Instructions to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF",
	filename="LFM2.5-8B-A1B-Uncensored-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaakirio/LFM2.5-8B-A1B-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 zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zaakirio/LFM2.5-8B-A1B-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 zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

Ollama
How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF to start chatting

How to use zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-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"

Docker Model Runner
How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-8B-A1B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

LFM2.5-8B-A1B-Uncensored — GGUF

GGUF quantizations of zaakirio/LFM2.5-8B-A1B-Uncensored, a decensored (Heretic-abliterated) version of LiquidAI/LFM2.5-8B-A1B.

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

Requires a recent llama.cpp build with LFM2 MoE support. This model uses the lfm2moe architecture (hybrid short-conv + attention with 32 experts, 4 active per token). Only llama.cpp builds that include Lfm2MoeForCausalLM support can load these files. Use a current release (or current Ollama / LM Studio). Older builds will fail with an "unknown architecture 'lfm2moe'" error.

Files

File	Quant	Size	BPW	Notes
`LFM2.5-8B-A1B-Uncensored-Q2_K.gguf`	Q2_K	3.0 GB	3.01	Smallest; significant quality loss but works on very constrained hardware.
`LFM2.5-8B-A1B-Uncensored-Q3_K_S.gguf`	Q3_K_S	3.5 GB	3.54	Small, lower quality.
`LFM2.5-8B-A1B-Uncensored-Q3_K_M.gguf`	Q3_K_M	3.9 GB	3.87	Small; some quality loss.
`LFM2.5-8B-A1B-Uncensored-IQ4_XS.gguf`	IQ4_XS	4.3 GB	4.25	Smaller than Q4_K_S with comparable quality; uses iquant scheme.
`LFM2.5-8B-A1B-Uncensored-Q4_K_S.gguf`	Q4_K_S	4.6 GB	4.59	Slightly smaller than Q4_K_M.
`LFM2.5-8B-A1B-Uncensored-Q4_K_M.gguf`	Q4_K_M	4.9 GB	4.85	Recommended — best size/quality balance for most users.
`LFM2.5-8B-A1B-Uncensored-Q5_K_S.gguf`	Q5_K_S	5.5 GB	5.49	Higher quality.
`LFM2.5-8B-A1B-Uncensored-Q5_K_M.gguf`	Q5_K_M	5.7 GB	5.69	Higher quality, marginally larger.
`LFM2.5-8B-A1B-Uncensored-Q6_K.gguf`	Q6_K	6.5 GB	6.56	Near-lossless.
`LFM2.5-8B-A1B-Uncensored-Q8_0.gguf`	Q8_0	8.4 GB	8.50	Effectively lossless vs the BF16 source.
`LFM2.5-8B-A1B-Uncensored-BF16.gguf`	BF16	16 GB	16.00	Full precision, identical numerics to the source HF model.

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 or Q2 only if you're memory-constrained. Because this is an MoE with only ~1B active parameters per token, inference throughput is fast even at the larger quants if your hardware has the RAM.

Usage

llama.cpp (auto-download from this repo)

# Interactive chat — downloads the chosen quant automatically
llama-cli -hf zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

# OpenAI-compatible server
llama-server -hf zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M -c 4096

Or, with a file you've already downloaded:

llama-cli -m LFM2.5-8B-A1B-Uncensored-Q4_K_M.gguf -p "Hello, who are you?"

Ollama

ollama run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF:Q4_K_M

LM Studio / Jan

Search for zaakirio/LFM2.5-8B-A1B-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/LFM2.5-8B-A1B-Uncensored-GGUF \
  --include "LFM2.5-8B-A1B-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 LFM2.5-8B-A1B:

Metric	Decensored	Original
Refusals (/100 harmful prompts)	0	0
KL divergence (harmless prompts)	0.0481	0 (by definition)

The base LFM2.5-8B-A1B measured 0–2 / 100 refusals on Heretic's marker-based detector (compared to ~98 / 100 for its smaller sibling), suggesting it is comparatively compliant out of the box. The abliteration still makes real, measurable changes to the attention and dense MLP projections (KL ≈ 0.05).

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/LFM2.5-8B-A1B-Uncensored (BF16) using llama.cpp convert_hf_to_gguf.py + llama-quantize.
Base model: LiquidAI/LFM2.5-8B-A1B
Decensoring tool: Heretic by p-e-w

Downloads last month: 1,127

GGUF

Model size

8B params

Architecture

lfm2moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for zaakirio/LFM2.5-8B-A1B-Uncensored-GGUF

Base model

LiquidAI/LFM2.5-8B-A1B-Base

Finetuned

LiquidAI/LFM2.5-8B-A1B

Finetuned

zaakirio/LFM2.5-8B-A1B-Uncensored

Quantized

(2)

this model