Instructions to use ramixpe/Iosxr-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramixpe/Iosxr-expert with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ramixpe/Iosxr-expert",
	filename="gguf/iosxr-expert-hybrid-a-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ramixpe/Iosxr-expert with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ramixpe/Iosxr-expert:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ramixpe/Iosxr-expert:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ramixpe/Iosxr-expert:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ramixpe/Iosxr-expert:Q8_0

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 ramixpe/Iosxr-expert:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ramixpe/Iosxr-expert:Q8_0

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 ramixpe/Iosxr-expert:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramixpe/Iosxr-expert:Q8_0

Use Docker

docker model run hf.co/ramixpe/Iosxr-expert:Q8_0

LM Studio
Jan
Ollama
How to use ramixpe/Iosxr-expert with Ollama:
```
ollama run hf.co/ramixpe/Iosxr-expert:Q8_0
```

Unsloth Studio new

How to use ramixpe/Iosxr-expert 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 ramixpe/Iosxr-expert 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 ramixpe/Iosxr-expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ramixpe/Iosxr-expert to start chatting

Pi new

How to use ramixpe/Iosxr-expert with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ramixpe/Iosxr-expert:Q8_0

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": "Iosxr-expert"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ramixpe/Iosxr-expert with Docker Model Runner:
```
docker model run hf.co/ramixpe/Iosxr-expert:Q8_0
```

Lemonade

How to use ramixpe/Iosxr-expert with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramixpe/Iosxr-expert:Q8_0

Run and chat with the model

lemonade run user.Iosxr-expert-Q8_0

List all available models

lemonade list

IOS-XR Expert - Fine-tuned Qwen3.5-9B

A specialized language model for Cisco IOS-XR service provider networking, fine-tuned from Qwen3.5-9B.

Model Details

Base Model: Qwen/Qwen3.5-9B (8.95B parameters)
Fine-tuning: LoRA r=64, bf16, all linear layers (116M trainable params)
Training: 5 epochs, A100 80GB, 3 hours
Dataset: 1,190 curated IOS-XR QA pairs across 8 task families

Performance (V1)

Metric	Score
Syntax accuracy	92.3%
Semantic correctness	95.6%
Contamination resistance	89.8%
Operational quality	23.8%
Overall	83.0%

Capabilities

IOS-XR configuration generation (BGP, MPLS, SR, EVPN, IS-IS, OSPF, L3VPN, L2VPN)
IOS/IOS-XE to IOS-XR migration
Configuration error detection and correction
Troubleshooting guidance
Route-policy (RPL) authoring
CLI to YANG mapping

Usage with Ollama

ollama create iosxr-expert -f Modelfile
ollama run iosxr-expert "Configure BGP VPNv4 peering on IOS-XR"

GGUF Quantizations

iosxr-qwen3.5-9b-q8_0.gguf (8.9 GB) - Best quality
iosxr-qwen3.5-9b-q4_k_m.gguf (5.3 GB) - Best size/quality ratio

Downloads last month: 123

Safetensors

Model size

9B params

Tensor type

BF16

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ramixpe/Iosxr-expert

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(202)

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