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: 814

GGUF

Model size

33B params

Architecture

laguna

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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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 13 hours ago • 128