Instructions to use Beinsezii/laguna-s-2.1-GGUF-HALO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Beinsezii/laguna-s-2.1-GGUF-HALO 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 Beinsezii/laguna-s-2.1-GGUF-HALO
# Run inference directly in the terminal:
llama cli -hf Beinsezii/laguna-s-2.1-GGUF-HALO

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Beinsezii/laguna-s-2.1-GGUF-HALO
# Run inference directly in the terminal:
llama cli -hf Beinsezii/laguna-s-2.1-GGUF-HALO

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 Beinsezii/laguna-s-2.1-GGUF-HALO
# Run inference directly in the terminal:
./llama-cli -hf Beinsezii/laguna-s-2.1-GGUF-HALO

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 Beinsezii/laguna-s-2.1-GGUF-HALO
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Beinsezii/laguna-s-2.1-GGUF-HALO

Use Docker

docker model run hf.co/Beinsezii/laguna-s-2.1-GGUF-HALO

LM Studio
Jan
Ollama
How to use Beinsezii/laguna-s-2.1-GGUF-HALO with Ollama:
```
ollama run hf.co/Beinsezii/laguna-s-2.1-GGUF-HALO
```
Unsloth Desktop

How to use Beinsezii/laguna-s-2.1-GGUF-HALO with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Beinsezii/laguna-s-2.1-GGUF-HALO

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Beinsezii/laguna-s-2.1-GGUF-HALO"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Beinsezii/laguna-s-2.1-GGUF-HALO with Docker Model Runner:
```
docker model run hf.co/Beinsezii/laguna-s-2.1-GGUF-HALO
```

Lemonade

How to use Beinsezii/laguna-s-2.1-GGUF-HALO with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Beinsezii/laguna-s-2.1-GGUF-HALO

Run and chat with the model

lemonade run user.laguna-s-2.1-GGUF-HALO-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Beinsezii/laguna-s-2.1-GGUF-HALO with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Beinsezii/laguna-s-2.1-GGUF-HALO

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 Beinsezii/laguna-s-2.1-GGUF-HALO

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Beinsezii/laguna-s-2.1-GGUF-HALO with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Beinsezii/laguna-s-2.1-GGUF-HALO

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 "Beinsezii/laguna-s-2.1-GGUF-HALO" \
  --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"

Update: I manually revised the chat template to change "<think>" -> "<think>\n" to fix inconsistent reasoning initialization. New templated included as jinja and embedded in gguf.

Quant optimized for quality / speed on a Strix Halo 128GiB system. Possibly also beneficial on DGX Spark and similar systems.

Left more headroom on this quant to better utilize long ctx and eventual DFlas once supported.

Refer to the tensor types for a breakdown of quantization method.

See the GLM version for more details on theory and comparisons.

Downloads last month: 65

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for Beinsezii/laguna-s-2.1-GGUF-HALO

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