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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tepirale/Laguna-S-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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
# Run inference directly in the terminal:
llama cli -hf tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
# Run inference directly in the terminal:
llama cli -hf tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

Use Docker

docker model run hf.co/tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tepirale/Laguna-S-2.1-DFLASH-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": "tepirale/Laguna-S-2.1-DFLASH-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

SGLang

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

Ollama
How to use tepirale/Laguna-S-2.1-DFLASH-GGUF with Ollama:
```
ollama run hf.co/tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
```
Unsloth Desktop

How to use tepirale/Laguna-S-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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tepirale/Laguna-S-2.1-DFLASH-GGUF with Docker Model Runner:
```
docker model run hf.co/tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

Run and chat with the model

lemonade run user.Laguna-S-2.1-DFLASH-GGUF-UD-IQ1_M

List all available models

lemonade list

Hermes Agent

How to use tepirale/Laguna-S-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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tepirale/Laguna-S-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 tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_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 "tepirale/Laguna-S-2.1-DFLASH-GGUF:UD-IQ1_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"

Base models

Model:
https://huggingface.co/unsloth/Laguna-S-2.1-GGUF

Dflash model:
https://huggingface.co/poolside/Laguna-S-2.1-GGUF

chat_template:
https://huggingface.co/poolside/Laguna-S-2.1-GGUF/raw/main/chat_template.jinja

Files

File manager:
https://huggingface.co/tepirale/Laguna-S-2.1-DFLASH-GGUF/blob/main/laguna_manager.sh

Config models:
https://huggingface.co/tepirale/Laguna-S-2.1-DFLASH-GGUF/blob/main/models.json

Auto model | GPU L40 '38 GiB / 45 GiB' V-Ram


!chmod +x laguna_manager.sh

# install llama.cpp or vllm
./laguna_manager.sh install llamacpp


# download Laguna-S-2.1-GGUF
hf download unsloth/Laguna-S-2.1-GGUF \
  --include "Laguna-S-2.1-UD-IQ1_M.gguf" \
  --local-dir ~/.laguna_manager/models/laguna-s-2.1-GGUF


# download Laguna-S-2.1-DFlash-GGUF
hf download poolside/Laguna-S-2.1-GGUF \
  --include "*DFlash*.gguf" \
  --local-dir ~/.laguna_manager/models/laguna-s-2.1-DFlash


# config
./laguna_manager.sh config set '.model="laguna-s-2.1" | .backend="llamacpp" | .quant="UD-IQ1_M" | .server.llamacpp.ctx_size=32768 | .server.llamacpp.spec_decoding=true'

# return config
'''
Actualizado.
{
  "backend": "llamacpp",
  "model": "laguna-s-2.1",
  "quant": "UD-IQ1_M",
  "vllm_variant": "fp8",
  "server": {
    "port": 8000,
    "host": "0.0.0.0",
    "llamacpp": {
      "ctx_size": 32768,
      "n_gpu_layers": 999,
      "flash_attn": "on",
      "jinja": true,
      "fit": "on",
      "parallel": 1,
      "threads": -1,
      "cache_type_k": "f16",
      "cache_type_v": "f16",
      "spec_decoding": true,
      "spec_type": "draft-dflash",
      "spec_draft_n_max": 15,
      "extra_args": ""
    },
    "vllm": {
      "max_model_len": 32768,
      "gpu_memory_utilization": 0.92,
      "tensor_parallel_size": "auto",
      "enable_thinking": true,
      "enable_auto_tool_choice": true,
      "served_model_name": "laguna",
      "speculative_dflash": false,
      "num_speculative_tokens": 7,
      "extra_args": ""
    }
  }
}
'''


# run
./laguna_manager.sh start

# return start
'''
== Montando servidor ==
  Modelo  : laguna-s-2.1
  Backend : llamacpp
  GPUs    : 1 (cuda, 44 GB)
  Puerto  : 8000
Comando:
  /root/.laguna_manager/llama.cpp/build/bin/llama-server   -m   /root/.laguna_manager/models/laguna-s-2.1-GGUF/Laguna-S-2.1-UD-IQ1_M.gguf   --host   0.0.0.0   --port   8000   --ctx-size   32768   -ngl   999   -fa   on   --parallel   1   --jinja   --fit   on   --cache-type-k   f16   --cache-type-v   f16 

Servidor lanzado (PID 415). Logs: ./laguna_manager.sh logs -f
Esperando a que el servidor esté listo...
✔ Servidor listo en http://0.0.0.0:8000/v1  (OpenAI-compatible)
'''

# restart
./laguna_manager.sh restart

# return restart
'''
Deteniendo servidor (PID 866)...
Servidor detenido.
== Montando servidor ==
  Modelo  : laguna-s-2.1
  Backend : llamacpp
  GPUs    : 1 (cuda, 44 GB)
  Puerto  : 8000
Comando:
  /root/.laguna_manager/llama.cpp/build/bin/llama-server   -m   /root/.laguna_manager/models/laguna-s-2.1-GGUF/Laguna-S-2.1-UD-IQ1_M.gguf   --host   0.0.0.0   --port   8000   --ctx-size   32768   -ngl   999   -fa   on   --parallel   1   --jinja   --fit   on   --cache-type-k   f16   --cache-type-v   f16   -md   /root/.laguna_manager/models/laguna-s-2.1-DFlash/laguna-s-2.1-DFlash-BF16.gguf   --spec-type   draft-dflash   --spec-draft-n-max   15 

Servidor lanzado (PID 1783). Logs: ./laguna_manager.sh logs -f
Esperando a que el servidor esté listo...
✔ Servidor listo en http://0.0.0.0:8000/v1  (OpenAI-compatible)
'''



'''
./laguna_manager.sh gpu        # detecta GPUs (nvidia-smi/rocm-smi), VRAM total, RAM, disco
./laguna_manager.sh install    # auto-elige backend e instala lo que falte
./laguna_manager.sh download   # menú interactivo: modelo → backend → quant
./laguna_manager.sh start      # monta el servidor con tus hiperparámetros
./laguna_manager.sh stop       # lo detiene (con limpieza de workers de vLLM)
./laguna_manager.sh status     # PID, URL, modelos servidos, uso de VRAM
./laguna_manager.sh logs -f    # sigue los logs en vivo
'''

Downloads last month: 126

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

1-bit

2-bit

16-bit

Model tree for tepirale/Laguna-S-2.1-DFLASH-GGUF

Base model

poolside/Laguna-XS-2.1

Quantized

(39)

this model