Instructions to use antirez/Laguna-S-2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antirez/Laguna-S-2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="antirez/Laguna-S-2.1-GGUF",
	filename="laguna-s-2.1-DFlash-Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use antirez/Laguna-S-2.1-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 antirez/Laguna-S-2.1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf antirez/Laguna-S-2.1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf antirez/Laguna-S-2.1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf antirez/Laguna-S-2.1-GGUF:Q8_0

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 antirez/Laguna-S-2.1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf antirez/Laguna-S-2.1-GGUF:Q8_0

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 antirez/Laguna-S-2.1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf antirez/Laguna-S-2.1-GGUF:Q8_0

Use Docker

docker model run hf.co/antirez/Laguna-S-2.1-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use antirez/Laguna-S-2.1-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/antirez/Laguna-S-2.1-GGUF:Q8_0

Ollama
How to use antirez/Laguna-S-2.1-GGUF with Ollama:
```
ollama run hf.co/antirez/Laguna-S-2.1-GGUF:Q8_0
```

Unsloth Studio

How to use antirez/Laguna-S-2.1-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 antirez/Laguna-S-2.1-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 antirez/Laguna-S-2.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use antirez/Laguna-S-2.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf antirez/Laguna-S-2.1-GGUF:Q8_0

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": "antirez/Laguna-S-2.1-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use antirez/Laguna-S-2.1-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 antirez/Laguna-S-2.1-GGUF:Q8_0

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 antirez/Laguna-S-2.1-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use antirez/Laguna-S-2.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf antirez/Laguna-S-2.1-GGUF:Q8_0

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 "antirez/Laguna-S-2.1-GGUF:Q8_0" \
  --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 antirez/Laguna-S-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/antirez/Laguna-S-2.1-GGUF:Q8_0
```

Lemonade

How to use antirez/Laguna-S-2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull antirez/Laguna-S-2.1-GGUF:Q8_0

Run and chat with the model

lemonade run user.Laguna-S-2.1-GGUF-Q8_0

List all available models

lemonade list

Laguna-S-2.1-GGUF / README.md

antirez

Add mixed Q2_K/Q3_K model card

d5dfefa verified 6 days ago

preview code

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metadata

base_model: poolside/Laguna-S-2.1
library_name: gguf
pipeline_tag: text-generation
tags:
  - gguf
  - quantized
  - laguna
  - mixture-of-experts
  - q2_k
  - q3_k
  - ds4

Laguna S 2.1 GGUF

This repository contains a reduced-memory Laguna S 2.1 quantization for DwarfStar.

Mixed Q2_K/Q3_K variant

laguna-s-2.1-RoutedQ2_K-Last27Q3_K.gguf keeps every non-routed tensor byte-identical to Poolside's laguna-s-2.1-Q4_K_M.gguf at revision 706fa69799926b6afde1af9e24ca2a4923f110a1. Only routed expert tensors were requantized using the source importance matrix:

routed layers 1 through 20: Q2_K gate, up, and down
routed layers 21 through 47: Q3_K gate, up, and down
all other tensors: unchanged from the official Q4_K_M GGUF

The file is 48,260,803,968 bytes (44.946 GiB), intended for full-residency inference on 64 GiB systems. Runtime memory also depends on context size and KV-cache allocation.

SHA-256:

61fc66596597985cb9408a8530de6322d9e0d5b1d2ad4ed6503938018e0ce903

DwarfStar

Q3_K routed Laguna inference is supported starting with DwarfStar commit 938227a2.

./download_model.sh laguna-q2-q3
./ds4 -m gguf/laguna-s-2.1-RoutedQ2_K-Last27Q3_K.gguf -p "Hello"

On an Apple M5 Max, the tested model reached approximately 514 tokens/second for a 4096-token prefill and 63 tokens/second steady-state generation.

Against 100 official continuation vectors, the mixed model obtained average NLL 0.2583, 87/100 first-token matches, and average matching-prefix length 9.50 tokens. The corresponding full Q4_K_M measurements were 0.2352, 92/100, and 10.86.