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# FlowFinal: Comprehensive Technical Documentation
This directory contains detailed technical documentation for the FlowFinal antimicrobial peptide generation model.
## Documentation Structure
### Core Architecture Components
1. **[Encoder Process](encoder_process.tex)** - ESM-2 contextual embedding extraction and preprocessing
- Sequence validation and preprocessing pipeline
- ESM-2 embedding extraction methodology
- Statistical normalization procedures
- Comprehensive algorithms for reproducibility
2. **[Compressor/Decompressor](compressor_decompressor.tex)** - Transformer-based compression architecture
- Hourglass pooling and unpooling operations
- 16× compression methodology (1280D → 80D)
- Joint training procedures and optimization
- Performance metrics and validation results
3. **[Flow Matching Model](flow_model_training.tex)** - Core generative model with CFG
- 12-layer transformer architecture with skip connections
- Classifier-Free Guidance implementation and theory
- H100-optimized training methodology
- CFG scale analysis and optimal conditioning
4. **[Decoder Process](decoder_process.tex)** - ESM-2 language model head decoder
- Probabilistic sequence sampling (non-cosine approach)
- Nucleus sampling with temperature control
- Advantages over cosine similarity methods
- Implementation details and performance metrics
### Pipeline Components
5. **[CFG Dataset & Generation Pipeline](cfg_dataset_generation_pipeline.tex)** - Complete system pipeline
- Multi-source data integration and validation
- Strategic masking for CFG training
- Advanced ODE integration methods (DOPRI5, RK4, Euler)
- End-to-end generation with quality control
6. **[Results Analysis & Conclusions](results_analysis_conclusions.tex)** - Comprehensive experimental analysis
- Complete catalog of all 80 generated sequences
- Dual validation results (HMD-AMP + APEX)
- Physicochemical property analysis
- Performance insights and future directions
## Key Results Summary
- **Total Sequences Generated**: 80 across 4 CFG scales
- **HMD-AMP Success Rate**: 8.8% overall, 20% for Strong CFG (scale 7.5)
- **Optimal CFG Scale**: 7.5 (balanced control and diversity)
- **Training Efficiency**: 2.3 hours convergence on H100 GPU
- **Model Size**: 607MB final checkpoint, 78M+ parameters
## Mathematical Framework
All documentation includes:
- Complete mathematical formulations
- Detailed algorithmic descriptions
- Performance benchmarks and validation
- Implementation-ready pseudocode
- Comprehensive references and citations
## Usage
These LaTeX files are designed for:
- Academic paper submission and peer review
- Technical documentation and reproducibility
- Educational materials for flow matching in proteins
- Implementation guidance for researchers
## Model Availability
The complete FlowFinal model, weights, and datasets are available at:
https://huggingface.co/esunAI/FlowFinal
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*Documentation generated on 2025-08-29 17:01:37*
*Total documentation: 6 comprehensive LaTeX files*
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