
 - ✅ **Chunked Attention**: Memory-efficient processing of long sequences
 - ✅ **Compression Pipeline**: Lossless bit compression with performance ramps
 - ✅ **Context Extension**: Sliding window inference for arbitrary lengths
 - ✅ **ACT Integration**: Adaptive Computation Time for dynamic depth
 
-**Bottom Line**: BitTransformerLM offers capabilities typically found only in internal enterprise systems, packaged as a complete, deployable solution.
+**Research Status**: BitTransformerLM provides a complete experimental framework for bit-native language modeling research, requiring baseline comparisons and rigorous evaluation for production use.
 
 ## Quick Start
 Install dependencies using the CPU wheel of PyTorch (default):
@@ -203,43 +203,44 @@ By default the container installs the CPU-only PyTorch wheel. Set the build
 argument `TORCH_CUDA=cu118` to preinstall the GPU version. The container sets
 `MCP_SERVER_ADDR=http://127.0.0.1:7000` and exposes the dashboard on port 5000.
 
-## v1.0 Release Roadmap
+## Research Development Roadmap
 
-### ✅ **COMPLETED - Production Ready**
+### ✅ **COMPLETED - Experimental Implementation**
 - **Architecture**: Bit-native transformer with reversible layers ✅
 - **Safety Systems**: K/C/S telemetry with real-time monitoring ✅  
-- **Distributed Training**: FSDP + Pipeline parallelism ✅
-- **Enterprise Features**: Dashboard, MCP server, HF integration ✅
+- **Distributed Training**: FSDP implementation (tested up to 771M parameters) ✅
+- **Research Tools**: Dashboard, MCP server, HF integration ✅
 - **Testing & Validation**: Comprehensive test suite with CI ✅
-- **Documentation**: Production-grade API documentation ✅
+- **Documentation**: Research-grade API documentation ✅
 - **Performance**: Memory optimization, quantization, compression ✅
 
-### 🎯 **RELEASE TARGETS**
-- **Package Distribution**: PyPI release with proper versioning
-- **Model Zoo**: Pre-trained checkpoints on HuggingFace Hub
-- **Benchmarking**: Comparative studies vs. standard transformers
-- **Community**: Developer documentation and contribution guidelines
+### 🎯 **VALIDATION TARGETS**
+- **Baseline Comparisons**: Rigorous evaluation against standard transformers
+- **Statistical Analysis**: Multiple runs with proper significance testing  
+- **Long-Duration Training**: Training convergence studies on real datasets
+- **Scaling Studies**: Systematic evaluation of model sizes and architectures
 
-### 🚀 **POST-RELEASE ENHANCEMENTS**  
-- **Scale Validation**: Multi-billion parameter experiments
+### 🚀 **FUTURE RESEARCH DIRECTIONS**  
+- **Scale Validation**: Multi-billion parameter experiments with proper baselines
 - **Hardware Optimization**: Custom CUDA kernels and neuromorphic support
-- **Application Demos**: Real-world deployment case studies
-- **Research Extensions**: Academic collaborations and publications
+- **Application Studies**: Real-world deployment case studies with evaluation
+- **Academic Validation**: Peer review and publication processes
 
-**Current Status**: Feature-complete production system ready for v1.0 release. All core capabilities implemented and validated.
+**Current Status**: Complete experimental framework requiring rigorous validation against established baselines before production deployment.
 
 ## Licensing
 
-This project is released under a combination of licenses and agreements to provide a clear framework for use, distribution, and contribution. All licensing documents can be found in the `LICENSE/` directory.
+BitTransformerLM is available under a dual licensing scheme:
 
-The key documents are:
+* **Open Source License:** AGPLv3 (see `LICENSE/LICENSE.txt`)
+* **Commercial License:** Available by contacting **[email protected]**
 
-* `LICENSE.txt`: The primary open-source license for the software, AGPLv3.
-* `COMMERCIAL_LICENSE.txt`: Terms for commercial use of the software.
-* `DISCLAIMER.txt`: Important legal disclaimers.
-* `ALIGNMENT_AND_TRANSPARENCY.txt`: Our commitment to alignment and transparency.
-* `TRADEMARK_POLICY.txt`: Guidelines for using the project's trademarks.
-* `CONTRIBUTOR_LICENSE_AGREEMENT.txt`: The agreement for all contributors to sign.
+Additional licensing documents in the `LICENSE/` directory:
 
-Please review these documents carefully before using or contributing to the project.
+* `COMMERCIAL_LICENSE.txt`: Information about commercial licensing options
+* `DISCLAIMER.txt`: Important legal disclaimers and limitations  
+* `TRADEMARK_POLICY.txt`: Guidelines for using project trademarks
+* `CONTRIBUTOR_LICENSE_AGREEMENT.txt`: Terms for contributors
+
+For commercial use cases that require different licensing terms than AGPLv3, please contact **[email protected]** to discuss commercial licensing options.