Instructions to use AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use AtomicChat/LFM3-1.7B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/LFM3-1.7B-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": "AtomicChat/LFM3-1.7B-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Ollama
How to use AtomicChat/LFM3-1.7B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-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": "AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/LFM3-1.7B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use AtomicChat/LFM3-1.7B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM3-1.7B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/LFM3-1.7B-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 AtomicChat/LFM3-1.7B-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 "AtomicChat/LFM3-1.7B-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"

LFM3-1.7B Uncensored — GGUF (by AtomicChat)

Built by the team behind Atomic Chat, the app that runs open-weight models locally and offline.

Safety-ablated GGUF of LiquidAI/LFM3-1.7B. A projected rank-1 LoRA (direction = mean harmful − mean harmless activations, with the harmless component projected out) is baked into the weights at W*=2.0, then AD-quantized.

What changed (measured)

metric	base	uncensored
refusals on harmful prompts	61.7%	0.0%
false refusals (XSTest)	5.2%	0.0%
MMLU (2000 q)	41.0%	41.0% (McNemar p=1.0 — no drop)
needle (long context)	3/3	3/3
ΔKLD vs base (neutral)	—	+0.019

A random edit of the same size dropped MMLU 1.3pp and had a larger ΔKLD (0.028), so the targeted direction is both more effective and less damaging. Thinking-loop and empty-output checks pass at W*=2.0.

Rungs

Rung	GB on disk	bpw
AD-Q6_K	1.6	7.31
AD-Q4_K_M	1.1	5.20

Run (llama.cpp)

Needs a build with LFM3 support (merged in llama.cpp #31252; produced at llama.cpp 19cce69).

llama-cli -hf AtomicChat/LFM3-1.7B-Uncensored-GGUF:AD-Q4_K_M -p "Hello"

Base model & license

Base model: LiquidAI/LFM3-1.7B. License: LFM Open License v1.0 (see LICENSE). This removes safety refusals; use responsibly and in line with the base license.

Quants by AtomicChat.