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

Libraries

How to use unsloth/Laguna-S-2.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/Laguna-S-2.1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/Laguna-S-2.1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/Laguna-S-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use unsloth/Laguna-S-2.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/Laguna-S-2.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Laguna-S-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/Laguna-S-2.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Laguna-S-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

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

How to use unsloth/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 unsloth/Laguna-S-2.1-GGUF:UD-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": "unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/Laguna-S-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use unsloth/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 unsloth/Laguna-S-2.1-GGUF:UD-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 unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/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 unsloth/Laguna-S-2.1-GGUF:UD-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 "unsloth/Laguna-S-2.1-GGUF:UD-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"

The model in quantum UD-Q4_K_M cycles at a distance

#10

by Salormatik999 - opened Jul 23

Discussion

Salormatik999

Jul 23

the model went into a loop after 10 minutes of reasoning

SomAnon

Jul 23

•

edited Jul 23

Fixed in newest release. Unsloth doesn't yet include the fix.
Wait for re-upload or use official

Salormatik999

about 1 month ago

They didn't fix anything, the problem remains.

H-J-D

about 1 month ago

Just wanted to add here, my setup was missing config, and I saw this post, https://www.reddit.com/r/LocalLLaMA/comments/1v3wyre/comment/oz9ypep/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

they suggested --rope-scaling yarn --rope-scale 32 --yarn-orig-ctx 8192

After adding that it thinks perfectly.

tetsuo420

30 days ago

Can confirm the solution above works, thanks @H-J-D .

It works for me using llama.cpp (version: 10106 (1425386fd)), and the following entry in models.ini:

[Laguna-S-2.1-MXFP4-coding-256k]
model = /XXXXX/Laguna-S-2.1-MXFP4_MOE-00001-of-00003.gguf
flash-attn = true
jinja = true
ctx-size = 256000

# YaRN RoPE Scaling fixes for thinking loops
rope-scaling = yarn
yarn-orig-ctx = 8192
rope-scale = 32

# Reasoning & sampling
temp = 0.6
top-p = 0.95
top-k = 20

cache-type-k = bf16
cache-type-v = bf16

I am not sure if the settings shown above are optimal, but they work well for me.

CHTL85

28 days ago

maybe use repeat penalty

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