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

Libraries

How to use Lucebox/Laguna-XS.2-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-GGUF",
	filename="laguna-xs2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

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

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M

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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M

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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lucebox/Laguna-XS.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Lucebox/Laguna-XS.2-GGUF with Ollama:
```
ollama run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M
```

Unsloth Studio new

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

Docker Model Runner
How to use Lucebox/Laguna-XS.2-GGUF with Docker Model Runner:
```
docker model run hf.co/Lucebox/Laguna-XS.2-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lucebox/Laguna-XS.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Laguna-XS.2-GGUF-Q4_K_M

List all available models

lemonade list

Laguna-XS.2 GGUF (BF16 + Q4_K_M)

GGUF conversions of poolside/Laguna-XS.2, a 33B-A3B (3B active) MoE coding model from Poolside under Apache 2.0. Built for use with lucebox-hub (dflash + PFlash) on consumer GPUs.

Files

File	Quant	Size	BPW	Notes
`laguna-xs2-bf16.gguf`	BF16	66.9 GB	16.01	reference, identical math to HF transformers fp/bf16
`laguna-xs2-Q4_K_M.gguf`	Q4_K_M	20.3 GB	4.85	imatrix-calibrated, fits a single 24 GB GPU
`laguna-xs2.imatrix`	imatrix	188 MB	—	Bartowski calibration_datav3 (134 chunks, 68608 tokens)

Architecture

40 layers, n_embd 2048, n_head_kv 8, head_dim 128
Per-layer head count [48, 64, 64, 64] × 10 (4-layer SWA pattern: full, sw, sw, sw)
256 experts, top-8 routing, 1 always-on shared expert
Sigmoid router, expert weights scale 2.5
Sliding window 512, partial RoPE with YaRN (orig ctx 4096, factor 32)
Vocab 100,352, BOS=2, EOS=2, PAD=9

Quality

Metric	BF16	Q4_K_M	Δ
Perplexity (Bartowski v3, 20×512)	10.7594 ± 0.522	11.2854 ± 0.553	+4.9%

Imatrix calibration uses Bartowski calibration_datav3.txt (multilingual + code mix), the same corpus Unsloth-distributed quants use.

Verified vs the official Poolside HF reference (BF16, eager attention, greedy decoding): logits match exactly for the first 30+ tokens on a B-tree explanation prompt; subsequent divergence is fp precision drift, not a graph bug.

Performance (RTX 3090 24 GB, Q4_K_M)

Measured with bench_laguna_generate from lucebox-hub (dflash autoregressive forward, no spec-decode draft yet):

Workload	Throughput	Notes
Decode @ ctx=128 (greedy)	113 tok/s	n_gen=128
Decode @ ctx=1K	104 tok/s
Decode @ ctx=4K	65 tok/s
128K TTFT via dflash + PFlash	15.91 s	5.4× faster than llama.cpp pp131072 (86.60 s)
Loader VRAM	18.77 GiB	+ 110 MiB tok_embd kept on CPU

Usage

lucebox-hub (dflash + PFlash, recommended for 128K)

# clone
git clone https://github.com/Luce-Org/lucebox-hub
cd lucebox-hub/dflash

# build with sm_86 (3090 / A6000)
cmake -B build -DCMAKE_CUDA_ARCHITECTURES=86
cmake --build build -j

# fetch the Q4_K_M GGUF + Poolside tokenizer
hf download Lucebox/Laguna-XS.2-GGUF laguna-xs2-Q4_K_M.gguf --local-dir models/
hf download poolside/Laguna-XS.2 chat_template.jinja tokenizer.json tokenizer_config.json \
   special_tokens_map.json config.json --local-dir models/Laguna-XS-2

# run the OpenAI server (same server.py as qwen35, arch auto-detected from GGUF).
# -ctk/-ctv q4_0 keeps the 131K KV cache under ~6 GB so weights + KV fit on 24 GB.
python3 scripts/server.py \
  --target models/laguna-xs2-Q4_K_M.gguf \
  --tokenizer models/Laguna-XS-2 \
  --port 8000 --max-ctx 131072 \
  -ctk q4_0 -ctv q4_0

# chat
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"luce-dflash","messages":[{"role":"user","content":"hello"}],"stream":true}'

License

Apache 2.0, inherited from upstream poolside/Laguna-XS.2.

Model tree for Lucebox/Laguna-XS.2-GGUF

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