Instructions to use tnadmin/Sentinel-NX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tnadmin/Sentinel-NX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tnadmin/Sentinel-NX",
	filename="sentinel-nx-q6_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tnadmin/Sentinel-NX with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tnadmin/Sentinel-NX:Q6_K
# Run inference directly in the terminal:
llama cli -hf tnadmin/Sentinel-NX:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tnadmin/Sentinel-NX:Q6_K
# Run inference directly in the terminal:
llama cli -hf tnadmin/Sentinel-NX:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tnadmin/Sentinel-NX:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf tnadmin/Sentinel-NX:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tnadmin/Sentinel-NX:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tnadmin/Sentinel-NX:Q6_K

Use Docker

docker model run hf.co/tnadmin/Sentinel-NX:Q6_K

LM Studio
Jan

vLLM

How to use tnadmin/Sentinel-NX with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tnadmin/Sentinel-NX:Q6_K

Ollama
How to use tnadmin/Sentinel-NX with Ollama:
```
ollama run hf.co/tnadmin/Sentinel-NX:Q6_K
```

Unsloth Studio

How to use tnadmin/Sentinel-NX with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tnadmin/Sentinel-NX to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tnadmin/Sentinel-NX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tnadmin/Sentinel-NX to start chatting

How to use tnadmin/Sentinel-NX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tnadmin/Sentinel-NX:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tnadmin/Sentinel-NX:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tnadmin/Sentinel-NX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tnadmin/Sentinel-NX:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tnadmin/Sentinel-NX:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use tnadmin/Sentinel-NX with Docker Model Runner:
```
docker model run hf.co/tnadmin/Sentinel-NX:Q6_K
```

Lemonade

How to use tnadmin/Sentinel-NX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tnadmin/Sentinel-NX:Q6_K

Run and chat with the model

lemonade run user.Sentinel-NX-Q6_K

List all available models

lemonade list

Sentinel-NX — Cisco IOS-XE Config Assistant (V3.1, GGUF)

A small, edge-deployable Cisco IOS-XE configuration assistant: a QLoRA fine-tune of Qwen2.5-Coder-3B-Instruct, merged and quantized to GGUF. It emits strict, syntactically valid IOS-XE for exactly what's requested — no invented interfaces, IPs, loopbacks, route-maps, no shutdowns, descriptions, or unrequested best-practice config.

Built with Qwen. Non-commercial only (see License).

Project / code / methodology: https://github.com/tnadmin1/Sentinel-NX

Files

File	Quant	Size	Use
`sentinel-nx-q8_0.gguf`	Q8_0	~3.1 GB	Primary — highest fidelity
`sentinel-nx-q6_k.gguf`	Q6_K	~2.4 GB	Faster, near-lossless

Results

Manually-scored benchmarks; the hidden set uses entirely new interfaces, VLANs, ASNs, IPs, and object names not seen in training (a generalization test).

Hidden 20-prompt benchmark (5 pts each):

Model	Score
Base Qwen2.5-Coder-3B-Instruct	58 / 100
V2	71 / 100
V3.1	97 / 100

Original 25-prompt benchmark (4 pts each): Base 58 → V2 70 → V3 69 → V3.1 93.

Usage

# Ollama (pull directly from this repo)
ollama run hf.co/tnadmin/Sentinel-NX:Q8_0

# llama.cpp
./llama-cli -m sentinel-nx-q8_0.gguf --temp 0 -c 4096 -cnv \
  -sys "You are a Cisco IOS-XE configuration assistant. Output only strict, valid configuration for exactly what is requested. Do not invent values."

Strict behavior is prompt-conditioned. The model suppresses over-completion when the system prompt and request instruct it to (e.g. "Do not add descriptions, no shutdown, spanning-tree, or anything not explicitly requested"). Use a strict prompt for best results.

Known limitations

OSPF router-id is occasionally emitted as ip ospf <process> router-id <id> under an interface instead of router-id under router ospf <process>. Targeted corrective data is the next iteration.

Training

QLoRA (LoRA rank 16) on Qwen2.5-Coder-3B-Instruct, RTX 4070 12 GB. ~5,200 curated + failure-driven remedial IOS-XE instruction pairs, built through three corrective rounds (V2 → V3 → V3.1). See the GitHub repo for the full methodology.

License & attribution

This model is a derivative of Qwen2.5-Coder-3B-Instruct and is distributed under the Qwen Research License — non-commercial use only. Built with Qwen. Copyright (c) Alibaba Cloud. All Rights Reserved.