Instructions to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Null-Guard/LFM2.5-230M-Uncensored-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": "Null-Guard/LFM2.5-230M-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M

Ollama
How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with Ollama:
```
ollama run hf.co/Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_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": "Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-230M-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-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 Null-Guard/LFM2.5-230M-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Null-Guard/LFM2.5-230M-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Null-Guard/LFM2.5-230M-Uncensored-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 "Null-Guard/LFM2.5-230M-Uncensored-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"

LFM 2.5 230M Uncensored (GGUF)

This repository contains the GGUF conversions of the uncensored version of LiquidAI/LFM2.5-230M.

The original model weights were processed to remove alignment restrictions and then converted to .gguf format for efficient inference on CPU and Apple Silicon using llama.cpp and compatible frontends (e.g., LM Studio, Ollama, GPT4All).

Available Quants

This repository includes a comprehensive set of quantizations from extreme 1-bit compression up to full 16-bit precision.

Quantization	Bits	Recommended Use Case
IQ1_S / IQ1_M	~1.5 - 2	Maximum memory savings. High perplexity loss expected on this small parameter size.
IQ2_XXS - IQ2_M	~2	Extreme compression. Usable, but degradation is noticeable.
IQ3_XXS - Q3_K_L	~3	High compression. Good for strictly limited memory environments.
IQ4_XS - Q4_K_M	~4	Recommended sweet spot. Great balance of memory usage and quality.
Q5_0 - Q5_K_M	~5	Near-lossless performance with modest memory savings.
Q6_K	~6	Effectively lossless.
Q8_0	8	Fully lossless integer quantization.
F16	16	Unquantized baseline. Highest quality, highest memory footprint.

Usage with `llama.cpp`

You can run this model via the llama.cpp CLI. Replace <quant_type> with your desired precision (e.g., Q4_K_M).

./llama-cli -m model-<quant_type>.gguf -p "Your prompt here" -n 256

Downloads last month: 336

GGUF

Model size

0.2B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Null-Guard/LFM2.5-230M-Uncensored-GGUF

Base model

LiquidAI/LFM2.5-230M-Base

Finetuned

LiquidAI/LFM2.5-230M

Quantized

(29)

this model

LFM 2.5 230M Uncensored (GGUF)

Available Quants

Usage with llama.cpp

Model tree for Null-Guard/LFM2.5-230M-Uncensored-GGUF

Usage with `llama.cpp`