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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NobodyWho/LFM2.5-8B-A1B-GGUF",
	filename="LFM2.5-8B-A1B-F16-vendor-sampling.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 NobodyWho/LFM2.5-8B-A1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio

How to use NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use NobodyWho/LFM2.5-8B-A1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NobodyWho/LFM2.5-8B-A1B-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": "NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NobodyWho/LFM2.5-8B-A1B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

NobodyWho/LFM2.5-8B-A1B-GGUF

Overview

GGUF quantization of LiquidAI's LFM2.5-8B-A1B model, prepared for NobodyWho: it works with NobodyWho out of the box, with LiquidAI's recommended sampling metadata embedded in every quant. LFM2.5-8B-A1B is a sparse Mixture-of-Experts model (8B total / ≈1B active per token) built on the hybrid LFM2 architecture — the fastest model in its size class on both CPU and GPU. Note: tool calling is unreliable on this model — see the Tool calling note below.

Model Capabilities

Text generation — instruction-following chat
Tool calling — supported but unreliable: the model often answers in prose instead of invoking a tool. NobodyWho suite: 8/14 (F16, Q8_0), 4/14 (Q4_K_M)
Long context — 128k tokens
Efficient MoE — 8B total / ≈1B active per token

NobodyWho preparation

The upstream GGUF (built from LiquidAI commit feb5e04) already renders tool calls correctly in the model's native markup — <|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|> — so nothing needs patching; NobodyWho just verifies it with the test suite. The -vendor-sampling quants additionally embed LiquidAI's recommended sampling settings as general.sampling.* metadata, which NobodyWho reads and applies by default (see core/src/sampler.rs).

Available Quantizations

File	Approach	Tool-calling tests
`LFM2.5-8B-A1B-F16-vendor-sampling.gguf`	Vendor sampling injected	8/14
`LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf`	Vendor sampling injected	8/14
`LFM2.5-8B-A1B-Q4_K_M-vendor-sampling.gguf`	Vendor sampling injected	4/14

Tool-calling results from NobodyWho's suite (June 2026). Failures are the model declining to emit a tool call on complex parameter schemas (sets / tuples / nested lists / dicts), not a format error. Vendor sampling does not change the result (verified with and without). The -vendor-sampling suffix marks files that embed general.sampling.* metadata.

Quick Start

Using the NobodyWho library:

from nobodywho import Chat

chat = Chat("huggingface:NobodyWho/LFM2.5-8B-A1B-GGUF/LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf")
response = chat.ask("What is the capital of Denmark?").completed()
print(response)  # The capital of Denmark is Copenhagen.

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="NobodyWho/LFM2.5-8B-A1B-GGUF",
    filename="LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf",
)

Model Specifications

Parameters: 8B total / ≈1B active (MoE)
Context length: 128,000 tokens
License: LFM Open License v1.0
Base model: LiquidAI/LFM2.5-8B-A1B
Architecture: lfm2moe

Licensing / Credits

Licensed under LFM Open License v1.0 (unchanged from upstream). All model credit belongs to Liquid AI.

Downloads last month: 127

GGUF

Model size

8B params

Architecture

lfm2moe

Hardware compatibility

4-bit

8-bit

16-bit

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

Base model

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

Finetuned

LiquidAI/LFM2.5-8B-A1B

Quantized

(51)

this model

Collection including NobodyWho/LFM2.5-8B-A1B-GGUF

LFM 2.5

Collection

LiquidAI LFM GGUFs prepared for tool calling: fixed chat templates and vendor sampling metadata, verified with NobodyWho's test suite. • 4 items • Updated 2 days ago