Instructions to use skyuu72/Llama-Quantara-Sentinel-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use skyuu72/Llama-Quantara-Sentinel-8B 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 skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf skyuu72/Llama-Quantara-Sentinel-8B: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 skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf skyuu72/Llama-Quantara-Sentinel-8B: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 skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M

Use Docker

docker model run hf.co/skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use skyuu72/Llama-Quantara-Sentinel-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skyuu72/Llama-Quantara-Sentinel-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skyuu72/Llama-Quantara-Sentinel-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M

Ollama
How to use skyuu72/Llama-Quantara-Sentinel-8B with Ollama:
```
ollama run hf.co/skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use skyuu72/Llama-Quantara-Sentinel-8B with Docker Model Runner:
```
docker model run hf.co/skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M
```

Lemonade

How to use skyuu72/Llama-Quantara-Sentinel-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull skyuu72/Llama-Quantara-Sentinel-8B:Q4_K_M

Run and chat with the model

lemonade run user.Llama-Quantara-Sentinel-8B-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama-Quantara-Sentinel-8B / Modelfile.Q4_K_M

skyuu72

v1.0 card, AUP, Modelfiles, evaluation evidence

27e800b verified 13 days ago

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909 Bytes

	# Llama-Quantara-Sentinel-8B v1.0 — Q4_K_M (training run v6)
	# Cisco Foundation-Sec native grammar, NOT llama-3.1. Unsloth reports
	# "No Ollama template mapping found" for this base and writes no Modelfile,
	# so this is hand-written on purpose. Get the stop tokens wrong and every
	# local user gets the v1/v2 runaway output back.
	FROM ./Llama-Quantara-Sentinel-8B-v1.0.Q4_K_M.gguf

	TEMPLATE """<\|system\|>
	{{ .System }}
	<\|user\|>
	{{ .Prompt }}
	<\|assistant\|>
	"""

	SYSTEM """You are Quantara Sentinel, a defensive cybersecurity assistant. You help users find, understand, and fix security weaknesses. You explain risks clearly and suggest safe fixes. You refuse to produce working exploits, malware, or instructions to attack systems the user does not own."""

	PARAMETER temperature 0.3
	PARAMETER top_p 0.9
	PARAMETER num_ctx 8192
	PARAMETER stop "<\|end_of_text\|>"
	PARAMETER stop "<\|user\|>"