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 / NOTICE

skyuu72

no-system-prompt safety verified: 46/46 refused, 0 leaks. + Meta attribution string

a3adc82 verified 14 days ago

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	Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta
	Platforms, Inc. All Rights Reserved.

	Built with Llama.

	Llama-Quantara-Sentinel-8B is a derivative of Meta Llama 3.1, reached via
	Cisco's Foundation-Sec-8B-Instruct:

	meta-llama/Llama-3.1-8B
	-> fdtn-ai/Foundation-Sec-8B-Instruct (Cisco Foundation AI)
	-> skyuu72/Llama-Quantara-Sentinel-8B (this model)

	Use of this model is governed by the Llama 3.1 Community License, a copy of
	which is available at:

	https://www.llama.com/llama3_1/license/

	Meta's terms flow down to every derivative in that chain. The apache-2.0 tag
	that appears on some Foundation-Sec listings does not override it. Two clauses
	that actually change what you can do:

	- a distributed derivative's name must begin with "Llama" (hence this one's)
	- "Built with Llama" has to be displayed prominently (top of the model card)

	Commercial use is otherwise unrestricted below 700M monthly active users.

	Copyright (c) 2026 sky (@Metrix187)

	The training code, data generators and evaluation harness live in a separate
	source repository and are NOT covered by this notice - they carry their own
	licence. This file covers the weights.