Instructions to use NobodyWho/LFM2.5-8B-A1B-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 NobodyWho/LFM2.5-8B-A1B-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 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 serve -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

Llama new
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 Desktop

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 serve -hf NobodyWho/LFM2.5-8B-A1B-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": "NobodyWho/LFM2.5-8B-A1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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 serve -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

OpenClaw

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

Start the llama.cpp server

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

LFM2.5-8B-A1B-GGUF / README.md

gergelyvagujhelyi

Fix: replace ~ with ≈ (avoid markdown strikethrough)

5a9b8ba verified 4 months ago

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metadata

license: other
license_name: lfm-open-license-v1.0
license_link: https://www.liquid.ai/lfm-open-license
base_model: LiquidAI/LFM2.5-8B-A1B
tags:
  - gguf
  - nobodywho
  - tool-calling
  - lfm2
  - moe
pipeline_tag: text-generation
library_name: gguf

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.