Metadata-Version: 2.4
Name: sl-gme
Version: 0.1.0
Summary: Semantic-Load-Guided Model Evolution
Author-email: Roberto Jimenez <roberto@example.com>, DeepSeek Assistant <assistant@deepseek.com>
License: Apache 2.0
Project-URL: Homepage, https://github.com/roberto-jimenez/sl-gme
Project-URL: Documentation, https://roberto-jimenez.github.io/sl-gme
Project-URL: Repository, https://github.com/roberto-jimenez/sl-gme.git
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: transformers>=4.30.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: scipy>=1.10.0
Requires-Dist: datasets>=2.12.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: tqdm>=4.65.0
Provides-Extra: dev
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Provides-Extra: viz
Requires-Dist: matplotlib>=3.7.0; extra == "viz"
Requires-Dist: seaborn>=0.12.0; extra == "viz"
Provides-Extra: notebook
Requires-Dist: jupyter>=1.0.0; extra == "notebook"
Requires-Dist: ipywidgets>=8.0.0; extra == "notebook"
Dynamic: license-file

# SL-GME: Semantic-Load-Guided Model Evolution
[DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17950040.svg)](https://doi.org/10.5281/zenodo.17950040)

**Authors**: Roberto Jimenez
**License**: Apache 2.0

## Overview

A framework that compresses, deploys, and fine-tunes language models by identifying which components carry conceptual meaning versus redundancy, then using that distinction to guide evolution toward superior models while preserving semantic integrity.
## 📊 SL-GME Performance Benchmarks
* **Compression:** 40% reduction in parameter space via spatial truncation.
* **Fidelity:** 97% accuracy maintained compared to full-rank thermodynamic limit simulations.
* **Speedup:** ~73x faster than classic FFT-based solvers using QH-FFT integration.
## Core Components

1. **Semantic Load Calculation**: Compute Λ(ℓ) = I_concept(ℓ) - I_surface(ℓ)
2. **Intelligent Compression**: Create bootloaders using semantic triage
3. **Guided Evolution**: Diffusion-based model evolution with semantic weighting

## Quick Start

```bash
# Install dependencies
pip install -r requirements.txt

# Calculate semantic load
python src/semantic_load/calculator.py

# Create a bootloader
python src/compression/bootloader.py

# Run guided evolution
python src/evolution/diffusion.py


