Remove MODEL_CARD.md - cleanup for OS launch

MODEL_CARD.md

@@ -1,144 +0,0 @@
-# BitTransformerLM Model Card
-
-## Model Details
-
-**Model Type:** Experimental Bit-Native Transformer Language Model  
-**Architecture:** Transformer with reversible layers and bit-level processing  
-**Developer:** WCNegentropy Research  
-**Release Date:** August 2025  
-**Version:** Pre-release Experimental  
-**License:** AGPLv3 (see LICENSE/ directory)  
-
-## Model Description
-
-BitTransformerLM is an experimental language model that processes text at the bit level rather than using traditional token-based approaches. The architecture explores potential memory efficiency improvements through reversible transformer layers and provides built-in safety monitoring through real-time telemetry.
-
-### Architecture Details
-- **Input Processing:** Direct binary sequence processing (0/1 bits)
-- **Attention Mechanism:** Multi-head self-attention on bit embeddings
-- **Layer Design:** Reversible transformer blocks for memory efficiency
-- **Safety Features:** Built-in K/C/S (Negentropy/Complexity/Symbiosis) telemetry
-- **Training Modes:** Causal autoregressive and experimental diffusion mode
-
-## Training Data and Methodology
-
-### Experimental Configurations Tested
-1. **Small-scale CPU Training (793K parameters)**
-   - Dataset: 4 samples, 16 sequence length
-   - Training time: 0.21 seconds
-   - Convergence: Achieved on toy data
-
-2. **Large-scale GPU Training (771M parameters)**
-   - Dataset: 5 text samples with zero-padding
-   - Hardware: Single GPU (despite multi-GPU claims in some docs)
-   - Training time: 11.47 seconds
-   - Architecture: d_model=1792, 20 layers, 28 attention heads
-
-### Limitations Identified
-- **Limited Training Data:** Experiments used minimal datasets insufficient for language modeling evaluation
-- **No Baseline Comparisons:** Missing comparative evaluation against standard transformers
-- **Scale Claims:** Some documentation overstated parameter counts and GPU usage
-- **Training Duration:** Short training periods insufficient for convergence assessment
-
-## Performance and Evaluation
-
-### Empirical Results (From test data)
-
-**Small Model (793K parameters):**
-- Final Loss: 0.629
-- Best Loss: 0.571
-- Success Rate: 100% on single test prompt
-- Telemetry: Empty (minimal data)
-
-**Large Model (771M parameters):**
-- Training Loss Progression: 11.84 → 18.65 → 17.15 → 8.15 → 5.35
-- Peak Memory Usage: 15.28 GB
-- Inference Success: 100% on 5 test prompts
-- Telemetry Metrics: K≈0.0013, C≈0.52, S≈0.46
-
-### Known Issues and Limitations
-
-1. **Experimental Status:** This is research code requiring rigorous validation
-2. **Training Data:** Evaluated only on toy datasets, not real language modeling tasks
-3. **Baseline Gaps:** No systematic comparison to established transformer architectures  
-4. **Scale Verification:** Largest validated model is 771M parameters, not 1B+ as claimed elsewhere
-5. **Convergence:** Training times too short to establish genuine convergence behavior
-
-## Intended Use and Applications
-
-### Research Applications ✅
-- Bit-level language modeling research
-- Memory-efficient transformer architecture studies  
-- Safety telemetry and monitoring system development
-- Experimental diffusion-based text generation
-
-### Production Applications ⚠️
-- **Not Recommended:** Requires extensive validation and baseline comparisons
-- **Missing:** Proper evaluation on standard datasets and benchmarks
-- **Needs:** Long-duration training studies and statistical significance testing
-
-## Ethical Considerations and Risks
-
-### Potential Benefits
-- Enhanced interpretability through bit-level processing
-- Built-in safety monitoring and gating mechanisms
-- Memory-efficient architecture exploration
-- Open research contributing to AI safety
-
-### Potential Risks  
-- **Overstated Capabilities:** Early documentation contained inflated claims
-- **Incomplete Evaluation:** Missing critical baseline comparisons
-- **Research Maturity:** Experimental status requires careful interpretation of results
-
-### Recommendations
-- Use for research and experimentation only
-- Conduct rigorous baseline comparisons before any production use
-- Validate claims through independent evaluation
-- Follow established ML research best practices
-
-## Technical Specifications
-
-### Model Architecture
-- **Bit Embedding Size:** Configurable (16-1792 tested)
-- **Attention Heads:** Configurable (2-28 tested)  
-- **Layers:** Configurable (1-20 tested)
-- **Max Sequence Length:** Configurable (16-512 tested)
-- **Reversible Layers:** Optional memory-efficient computation
-- **Quantization:** Experimental 4-bit QAT support
-
-### System Requirements
-- **Minimum:** Python 3.10+, PyTorch 2.7.1, 8GB RAM
-- **Recommended:** 16GB+ RAM, CUDA-capable GPU for larger models
-- **Dependencies:** See requirements.txt for complete specification
-
-### Training Features
-- FSDP distributed training support
-- Mixed precision (FP16/BF16) training
-- Progressive scaling and curriculum learning
-- Real-time telemetry and safety monitoring
-- Interactive dashboard for training control
-
-## Citation
-
-If you use BitTransformerLM in your research, please cite:
-
-```bibtex
-@software{bittransformerlm2025,
-  title={BitTransformerLM: Experimental Bit-Native Transformer Language Model},
-  author={WCNegentropy Research},
-  year={2025},
-  url={https://github.com/WCNegentropy/BitTransformerLM},
-  note={Experimental research implementation}
-}
-```
-
-## Additional Resources
-
-- **Repository:** [GitHub - WCNegentropy/BitTransformerLM](https://github.com/WCNegentropy/BitTransformerLM)
-- **Documentation:** README.md, AGENTS.md
-- **License:** AGPLv3 with additional terms (see LICENSE/ directory)
-- **Issues:** GitHub Issues for bug reports and feature requests
-
----
-
-**Disclaimer:** This is experimental research code. Claims in some historical documentation may be overstated. Users should conduct independent evaluation and validation before any production use. The model requires rigorous baseline comparisons and statistical validation to establish its capabilities relative to standard approaches.