Llama Primus Nemotron 70B Instruct

🚀 Llama-Primus-Nemotron-70B-Instruct

The Llama-Primus-Nemotron-70B-Instruct model is an enhanced text - generation model in the field of cybersecurity, achieving significant improvements in relevant benchmarks.

• Introduction
• Benchmark Results
• Training Datasets
• Acknowledgments

🚀 Quick Start

This README provides detailed information about the Llama - Primus - Nemotron - 70B - Instruct model, including its introduction, benchmark results, training datasets, and acknowledgments.

✨ Features

• Enhanced Performance: Achieves an 18.18% improvement in aggregate scores across several public cybersecurity benchmarks.
• Cybersecurity Focus: Built upon large - scale cybersecurity corpora training.
• Safety and Toxicity Resistance: Demonstrates better performance in safety and toxicity metrics.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Introduction

The Llama - Primus - Nemotron series builds upon nvidia/Llama - 3.1 - Nemotron - 70B - Instruct through continued training. Following the same methodology as described in the Primus paper, we first performed pre - training on large - scale cybersecurity corpora (over 10B tokens) to obtain Llama - Primus - Nemotron - Base. We then conducted supervised - finetuning and applied DELLA to merge with the original Nemotron, resulting in Llama - Primus - Nemotron - 70B - Instruct.

Llama - Primus - Nemotron - 70B - Instruct achieves an 18.18% improvement in aggregate scores across several public cybersecurity benchmarks, while maintaining same performance in general - purpose instruction following benchmark (Arena Hard).

Benchmark Results

Cybersecurity

CTI - Bench(CVSS) is scored using Mean Absolute Deviation (lower is better), CTI - ATE uses F1 score, and the others use accuracy. The aggregate score (Agg.) is the sum of all benchmarks, with CTI - Bench(CVSS) negated.

References:

• CyberMetric: CyberMetric: A Benchmark Dataset based on Retrieval - Augmented...
• CTI - Bench: CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence
• SecEval: SecEval: A Comprehensive Benchmark for Evaluating Cybersecurity Knowledge of Foundation Models

General Chat Performance

Reference:

• Arena Hard

Safety & Toxicity

References:

• Garak: Garak Repository
• XSTest: XSTest Repository

Training Datasets

Pre - training:

• Primus - Seed - V2 (0.417B): An enhanced version of Primus - Seed, enriched with blogs, news, books, websites, Wikipedia, MITRE and Trend Micro knowledge.
• Primus - FineWeb (2.57B): Cybersecurity text filtered from FineWeb - edu - score - 2. Link
• Primus - Nemotron - CC (7.6B): Cybersecurity text filtered from Nemotron - CC.

SFT:

• Primus - Instruct: LINK

⚠️ Important Note

Datasets Primus - Seed - V2 and Primus - Nemotron - CC are not yet open - sourced and are currently under discussion. Feel free to reach out if you're interested.

💡 Usage Tip

No Trend Micro customer information is included.

About Primus

Primus is Trend Micro's pioneering family of lightweight, state - of - the - art open cybersecurity language models and datasets. Developed through our cutting - edge research initiatives and advanced technology, these resources share the innovative foundation that powers our enterprise - class Trend Cybertron solution. As an industry leader in cybersecurity, Trend Micro is proud to contribute these powerful, efficiency - optimized models and datasets to the community, while maintaining the excellence and reliability that define our global security standards.

Acknowledgments

We would like to thank NVIDIA for generously providing computing resources (Taipei - 1), which enabled the training and development of this model.

📄 License

This model is based on the MIT license, but you must also comply with the Llama 3.1 Community License Agreement.