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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mudler/Laguna-XS-2.1-APEX-GGUF",
	filename="Laguna-XS-2.1-APEX-Balanced.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mudler/Laguna-XS-2.1-APEX-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 mudler/Laguna-XS-2.1-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf mudler/Laguna-XS-2.1-APEX-GGUF

Install from WinGet (Windows)

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

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

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

Use Docker

docker model run hf.co/mudler/Laguna-XS-2.1-APEX-GGUF

LM Studio
Jan
Ollama
How to use mudler/Laguna-XS-2.1-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Laguna-XS-2.1-APEX-GGUF
```

Unsloth Studio

How to use mudler/Laguna-XS-2.1-APEX-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 mudler/Laguna-XS-2.1-APEX-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 mudler/Laguna-XS-2.1-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use mudler/Laguna-XS-2.1-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Laguna-XS-2.1-APEX-GGUF

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": "mudler/Laguna-XS-2.1-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Laguna-XS-2.1-APEX-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 mudler/Laguna-XS-2.1-APEX-GGUF

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 mudler/Laguna-XS-2.1-APEX-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mudler/Laguna-XS-2.1-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Laguna-XS-2.1-APEX-GGUF

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 "mudler/Laguna-XS-2.1-APEX-GGUF" \
  --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 mudler/Laguna-XS-2.1-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Laguna-XS-2.1-APEX-GGUF
```

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Laguna-XS-2.1-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

⚡ Each donation = another big MoE quantized

I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.

🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors

Laguna-XS-2.1 — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of poolside/Laguna-XS-2.1 — poolside's Laguna XS.2 Mixture-of-Experts model for coding and agentic software engineering.

Brought to you by the LocalAI team | APEX Project | Technical Report

Requires a recent llama.cpp with Laguna support (PR #25165). Older builds cannot load arch=laguna.

Available Files

File	Profile	Best For
Laguna-XS-2.1-APEX-I-Balanced.gguf	I-Balanced	Best overall — imatrix-enhanced
Laguna-XS-2.1-APEX-I-Quality.gguf	I-Quality	Highest quality with imatrix
Laguna-XS-2.1-APEX-Quality.gguf	Quality	Highest quality (no imatrix)
Laguna-XS-2.1-APEX-Balanced.gguf	Balanced	General purpose
Laguna-XS-2.1-APEX-I-Compact.gguf	I-Compact	Consumer GPUs, imatrix-enhanced
Laguna-XS-2.1-APEX-Compact.gguf	Compact	Consumer GPUs
Laguna-XS-2.1-APEX-I-Mini.gguf	I-Mini	Smallest viable, fastest inference

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention, dense FFN) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers compress more aggressively. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

In MoE models the routed-expert FFN tensors dominate the weight budget but only ~8/256 experts fire per token, so APEX compresses middle-layer routed experts hardest while preserving edge layers, attention, and the always-active shared expert.

APEX layout for Laguna

Laguna XS.2 has a structure APEX handles explicitly:

Layer 0 is a leading dense FFN (no experts) — pinned to Q8_0, since every token traverses it.
Layers 1–39 are MoE — 256 routed experts + a shared expert, 8 active per token, sigmoid gating.
Shared expert (ffn_*_shexp) kept at Q8_0 on every tier (always active).
Routed experts follow the 5+5 symmetric edge gradient (higher precision at the first/last layers, most aggressive in the middle).
Router (ffn_gate_inp), norms and the exp_probs_b gating bias stay at full precision.

Architecture

Model: Laguna-XS-2.1 (LagunaForCausalLM, arch laguna)
Layers: 40 (1 dense + 39 MoE) · Experts: 256 routed + 1 shared (8 active)
Attention: 48 heads / 8 KV, per-layer output gate, hybrid full + sliding-window, YaRN rope
Vocab: 100352 · text-only
Calibration: v1.3 diverse dataset

Run with LocalAI

local-ai run mudler/Laguna-XS-2.1-APEX-GGUF@Laguna-XS-2.1-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Built on llama.cpp. Base model by poolside.

Downloads last month: 307

GGUF

Model size

33B params

Architecture

laguna

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mudler/Laguna-XS-2.1-APEX-GGUF

Base model

poolside/Laguna-XS-2.1

Quantized

(27)

this model

Collection including mudler/Laguna-XS-2.1-APEX-GGUF

APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 44 items • Updated about 7 hours ago • 128