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

Libraries

How to use poolside/Laguna-XS-2.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="poolside/Laguna-XS-2.1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("poolside/Laguna-XS-2.1-GGUF", dtype="auto")

llama-cpp-python

How to use poolside/Laguna-XS-2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="poolside/Laguna-XS-2.1-GGUF",
	filename="Laguna-XS-2.1-BF16.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 poolside/Laguna-XS-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 poolside/Laguna-XS-2.1-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf poolside/Laguna-XS-2.1-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf poolside/Laguna-XS-2.1-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf poolside/Laguna-XS-2.1-GGUF:BF16

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 poolside/Laguna-XS-2.1-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf poolside/Laguna-XS-2.1-GGUF:BF16

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 poolside/Laguna-XS-2.1-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf poolside/Laguna-XS-2.1-GGUF:BF16

Use Docker

docker model run hf.co/poolside/Laguna-XS-2.1-GGUF:BF16

LM Studio
Jan

vLLM

How to use poolside/Laguna-XS-2.1-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/poolside/Laguna-XS-2.1-GGUF:BF16

SGLang

How to use poolside/Laguna-XS-2.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "poolside/Laguna-XS-2.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poolside/Laguna-XS-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "poolside/Laguna-XS-2.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "poolside/Laguna-XS-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use poolside/Laguna-XS-2.1-GGUF with Ollama:
```
ollama run hf.co/poolside/Laguna-XS-2.1-GGUF:BF16
```

Unsloth Studio

How to use poolside/Laguna-XS-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 poolside/Laguna-XS-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 poolside/Laguna-XS-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 poolside/Laguna-XS-2.1-GGUF to start chatting

How to use poolside/Laguna-XS-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 poolside/Laguna-XS-2.1-GGUF:BF16

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": "poolside/Laguna-XS-2.1-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use poolside/Laguna-XS-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 poolside/Laguna-XS-2.1-GGUF:BF16

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 poolside/Laguna-XS-2.1-GGUF:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use poolside/Laguna-XS-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/poolside/Laguna-XS-2.1-GGUF:BF16
```

Lemonade

How to use poolside/Laguna-XS-2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull poolside/Laguna-XS-2.1-GGUF:BF16

Run and chat with the model

lemonade run user.Laguna-XS-2.1-GGUF-BF16

List all available models

lemonade list

Use on OpenRouter · Release blog post · Laguna XS 2.1 collection

Laguna XS 2.1

Laguna XS 2.1 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. This model is an upgraded version of our Laguna XS.2 model with a +5.4% jump on SWE-bench Multilingual as well as stronger performance on terminal-style tasks.

This repository contains official GGUF conversions from our standard format release built for llama.cpp (and compatible with vLLM and SGLang). To use with Ollama, pull directly with ollama pull laguna-xs-2.1.

llama.cpp support is not yet upstreamed. See below.

Highlights

Mixed SWA and global attention layout: Laguna XS 2.1 uses sigmoid gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 40 total layers)
KV cache in FP8: KV cache quantized to FP8, reducing memory per token
Native reasoning support: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request
Local-ready: At 33B total parameters and 3B activated, Laguna XS 2.1 is compact enough to run on a Mac with 36 GB of RAM. Available on Ollama and llama.cpp. High-quality FP8, NVFP4 and INT4 quantized variants available (see the collection)
OpenMDW-1.1 license: Use and modify the model and associated materials freely for commercial and non-commercial purposes (learn more about OpenMDW)

Model overview

Training: pre-training, post-training and reinforcement learning stages
Number of parameters: 33B total with 3B activated per token
Optimizer: Muon
Layers: 40 layers (10 layers with global attention, 30 layers with sliding window attention)
Experts: 256 experts with 1 shared expert
Sliding Window: 512 tokens
Modality: text-to-text
Context window: 262,144 tokens
Reasoning support: interleaved thinking with preserved thinking

Files

File	Quant	Size
`Laguna-XS-2.1-BF16.gguf`	BF16 (full precision)	66.9 GB
`Laguna-XS-2.1-Q4_K_M.gguf`	Q4_K_M	20.3 GB

Q4_K_M is the recommended default for local use. Use BF16 if you want a reference full-precision baseline or intend to produce your own quantizations.

llama.cpp

Laguna XS 2.1 support is not yet in upstream llama.cpp. Until it lands, build llama.cpp from the PR that adds Laguna XS 2.1 support (ggml-org/llama.cpp#25165).

# Build llama.cpp from the PR branch
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
git fetch origin pull/25165/head:laguna && git checkout laguna
cmake -B build && cmake --build build -j

# Download a GGUF
huggingface-cli download poolside/Laguna-XS-2.1-GGUF \
  Laguna-XS-2.1-Q4_K_M.gguf --local-dir ~/models/Laguna-XS-2.1-GGUF

Serve an OpenAI-compatible endpoint with llama-server:

./build/bin/llama-server \
  -m ~/models/Laguna-XS-2.1-GGUF/Laguna-XS-2.1-Q4_K_M.gguf \
  --jinja \
  -ngl 99 \
  -c 32768 \
  --port 8000

--jinja applies the model's built-in chat template (reasoning and tool-calling).
-ngl 99 offloads all layers to the GPU (CUDA). Drop or lower it for CPU-only.
-c sets the context length; the model supports up to 262,144 tokens, but a bounded value (e.g. 32768) keeps KV-cache memory reasonable on local machines.

macOS (Metal) users: the same recipe works on Apple Silicon via the Metal backend. Enable flash attention (-fa on) for lower memory use and better throughput.

License

This model is licensed under the OpenMDW-1.1 License.

Intended and Responsible Use

Laguna XS 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna XS 2.1 is subject to the OpenMDW-1.1 License, and should be used consistently with Poolside's Acceptable Use Policy. We advise against circumventing Laguna XS 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

Please report security vulnerabilities or safety concerns to security@poolside.ai.

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GGUF

Model size

33B params

Architecture

laguna

Hardware compatibility

4-bit

16-bit

Collection including poolside/Laguna-XS-2.1-GGUF

Laguna XS 2.1

Collection

Designed for agentic coding and long-horizon work on a local machine. Licensed under OpenMDW-1.1. • 9 items • Updated about 13 hours ago • 10