Instructions to use irfanalee/incident-responder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use irfanalee/incident-responder-gguf 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 irfanalee/incident-responder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf irfanalee/incident-responder-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf irfanalee/incident-responder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf irfanalee/incident-responder-gguf:Q4_K_M

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 irfanalee/incident-responder-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf irfanalee/incident-responder-gguf:Q4_K_M

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 irfanalee/incident-responder-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf irfanalee/incident-responder-gguf:Q4_K_M

Use Docker

docker model run hf.co/irfanalee/incident-responder-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use irfanalee/incident-responder-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/irfanalee/incident-responder-gguf:Q4_K_M

Ollama
How to use irfanalee/incident-responder-gguf with Ollama:
```
ollama run hf.co/irfanalee/incident-responder-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use irfanalee/incident-responder-gguf with Docker Model Runner:
```
docker model run hf.co/irfanalee/incident-responder-gguf:Q4_K_M
```

Lemonade

How to use irfanalee/incident-responder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull irfanalee/incident-responder-gguf:Q4_K_M

Run and chat with the model

lemonade run user.incident-responder-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

DevOps Incident Responder (GGUF)

A fine-tuned Mistral-NeMo-Minitron-8B-Instruct model for DevOps incident diagnosis and resolution.

Model Description

Analyzes error logs, stack traces, and incident descriptions to provide:

Root cause analysis
Severity assessment
Step-by-step fixes with exact commands
Prevention guidance

Tech coverage: Kubernetes, Docker, Terraform, Azure, GCP, Node.js, Redis, MongoDB, Nginx, PostgreSQL, InfluxDB

Training Details

Base Model: nvidia/Mistral-NeMo-Minitron-8B-Instruct
Method: QLoRA (4-bit quantization + LoRA adapters)
Dataset: 4,755 examples (scraped + synthetic)
Epochs: 2
LoRA Rank: 32, Alpha: 64
Quantization: Q4_K_M (4.8 GB)

Usage with Ollama

Download the GGUF file
Create a Modelfile:

Downloads last month: 29

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for irfanalee/incident-responder-gguf

Base model

nvidia/Mistral-NeMo-Minitron-8B-Base

Finetuned

nvidia/Mistral-NeMo-Minitron-8B-Instruct

Quantized

(13)

this model