Instructions to use dev7a/Laguna-S-2.1-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0

Use Docker

docker model run hf.co/dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use dev7a/Laguna-S-2.1-DFlash-GGUF with Ollama:
```
ollama run hf.co/dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
```

Unsloth Studio

How to use dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dev7a/Laguna-S-2.1-DFlash-GGUF to start chatting

How to use dev7a/Laguna-S-2.1-DFlash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dev7a/Laguna-S-2.1-DFlash-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": "dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use dev7a/Laguna-S-2.1-DFlash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dev7a/Laguna-S-2.1-DFlash-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 "dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0
```

Lemonade

How to use dev7a/Laguna-S-2.1-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-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 dev7a/Laguna-S-2.1-DFlash-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

Laguna S 2.1 DFlash GGUF

This repository contains a Q8_0 GGUF conversion of Poolside's DFlash drafter for Laguna S 2.1. It is an auxiliary draft model. It is not a standalone language model and does not include the Laguna S 2.1 target weights.

Artifact

File	Bytes	SHA-256
`Laguna-S-2.1-DFlash-Q8_0.gguf`	1,188,535,328	`94d5d6d93acb2dfb1209599bc099ebc5b30e959a3a1200dcaebfb01e3f480e43`

The GGUF has six DFlash layers and target taps [2, 11, 20, 30, 39, 48]. Its 49 matrix tensors use Q8_0. Its 27 normalization and bias tensors remain F32.

Download

hf download dev7a/Laguna-S-2.1-DFlash-GGUF Laguna-S-2.1-DFlash-Q8_0.gguf

Use this drafter only with a compatible Laguna S 2.1 target and a runtime that supports the standardized llama.cpp dflash GGUF schema. In NS4, use catalog coordinate dev7a/laguna-s:dflash.

Provenance and reproduction

The source weights are Poolside's SafeTensors at revision b0486d1586daa0d56435c508108171fc1c8daff9. The target tokenizer comes from Laguna S 2.1 revision 00af5a51782109b587a3b3bbf11875e566036fa7. Conversion uses Poolside's llama.cpp revision 06f8cebd7fe728687be3d19f8bdedb70d75883af.

The source manifest records every downloaded file, byte size, and SHA-256. The dependency locks contain hashes and use binary packages only. The released file and a clean repeated conversion were byte-identical.

Reproduce on Linux AArch64 with Python 3.12:

python3.12 scripts/download_sources.py --destination sources
python3.12 scripts/reproduce.py --sources sources --repeat-check

Verify the release without third-party Python packages:

python3.12 scripts/verify.py Laguna-S-2.1-DFlash-Q8_0.gguf

This is a community conversion, not an official Poolside release. The weights remain under OpenMDW-1.1. The unmodified upstream license is in LICENSE. The conversion-code license and third-party notices are in LICENSE.code and THIRD_PARTY_NOTICES.md.

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GGUF

Model size

1B params

Architecture

dflash

Hardware compatibility

8-bit

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