Text Generation
GGUF
llama.cpp
cisco
ios-xe
network-automation
qwen2.5-coder
non-commercial
conversational
Instructions to use tnadmin/Sentinel-NX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 2,825 Bytes
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license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
- cisco
- ios-xe
- network-automation
- gguf
- qwen2.5-coder
- non-commercial
library_name: llama.cpp
pipeline_tag: text-generation
---
# Sentinel-NX β 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 β no invented interfaces, IPs, loopbacks,
route-maps, `no shutdown`s, 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 β 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 β V2 70 β V3 69 β **V3.1 93**.
## Usage
```bash
# Ollama (pull directly from this repo)
ollama run hf.co/tnadmin/Sentinel-NX:Q8_0
```
```bash
# 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 β V3 β 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 β non-commercial use only**. Built with Qwen.
Copyright (c) Alibaba Cloud. All Rights Reserved. |