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Symbolic Vector Entropy and Latent Tensor Excavation

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SVELTE Framework

Symbolic Vector Entropy & Latent Tensor Excavation

Advanced Model Analysis & Interpretability Suite for Neural Language Models
Non-destructive extraction and interpretation of computational structures from quantized language models

📝 Table of Contents

🧐 About

SVELTE (Symbolic Vector Entropy & Latent Tensor Excavation) is a comprehensive framework for analyzing and interpreting neural language models through non-destructive tensor analysis. The framework extracts symbolic computational structures from quantized models, enabling deep understanding of model internals through entropy analysis, symbolic mapping, and multi-dimensional visualization.

Built on axiomatic foundations of latent symbolic encoding and structural invariance, SVELTE provides researchers and practitioners with tools to excavate the hidden computational archaeology of modern language models.

🚀 Features

Core Analysis Modules

  • Tensor Excavation: Non-destructive extraction of complete tensor structures from GGUF/GGML models
  • Entropy Analysis: Multi-dimensional information entropy mapping and semantic density analysis
  • Symbolic Mapping: Transformation of numeric patterns into interpretable symbolic representations
  • Attention Topology: Differential geometric analysis of attention mechanisms
  • Memory Pattern Recognition: Identification of recurring computational motifs and structures

Advanced Capabilities

  • Interactive Visualization: Multi-scale cognitive cartography with WebGL-accelerated rendering
  • Cross-Model Comparison: Comparative analysis across different architectures and sizes
  • Quantization Preservation: Analysis that maintains fidelity to original quantized representations
  • Regulatory Compliance: Automated documentation generation for safety and compliance review
  • Intervention Planning: Targeted modification strategies for model behavior adjustment

Deployment Options

  • Single-Node: Workstation deployment for smaller models
  • Distributed: Cluster-based analysis for large-scale models
  • Cloud-Native: Container-based microservices with auto-scaling
  • Hybrid: Edge preprocessing with cloud-based deep analysis

🏁 Getting Started

Prerequisites

Hardware Requirements:

  • Minimum: 16-core CPU, 64GB RAM, 8GB VRAM
  • Recommended: 32-core CPU, 128GB RAM, 24GB VRAM
  • Storage: NVMe SSD with 2GB/s+ speeds, 5-10x model size available

Software Dependencies:

Python 3.8+
PyTorch 2.0+
NumPy 1.20+
SciPy 1.7+
Plotly 5.5+

Installation

  1. Clone the repository:
git clone https://github.com/svelte-framework/svelte.git
cd svelte
  1. Install dependencies:
pip install -r requirements.txt
  1. Install SVELTE framework:
pip install -e .
  1. Verify installation:
python main.py --help

🎈 Usage

Command Line Interface

Full Pipeline Analysis:

python main.py --model models/your-model.gguf --pipeline --output results/

Selective Module Analysis:

python main.py --model models/your-model.gguf --modules parser,entropy,symbolic --headless

Interactive Mode:

python main.py
# Follow interactive prompts to select models and analysis options

Python API

from src.gguf_diagnostic_scanner import main as pipeline_main
from src.tensor_analysis.gguf_parser import GGUFParser
from src.tensor_analysis.entropy_analysis import EntropyAnalysisModule

# Parse model
parser = GGUFParser("path/to/model.gguf")
parser.parse()

# Analyze entropy
entropy_module = EntropyAnalysisModule(parser.tensor_field)
entropy_maps = entropy_module.compute_entropy()

# Run full pipeline
pipeline_main()

Web Interface

Start the web server:

python main.py serve --port 8080

Navigate to http://localhost:8080 for interactive analysis.

🏗️ Architecture

Core Processing Pipeline

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Tensor         │    │  Entropy        │    │  Symbolic       │
│  Excavation     │───▶│  Analysis       │───▶│  Mapping        │
│  Module (TEM)   │    │  Module (EAM)   │    │  Module (SMM)   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Cognitive      │    │  Meta-          │    │  Attention      │
│  Cartography    │    │  Interpretation │    │  Topology       │
│  Module (CCM)   │    │  Module (MISM)  │    │  System (ATS)   │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Module Specifications

  • TEM: GGUF parser, tensor field constructor, quantization reconstructor
  • EAM: Multi-dimensional entropy calculator, gradient field generator
  • SMM: Computational grammar extractor, pattern recognition engine
  • CCM: Interactive visualization, attention curvature mapping
  • MISM: Cross-module integration, reasoning chain reconstruction

📚 API Reference

REST API Endpoints

GET    /api/v1/models                 # List available models
POST   /api/v1/models                 # Upload new model
GET    /api/v1/models/{id}/analyze    # Start analysis
GET    /api/v1/analyses/{id}/status   # Check analysis status
GET    /api/v1/analyses/{id}/results  # Retrieve results
GET    /api/v1/symbols                # Access pattern library
POST   /api/v1/visualizations         # Generate visualization

Configuration Options

# analysis.yaml
analysis:
  modules: [parser, entropy, symbolic, attention]
  resolution: 512
  threads: 0  # auto-detect
  memory_limit: 0  # auto-detect
  output_format: [json, visualization, report]
  
visualization:
  engine: plotly
  interactive: true
  dimensions: 3
  color_scheme: viridis

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Install development dependencies: pip install -r requirements-dev.txt
  4. Run tests: pytest tests/
  5. Commit changes: git commit -m 'Add amazing feature'
  6. Push to branch: git push origin feature/amazing-feature
  7. Open a Pull Request

Code Style

  • Follow PEP 8 style guidelines
  • Use type hints for function signatures
  • Include docstrings for all public functions
  • Maintain test coverage above 80%

📖 Documentation

⛏️ Built Using

  • PyTorch - Tensor Operations & Neural Network Framework
  • NumPy - Numerical Computing Foundation
  • SciPy - Scientific Computing Library
  • Plotly - Interactive Visualization Engine
  • FastAPI - Modern Web API Framework
  • React - Frontend User Interface
  • D3.js - Data-Driven Visualization
  • WebGL - High-Performance Graphics

✍️ Authors

  • SVELTE Development Team - Framework Architecture & Implementation
  • Contributing Researchers - Theoretical Foundations & Validation

See the list of contributors who participated in this project.

🎉 Acknowledgements

  • Theoretical foundations inspired by work in differential geometry and information theory
  • Visualization techniques adapted from cognitive science and neuroscience research
  • Community contributions from researchers in AI safety and interpretability
  • Open source libraries and frameworks that make this work possible

Citation:

@software{svelte_framework,
  title={SVELTE: Symbolic Vector Entropy & Latent Tensor Excavation Framework},
  author={SVELTE Development Team},
  year={2025},
  url={https://github.com/svelte-framework/svelte}
}

License: MIT - see LICENSE file for details.

Project Link: https://github.com/svelte-framework/svelte

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