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Fuxi-Ops-Model

Fuxi-Ops-Model

Large Language Model for Server Operations and Automated Script Generation

Project Overview

SparkCore Network Studio

📌 Fuxi (Fuxi-OM)

A Large Language Model for Server Operations & Automation Script Development

📖 Project Overview

Fuxi (Fuxi-OM) is a large language model launched by SparkCore Network Studio, specifically designed for server operations and automation script development.

The model has been trained on a vast corpus of operations documentation, Shell/Python/Ansible scripts, cloud platform API manuals, and real-world production troubleshooting cases, enabling it to:

  • Generate secure, executable operations scripts directly from natural language requirements
  • Analyze existing configurations and logs, and provide optimization recommendations
  • Simulate troubleshooting processes to assist in rapidly pinpointing online issues
  • Support interactive command generation, seamlessly embedding into daily operations workflows

This project provides the open-source weights, inference code, and supporting tools for Fuxi-OM, aiming to lower the barrier to automated operations and promote the democratization of AIOps (AI for IT Operations) technology.

Features

  • Purpose-Built for Ops: Built upon a base language model with extensive continual pre-training and instruction fine-tuning in the operations domain, with deep understanding of core concepts such as crontab, systemd, iptables, docker, and k8s
  • Multi-Language Script Generation: Supports a wide range of operations scripting languages
Bash PowerShell Python Ansible YAML Terraform HCL
  • High-Security Alerts: Every generated script includes risk warnings and permission notes to prevent misoperation
⚠️ For example, high-risk commands like rm -rf will trigger explicit warnings
  • Interactive Dialogue: Supports multi-turn conversations, allowing you to refine requirements step by step — for instance, "First write a monitoring script, then add DingTalk alerting"
  • Lightweight Options: Offers 4‑bit quantized and GGUF versions, capable of running on standard servers or even a Raspberry Pi
  • Local & Offline: All inference is performed locally, with no need to upload sensitive operations data to third parties

Screenshots

Tech Stack

Python C++