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

Libraries

How to use AtomicChat/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="AtomicChat/LFM2.5-8B-A1B-GGUF",
	filename="lfm25-8b-a1b-IQ3_M.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 AtomicChat/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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use AtomicChat/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 "AtomicChat/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": "AtomicChat/LFM2.5-8B-A1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

Ollama
How to use AtomicChat/LFM2.5-8B-A1B-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
```

Unsloth Studio

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

How to use AtomicChat/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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

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": "AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AtomicChat/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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AtomicChat/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 AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

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 "AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL" \
  --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 AtomicChat/LFM2.5-8B-A1B-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/LFM2.5-8B-A1B-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.LFM2.5-8B-A1B-GGUF-UD-Q4_K_XL

List all available models

lemonade list

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

LFM2.5 8B A1B, self-quantized to GGUF by Atomic Chat. Built straight from Liquid AI's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

8.5B parameters: the weights this repo quantizes.
Context length: 128,000 tokens (125K), as published by Liquid AI.
24 layers: Mixture-of-Experts.
Full imatrix ladder: every quant is calibrated with an importance matrix.
On-device personal assistant: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
Compressed performance: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
Unmatched throughput: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.

These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Always pass --jinja so the LFM2.5 8B A1B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`LiquidAI/LFM2.5-8B-A1B`
Parameters	8.5B
Layers	24
Experts	32 routed (top-4)
Context length	128,000 tokens (125K)
Vocabulary	128,000
Modalities	Text
Architecture	Mixture-of-Experts, 32 experts (top-4), 32 attention heads over 8 KV heads, `Lfm2MoeForCausalLM`
This repo	GGUF quants (imatrix). Quants: `Q2_K`, `IQ3_M`, `Q3_K_M`, `Q3_K_L`, `IQ4_XS`, `Q4_K_S`, `Q4_K_M`, `UD-Q4_K_XL`, `Q5_K_S`, `Q5_K_M`, `Q6_K`, `Q8_0`

Scores are Liquid AI's published results for the base LiquidAI/LFM2.5-8B-A1B, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

Quant	Size	Notes
`Q2_K`	3.2 GB	Smallest K-quant. Minimal RAM, clear quality drop.
`IQ3_M`	3.8 GB	Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick.
`Q3_K_M`	4.1 GB	Low quality but usable.
`Q3_K_L`	4.4 GB	A step above Q3_K_M.
`IQ4_XS`	4.6 GB	Excellent quality for size. Recommended low-bit.
`Q4_K_S`	4.9 GB	Compact 4-bit, fast.
`Q4_K_M`	5.2 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	5.2 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_S`	5.9 GB	Higher quality, slightly more compact than Q5_K_M.
`Q5_K_M`	6.0 GB	Higher quality, low loss.
`Q6_K`	7.0 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	9.0 GB	Effectively lossless, reference quality.

Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run LFM2.5 8B A1B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/lfm25-8b-a1b-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M
LM Studio / Jan: search the repo id, download any quant.

Best practices

Parameter	Value
temperature	0.2
top_k	80
repetition_penalty	1.05

Liquid AI's recommended sampling configuration for LiquidAI/LFM2.5-8B-A1B.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

Download LiquidAI/LFM2.5-8B-A1B (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.
UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Liquid AI, released under the other license. Full terms: other. Quantized by Atomic Chat.

Downloads last month: 433

GGUF

Model size

8B params

Architecture

lfm2moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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

Base model

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

Finetuned

LiquidAI/LFM2.5-8B-A1B

Quantized

(59)

this model

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

LFM2.5

Collection

Atomic Chat self-quantized GGUFs of Liquid AI's LFM2.5 models, built from the original weights with an importance matrix. • 1 item • Updated Jun 18