Instructions to use zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaakirio/LFM2.5-1.2B-Instruct-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": "zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

Ollama
How to use zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF with Ollama:
```
ollama run hf.co/zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-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": "zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zaakirio/LFM2.5-1.2B-Instruct-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 zaakirio/LFM2.5-1.2B-Instruct-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 "zaakirio/LFM2.5-1.2B-Instruct-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"

LFM2.5-1.2B-Instruct-Uncensored — GGUF

GGUF quantizations of zaakirio/LFM2.5-1.2B-Instruct-Uncensored, a decensored (Heretic-abliterated) version of LiquidAI/LFM2.5-1.2B-Instruct.

These files run with llama.cpp and any tool built on it e.g. LM Studio, Jan, koboldcpp, etc.

Requires a recent llama.cpp build. LFM2 is a hybrid (convolution + attention) architecture; only llama.cpp builds that include LFM2 support can load these files. Use a current release (or current LM Studio / Jan). Older builds will fail with an "unknown architecture 'lfm2'" error.

Files

File	Quant	Size	Notes
`LFM2.5-1.2B-Instruct-Uncensored-Q3_K_M.gguf`	Q3_K_M	573 MB	Smallest; lowest quality. For very tight memory.
`LFM2.5-1.2B-Instruct-Uncensored-Q4_K_M.gguf`	Q4_K_M	697 MB	Recommended — best size/quality balance.
`LFM2.5-1.2B-Instruct-Uncensored-Q5_K_M.gguf`	Q5_K_M	804 MB	Higher quality, slightly larger.
`LFM2.5-1.2B-Instruct-Uncensored-Q6_K.gguf`	Q6_K	918 MB	Near-lossless.
`LFM2.5-1.2B-Instruct-Uncensored-Q8_0.gguf`	Q8_0	1.2 GB	Effectively lossless vs the BF16 source.

Not sure which to pick? Start with Q4_K_M. Go up to Q5/Q6/Q8 if you have the memory and want maximum fidelity; drop to Q3 only if you're memory-constrained.

Usage

llama.cpp (auto-download from this repo)

# Interactive chat — downloads the chosen quant automatically
llama-cli -hf zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M

# OpenAI-compatible server
llama-server -hf zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF:Q4_K_M -c 4096

Or, with a file you've already downloaded:

llama-cli -m LFM2.5-1.2B-Instruct-Uncensored-Q4_K_M.gguf -p "Hello, who are you?"

LM Studio / Jan

Search for zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF in the in-app model browser, or download a .gguf file from this page and load it.

Download a single file

pip install -U "huggingface_hub[cli]"
hf download zaakirio/LFM2.5-1.2B-Instruct-Uncensored-GGUF \
  --include "LFM2.5-1.2B-Instruct-Uncensored-Q4_K_M.gguf" --local-dir ./

Prompt format

The chat template is embedded in the GGUF files, so chat-aware tools apply it automatically. For reference, it is ChatML-style:

<|startoftext|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

About the base model

This is a decensored derivative produced with Heretic (automatic directional ablation). Compared with the original LFM2.5-1.2B-Instruct:

Metric	Decensored	Original
Refusals (/100 harmful prompts)	5	98
KL divergence (harmless prompts)	0.1003	0 (by definition)

See the source model card for the full abliteration parameters and run details.

Intended use & disclaimer

This model has had its refusal behavior substantially removed and will comply with requests the original model would have declined. It is provided for research and unrestricted local use. You are responsible for how you use it and for complying with all applicable laws and with the base model's lfm1.0 license, which carries over to this derivative.

Provenance

Quantized from zaakirio/LFM2.5-1.2B-Instruct-Uncensored (BF16) using llama.cpp convert_hf_to_gguf.py + llama-quantize.
Base model: LiquidAI/LFM2.5-1.2B-Instruct
Decensoring tool: Heretic by p-e-w