Instructions to use pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use pottokao/LFM2.5-24B-A2B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pottokao/LFM2.5-24B-A2B-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": "pottokao/LFM2.5-24B-A2B-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M

Ollama
How to use pottokao/LFM2.5-24B-A2B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-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": "pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pottokao/LFM2.5-24B-A2B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use pottokao/LFM2.5-24B-A2B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-24B-A2B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pottokao/LFM2.5-24B-A2B-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 pottokao/LFM2.5-24B-A2B-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 "pottokao/LFM2.5-24B-A2B-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"

LFM2.5-24B-A2B-Uncensored-GGUF

GGUF quantizations of pottokao/LFM2.5-24B-A2B-Uncensored (LiquidAI LFM2.5-24B-A2B decensored with Heretic ARA, searched and tested with thinking enabled; see the main repo for results).

File	Size	Notes
`LFM2.5-24B-A2B-Uncensored-Q4_K_M.gguf`	14.5 GB	no imatrix
`LFM2.5-24B-A2B-Uncensored-IQ4_XS.gguf`	12.9 GB	imatrix · full GPU offload on a 16 GB card
`LFM2.5-24B-A2B-Uncensored-Q3_K_M.gguf`	11.5 GB	imatrix · K-quant, better when experts run on CPU (`--n-cpu-moe`)
`LFM2.5-24B-A2B-Uncensored-IQ3_M.gguf`	10.5 GB	imatrix · smallest, plenty of room for long context on 16 GB
`lfm25-uncensored.imatrix`	—	importance matrix used above

IQ quants are fastest fully on GPU; if you offload experts to CPU, prefer the K-quants.

All files keep every attn_output tensor at Q8_0 (--tensor-type attn_output=q8_0): the decensoring edit lives in blk.22.attn_output and blk.26.attn_output, and the 10 attention outputs together are only ~40M parameters.
imatrix computed on the decensored BF16 GGUF with bartowski's calibration_datav3.
Converted and quantized with llama.cpp 0a1ef30.

KV cache is small (only 10 of 40 layers are attention, ~20 KB per token in F16), so most of the VRAM goes to weights.

llama-server -m LFM2.5-24B-A2B-Uncensored-IQ3_M.gguf -ngl 99 -c 32768 --jinja \
  --temp 0.1 --top-k 50 --repeat-penalty 1.05

The model always thinks before answering; allow a generous output budget. Liquid AI's official DSpark draft targets the original weights (acceptance on this model not yet measured).

⚠️ Notice

Refusal behavior has been substantially reduced. Intended for research, red-teaming and personal use; you are responsible for your use and for complying with laws and the base model license.

License & credits

Derivative of LiquidAI/LFM2.5-24B-A2B under the LFM Open License v1.0. Decensoring with Heretic (p-e-w, AGPL-3.0) / ARA. Quantized with llama.cpp.

Downloads last month: 6,201

GGUF

Model size

24B params

Architecture

lfm2moe

Hardware compatibility

3-bit

4-bit

Model tree for pottokao/LFM2.5-24B-A2B-Uncensored-GGUF

Base model

LiquidAI/LFM2.5-24B-A2B-Base

Finetuned

LiquidAI/LFM2.5-24B-A2B

Finetuned

pottokao/LFM2.5-24B-A2B-Uncensored

Quantized

(2)

this model