Instructions to use vcruz305/Laguna-S-2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vcruz305/Laguna-S-2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vcruz305/Laguna-S-2.1-GGUF",
	filename="Laguna-S-2.1-IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use vcruz305/Laguna-S-2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vcruz305/Laguna-S-2.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vcruz305/Laguna-S-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

Ollama
How to use vcruz305/Laguna-S-2.1-GGUF with Ollama:
```
ollama run hf.co/vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vcruz305/Laguna-S-2.1-GGUF to start chatting

How to use vcruz305/Laguna-S-2.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

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": "vcruz305/Laguna-S-2.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vcruz305/Laguna-S-2.1-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 vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

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 vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use vcruz305/Laguna-S-2.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

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 "vcruz305/Laguna-S-2.1-GGUF:Q4_K_M" \
  --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 vcruz305/Laguna-S-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/vcruz305/Laguna-S-2.1-GGUF:Q4_K_M
```

Lemonade

How to use vcruz305/Laguna-S-2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vcruz305/Laguna-S-2.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Laguna-S-2.1-GGUF-Q4_K_M

List all available models

lemonade list

Laguna-S-2.1 — GGUF quant suite (1–8 bit, imatrix)

Community GGUF quantizations of poolside/Laguna-S-2.1, covering the low-bit band (IQ1–IQ3) that the official release does not ship — so you can run this 117B-parameter MoE on a single GPU.

Every file is quantized with poolside's official importance matrix (laguna-s-2.1.imatrix, 400 chunks) from their official F16 GGUF, using the laguna architecture in llama.cpp. Each file is load-and-generate smoke tested on GPU before upload (see Verification below).

Uploading now, smallest first. Files appear as they finish. This card lists the full planned suite; the Status column shows what is live.

Model at a glance


Architecture	`laguna` (poolside) — Mixture-of-Experts
Total parameters	~117B (`256 × 4.5B` label)
Experts	256 routed, top-10 active per token
Layers	48
Hidden size	3072
Attention	hybrid full / sliding-window (512), GQA (48 Q / 8 KV heads, head_dim 128)
Context	262,144 native · up to 1,048,576 via YaRN
Base precision	bfloat16

Because only ~10 of 256 experts fire per token, decode is far faster than the 117B total suggests — this is what makes low-bit single-GPU serving practical.

Available quants

Recommended picks in bold. Sizes are file sizes; add KV cache + compute buffers for total VRAM (see Hardware).

Quant	Size	Status	Notes
IQ1_S	23.8 GB	✅ live	bonus: absolute smallest; single 24 GB GPU
IQ1_M	26.4 GB	✅ live	recommended smallest; better quality than IQ1_S
IQ2_M	~40 GB	⏳ uploading	best quality/size in the 2-bit band
Q2_K	~40 GB	⏳
IQ3_XXS	~45 GB	⏳	split
Q3_K_M	~56 GB	⏳	split; strong mid-range
IQ4_XS	~63 GB	⏳	split; near-original quality
Q4_K_M	~67 GB	⏳	split
Q5_K_M	~83 GB	⏳	split
Q8_0	~128 GB	⏳	split; near-lossless

For F16, use poolside's official Laguna-S-2.1-GGUF.

Split files: quants above ~48 GB are split with llama-gguf-split. Download all parts (*-00001-of-000NN.gguf …) into one folder and point llama.cpp at part 00001; it auto-merges. No manual concatenation.

Hardware / VRAM

Total VRAM ≈ file size + KV cache + ~0.5–1 GB compute buffers. KV cache for this model is 192 KiB/token in fp16 (0.75 GB at 4K, ~1.5 GB at 8K). Use -fa on and an fp8/q8 KV cache to cut that roughly in half.

Your GPU(s)	Recommended quant	Context
single 24 GB (3090/4090/RTX 6000)	IQ1_S	~4–8K on-GPU; longer via `--n-cpu-moe` offload
single 32 GB	IQ1_M / IQ2_M	comfortable mid context
48 GB (or 2×24)	IQ2_M / IQ3_XXS	long context
64 GB	Q3_K_M / IQ4_XS	long context, near-orig quality
80 GB+	IQ4_XS and up	full fidelity

Not enough VRAM? Because it's a sparse MoE, CPU-offloading the expert tensors is cheap. Add --n-cpu-moe 40 (offload experts of 40 layers to RAM) or classic -ngl partial offload; you keep most of the speed with a fraction of the VRAM.

How to run

⚠️ Requires poolside's llama.cpp fork (branch laguna) until upstream support lands — see ggml-org/llama.cpp#25165. Stock/mainline llama.cpp will error with unknown model architecture: 'laguna'.

1. Build the fork

git clone --branch laguna https://github.com/poolsideai/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release   # drop -DGGML_CUDA=ON for CPU-only
cmake --build build -j

2. Download a quant

pip install -U "huggingface_hub[cli]"
hf download vcruz305/Laguna-S-2.1-GGUF Laguna-S-2.1-IQ1_S.gguf --local-dir ./laguna
# split quant? grab every part:
# hf download vcruz305/Laguna-S-2.1-GGUF --include "Laguna-S-2.1-IQ4_XS-*.gguf" --local-dir ./laguna

3a. Chat (CLI)

./build/bin/llama-cli \
  -m ./laguna/Laguna-S-2.1-IQ1_S.gguf \
  -ngl 999 -fa on -c 8192 --jinja \
  -p "The capital of France is"

3b. OpenAI-compatible server

./build/bin/llama-server \
  -m ./laguna/Laguna-S-2.1-IQ1_S.gguf \
  -ngl 999 -fa on -c 8192 --jinja --port 8000
# then: curl http://localhost:8000/v1/chat/completions ...

Low-VRAM (offload experts to RAM)

./build/bin/llama-cli -m ./laguna/Laguna-S-2.1-IQ1_S.gguf \
  -ngl 999 --n-cpu-moe 40 -fa on -c 8192 --jinja -p "Hello"

Contexts beyond 256K

The weights are native 1M checkpoints. To exceed 256K, override rope/YaRN at load time per poolside's base card (e.g. --rope-scaling yarn --yarn-orig-ctx 262144); most users will not need this.

Verification

Each uploaded file was smoke-tested on GPU (offloaded, fresh load) and must emit the expected continuation before upload — e.g. IQ1_S:

Prompt: "The capital of France is"
Output: "The capital of France is **Paris**."

Files that fail the load-and-generate check are not uploaded. Quant sizes and short SHA-256 receipts are recorded in each file's commit message.

Choosing a quant

Want it to fit one 24 GB card? → IQ1_S. It's genuinely coherent for a 1.62-bit 117B model; expect some quality loss vs higher bits.
Best low-bit quality/size? → IQ2_M or Q3_K_M.
Near-original quality? → IQ4_XS and up (needs 64 GB+ or offload).
IQ (i-quants) are generally higher quality per byte than the same-size K-quant, but need the imatrix (already baked in here) and are slightly slower on some CPUs.

Credits & license

Base model, architecture, and importance matrix: poolside — please cite/credit them.
Speculative-decoding draft model for extra speed: Laguna-S-2.1-DFlash.
Quantization: vcruz305, llama.cpp (laguna fork).
Released under OpenMDW-1.1, following the base model's license. See LICENSE.md.

Not affiliated with poolside. Community redistribution of quantized weights.

Downloads last month: 2,806

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

8-bit

Model tree for vcruz305/Laguna-S-2.1-GGUF

Base model

poolside/Laguna-S-2.1

Quantized

(75)

this model