Instructions to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Use Docker

docker model run hf.co/Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-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": "Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Ollama
How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
```

Unsloth Studio

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF to start chatting

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

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": "Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

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 Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS
```

Lemonade

How to use Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF-IQ4_XS

List all available models

lemonade list

Laguna S 2.1 - GGUF Quants (IQ4_XS & Q4_K_M)

This repository contains GGUF quantizations for poolside/Laguna-S-2.1, including both IQ4_XS and Q4_K_M variants.

Original Model: poolside/Laguna-S-2.1
Quantization Formats: GGUF (IQ4_XS, Q4_K_M)
Model Architecture: 118B MoE (~8B activated parameters per token)

Quantization Details

File Name	Quant Method	Description
`laguna-s-2.1-IQ4_XS.gguf`	`IQ4_XS`	4-bit importance matrix quantization (extra small). Highly optimized for low memory usage with minimal quality loss.
`laguna-s-2.1-Q4_K_M.gguf`	`Q4_K_M`	Standard 4-bit K-quantization (medium). Balanced performance, speed, and accuracy.

Usage Guide

1. Running with `llama.cpp`

Use poolside's llama.cpp fork on the laguna branch for native support:

git clone --branch laguna [https://github.com/poolsideai/llama.cpp](https://github.com/poolsideai/llama.cpp)
cd llama.cpp && cmake -B build && cmake --build build -j

# Download your chosen model file from this repository
# Option A: IQ4_XS
huggingface-cli download Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF laguna-s-2.1-IQ4_XS.gguf --local-dir .

# Option B: Q4_K_M
huggingface-cli download Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF laguna-s-2.1-Q4_K_M.gguf --local-dir .

# Serve with llama-server
./build/bin/llama-server -m Laguna-S-2.1-IQ4_XS.gguf --jinja --port 8000

Downloads last month: 2,204

GGUF

Model size

84B params

Architecture

laguna

Hardware compatibility

4-bit

Model tree for Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF

Base model

poolside/Laguna-S-2.1

Quantized

(80)

this model

Laguna S 2.1 - GGUF Quants (IQ4_XS & Q4_K_M)

Quantization Details

Usage Guide

1. Running with llama.cpp

Model tree for Abiray/Laguna-S-2.1-IQ4_XS-Q4_K_M-GGUF

1. Running with `llama.cpp`