Instructions to use dev7a/Laguna-XS-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-XS-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-XS-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf dev7a/Laguna-XS-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-XS-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf dev7a/Laguna-XS-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-XS-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dev7a/Laguna-XS-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-XS-2.1-DFlash-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0

Use Docker

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

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

Unsloth Studio

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

How to use dev7a/Laguna-XS-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-XS-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-XS-2.1-DFlash-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use dev7a/Laguna-XS-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-XS-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-XS-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-XS-2.1-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

Laguna XS 2.1 DFlash GGUF

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

Artifact

File	Bytes	SHA-256
`Laguna-XS-2.1-DFlash-Q8_0.gguf`	494,735,712	`8b01363d09eac264d93e54df8020b0c73d229f6092bbbdb99ec6774c7a2aa4db`

The GGUF has five DFlash layers and target taps [2, 14, 26, 34, 40]. Its 41 matrix tensors use Q8_0. Its 23 normalization and bias tensors remain F32.

Download

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

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

Provenance and reproduction

The source weights are Poolside's SafeTensors at revision 5c36361aab23c8ed3afbd079c10c426b677bc607. The target tokenizer comes from Laguna XS 2.1 revision e9df9a59996d790b94b70f3fef343fe1d9e34bdf. 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-XS-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

0.5B params

Architecture

dflash

Hardware compatibility

8-bit

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