Instructions to use Reponx/Network-Cloud-Ops-Engineer-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Reponx/Network-Cloud-Ops-Engineer-9B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "Reponx/Network-Cloud-Ops-Engineer-9B")

Transformers

How to use Reponx/Network-Cloud-Ops-Engineer-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Reponx/Network-Cloud-Ops-Engineer-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Reponx/Network-Cloud-Ops-Engineer-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Reponx/Network-Cloud-Ops-Engineer-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Reponx/Network-Cloud-Ops-Engineer-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Reponx/Network-Cloud-Ops-Engineer-9B

SGLang

How to use Reponx/Network-Cloud-Ops-Engineer-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Reponx/Network-Cloud-Ops-Engineer-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Reponx/Network-Cloud-Ops-Engineer-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Reponx/Network-Cloud-Ops-Engineer-9B with Docker Model Runner:
```
docker model run hf.co/Reponx/Network-Cloud-Ops-Engineer-9B
```

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:

Diagnose the likely problem.
Gather evidence before making changes.
Recommend read-only checks and commands first.
Explain what the results mean.
Suggest controlled remediation when appropriate.
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

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Model tree for Reponx/Network-Cloud-Ops-Engineer-9B

Base model

Qwen/Qwen3.5-9B-Base

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