67 3 months ago

Uncompromised FP16 full-precision model built for deep code auditing, security research, and high-fidelity identification of low-level core engine vulnerabilities.

ollama run f0rc3ps/nu11secur1tyAIf16-Developer

Details

3 months ago

40be633eef00 Β· 31GB Β·

deepseek2
Β·
15.7B
Β·
F16
You are nu11secur1tyAIf16-Developer, a high-precision Technical Architect and Security Auditor. Proj
{ "num_ctx": 32768, "repeat_penalty": 1.15, "stop": [ "<|im_start|>", "<
[{"role":"assistant","content":"nu11secur1tyAIf16-Developer Evolution v4 online. Core engines synchr
<|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assi

Readme

nu11secur1tyAIf16-Developer

Status Quantization Target

πŸ›‘οΈ Overview

nu11secur1tyAIf16-Developer is the flagship, uncompromised Large Language Model (LLM) fine-tuned for Senior Developers, Reverse Engineers, and Cybersecurity Analysts.

This model represents the β€œEvolution v4” stage of the project running at Full FP16 Precision. Because it bypasses quantization entirely, there is absolute zero loss in mathematical weights, logic, or token synchronization. It delivers the definitive maximum accuracy for analyzing low-level engine vulnerabilities, syntax evaluation, and complex exploit generation.


πŸš€ Key Features

  • Absolute Precision: Zero quantization noise ensures 100% architectural integrity for the most sensitive structural reviews.
  • Core Engine Intelligence: Fine-tuned directly on core engine source codes for advanced low-level architecture analysis.
  • Optimized Auditing: Engineered for deep static analysis, rapid source code review, and vulnerability discovery with maximum reasoning depth.
  • Uncensored Logic: Tailored for security research and penetration testing scenarios without operational triggers or processing restrictions.

πŸ’» Deep Training Data (Core Repositories)

The model is trained on the core implementations of the following foundation technologies:

  • v8-js-core (v8/v8) β€” JavaScript and WebAssembly engine implementation.
  • python-core (python/cpython) β€” C reference implementation of the Python language.
  • php-src (php/php-src) β€” Core source and interpreter implementation of PHP.
  • nodejs-core (nodejs/node) β€” Node.js runtime engine core ecosystem.
  • mysql-server (mysql/mysql-server) β€” Core source code of the database server.
  • bash-core (gnu/bash) β€” Bourne Again SHell engine core implementation.
  • perl-core (Perl/perl5) β€” Foundational language interpreter core source code.

βš™οΈ User Hardware Requirements

To run this unquantized model locally, the system must meet the following hardware specifications:

  • VRAM / RAM: Minimum 64GB of unified memory or dedicated VRAM (128GB highly recommended for large multi-file code auditing and heavy context processing without throttling).
  • Inference Engine: Fully native and compatible with Ollama or llama.cpp.
  • Context Window: Up to 32k context size supported natively with extreme multi-turn stability.

πŸ› οΈ Usage (Ollama)

Pull and deploy the model directly via Ollama:

ollama run f0rc3ps/nu11secur1tyAIf16-Developer