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genpark-layer-norm-rms-norm-regularization-skill

mcp
Guvenlik Denetimi
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SUMMARY

LayerNorm and Root Mean Square Normalization (RMSNorm) with affine transformation parameters

README.md

genpark-layer-norm-rms-norm-regularization-skill

GitHub Stars
Python 3.9+
License: MIT
Zero External Dependencies
MCP Ready

LayerNorm and Root Mean Square Normalization (RMSNorm) with affine transformation parameters

Part of the GenPark Autonomous Agent Matrix, developed for production AI agents implementing on-device deep learning primitives, attention blocks, and differentiable computational graphs.

🏗️ Architecture

flowchart TD
    A[Tensors / Layer Inputs] --> B[genpark-layer-norm-rms-norm-regularization-skill]
    B --> C[Pure Python Standard Library Autograd / NN Engine]
    C --> D[Activated Embeddings / Attention Weights / Gradients]
    B --> E[MCP Protocol Endpoint stdio]
    E --> F[Cursor / Claude Desktop / Windsurf Integration]

🚀 Quickstart

Native Python Execution

python example_usage.py

Standard Library Verification

from client import *

MCP Server (Claude Desktop / Cursor)

{
  "mcpServers": {
    "genpark-layer-norm-rms-norm-regularization-skill": {
      "command": "python",
      "args": ["-m", "genpark_layer_norm_rms_norm_regularization_skill.mcp_server"]
    }
  }
}

📄 License

MIT License.

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