Instructions to use Lucebox/Laguna-XS.2-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lucebox/Laguna-XS.2-DFlash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lucebox/Laguna-XS.2-DFlash-GGUF",
	filename="laguna-dflash.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-DFlash-GGUF
# Run inference directly in the terminal:
llama cli -hf Lucebox/Laguna-XS.2-DFlash-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lucebox/Laguna-XS.2-DFlash-GGUF
# Run inference directly in the terminal:
llama cli -hf Lucebox/Laguna-XS.2-DFlash-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 Lucebox/Laguna-XS.2-DFlash-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Lucebox/Laguna-XS.2-DFlash-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 Lucebox/Laguna-XS.2-DFlash-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lucebox/Laguna-XS.2-DFlash-GGUF

Use Docker

docker model run hf.co/Lucebox/Laguna-XS.2-DFlash-GGUF

LM Studio
Jan
Ollama
How to use Lucebox/Laguna-XS.2-DFlash-GGUF with Ollama:
```
ollama run hf.co/Lucebox/Laguna-XS.2-DFlash-GGUF
```

Unsloth Studio

How to use Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-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 Lucebox/Laguna-XS.2-DFlash-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Lucebox/Laguna-XS.2-DFlash-GGUF with Docker Model Runner:
```
docker model run hf.co/Lucebox/Laguna-XS.2-DFlash-GGUF
```

Lemonade

How to use Lucebox/Laguna-XS.2-DFlash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lucebox/Laguna-XS.2-DFlash-GGUF

Run and chat with the model

lemonade run user.Laguna-XS.2-DFlash-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Laguna-XS.2 DFlash

DFlash speculative-decoding drafter for poolside/Laguna-XS.2. Full continued training from v23-step18000 on 60k clean regenerated rows (10k @16k ctx + 50k Open-PerfectBlend @4k ctx).

Real serving gate

metric	prev.	now	delta
accept_rate	29.5%	41.1%	+39.5% rel
avg_commit	3.36	4.29	+27.7%
mixed tok/s	50.47	65.25	+29.3%
MATH tok/s	45.25	69.32	+53.2%
GSM8K tok/s	50.72	63.84	+25.9%
HumanEval tok/s	56.36	63.04	+11.9%
agent tok/s	49.53	60.95	+23.1%

GGUF includes DSpark Markov/confidence aux heads

Downloads last month: 418

Safetensors

Model size

0.4B params

Tensor type

BF16

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