Instructions to use Outlier-Ai/Outlier-Lite-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Outlier-Ai/Outlier-Lite-7B-GGUF",
	filename="Outlier-Lite-7B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Outlier-Ai/Outlier-Lite-7B-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 Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Outlier-Ai/Outlier-Lite-7B-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 Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Outlier-Ai/Outlier-Lite-7B-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": "Outlier-Ai/Outlier-Lite-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

Ollama
How to use Outlier-Ai/Outlier-Lite-7B-GGUF with Ollama:
```
ollama run hf.co/Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Outlier-Ai/Outlier-Lite-7B-GGUF 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 Outlier-Ai/Outlier-Lite-7B-GGUF 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 Outlier-Ai/Outlier-Lite-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Outlier-Ai/Outlier-Lite-7B-GGUF to start chatting

Pi new

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

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": "Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

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 Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Outlier-Ai/Outlier-Lite-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M
```

Lemonade

How to use Outlier-Ai/Outlier-Lite-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Outlier-Ai/Outlier-Lite-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Outlier-Lite-7B-GGUF-Q4_K_M

List all available models

lemonade list

Superseded. This repo is a research artifact from an earlier Outlier lineage and is no longer the recommended download. The current shipping tier is at outlier.host.

Outlier-Lite-7B (GGUF)

This repo predates the v1.8 Outlier lineup. It is preserved here for reproducibility and historical reference, not as a production recommendation.

What replaced it

Current shipping tiers (see Outlier app v1.8+):

For the latest verified benchmarks and downloads, visit outlier.host.

Original notes

This was a research / preview artifact. It may contain experimental adapters, overlays, or quantization variants that did not graduate into the shipping product. Treat any technical claims in earlier revisions of this card as provisional.