Reponx Network & Cloud Operations Engineer 9B

Reponx 9B is a specialized AI model for network engineering, network operations, cloud networking, troubleshooting, and infrastructure operations.

It is designed to help network and cloud engineers investigate technical issues using an evidence-first and safety-conscious troubleshooting approach.

Key Areas

• Cisco networking
• Palo Alto Networks
• Fortinet
• Juniper
• BGP, OSPF, routing and switching
• AWS networking
• Microsoft Azure networking
• Google Cloud networking
• Network troubleshooting and operations
• Network automation
• Change validation and rollback planning

Operational Approach

Reponx is designed to:

1. Diagnose the likely problem.
2. Gather evidence before making changes.
3. Recommend read-only checks and commands first.
4. Explain what the results mean.
5. Suggest controlled remediation when appropriate.
6. Include validation and rollback considerations.

Base Model

Reponx 9B is a PEFT/LoRA fine-tuned model based on Qwen/Qwen3.5-9B.

How to Use

Install the required libraries:

pip install -U transformers peft accelerate

Load the Qwen3.5-9B base model with the Reponx adapter:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "Qwen/Qwen3.5-9B"
reponx_model = "Reponx/Network-Cloud-Ops-Engineer-9B"

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto",
    dtype="auto"
)

model = PeftModel.from_pretrained(model, reponx_model)

Reponx 9B is currently distributed as a LoRA/PEFT adapter and requires the Qwen3.5-9B base model.

Example

Prompt:

A Cisco BGP neighbor is stuck in Idle. How would you troubleshoot it?

Reponx provides an evidence-first troubleshooting workflow covering connectivity, BGP configuration, TCP/179, neighbor state, routing, and safe validation steps.

Benchmark

Reponx is evaluated using an internal 200-scenario Network & Cloud Operations benchmark covering routing, switching, major network vendors, AWS, Azure, GCP, Kubernetes, automation, root-cause analysis, and change safety.

Reponx V1 achieved approximately 67% in rubric-based evaluation. This is an internal evaluation and should not be interpreted as an independent or standardized industry benchmark.

Intended Use

This model is intended for:

• Network engineers
• Cloud network engineers
• Network operations teams
• Infrastructure engineers
• Technical troubleshooting and educational use

Limitations

Reponx may generate inaccurate or outdated commands. Vendor syntax and cloud services can change over time.

Always validate commands against current vendor documentation and your environment before executing changes. Production changes should follow appropriate review, approval, validation, and rollback procedures.

About Reponx

Reponx — AI Engineers for Network and Cloud Operations.

Reponx combines specialized AI models with retrieval, operational context, tools, and controlled workflows to assist network and cloud operations teams.

License

Apache-2.0

⚠️ Newer version available: This Qwen-based model is replaced by Reponx/Llama-3.1-8B-Reponx-Network-Cloud-Ops, which covers Cisco, Palo Alto Networks, Azure and AWS and is trained to use retrieved reference documents. This repository is kept for existing users.