Instructions to use hauser458b/lfm2.5-230m-code-math-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hauser458b/lfm2.5-230m-code-math-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hauser458b/lfm2.5-230m-code-math-GGUF",
	filename="lfm2.5-230m-code-math-F16.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 hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hauser458b/lfm2.5-230m-code-math-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hauser458b/lfm2.5-230m-code-math-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": "hauser458b/lfm2.5-230m-code-math-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M

Ollama
How to use hauser458b/lfm2.5-230m-code-math-GGUF with Ollama:
```
ollama run hf.co/hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
```

Unsloth Studio

How to use hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hauser458b/lfm2.5-230m-code-math-GGUF to start chatting

How to use hauser458b/lfm2.5-230m-code-math-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hauser458b/lfm2.5-230m-code-math-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": "hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hauser458b/lfm2.5-230m-code-math-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hauser458b/lfm2.5-230m-code-math-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 "hauser458b/lfm2.5-230m-code-math-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 hauser458b/lfm2.5-230m-code-math-GGUF with Docker Model Runner:
```
docker model run hf.co/hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M
```

Lemonade

How to use hauser458b/lfm2.5-230m-code-math-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hauser458b/lfm2.5-230m-code-math-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.lfm2.5-230m-code-math-GGUF-Q4_K_M

List all available models

lemonade list

lfm2.5-230m-code-math-GGUF / README.md

hauser

Update README.md

68beb6d verified 4 days ago

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metadata

license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE
base_model: hauser458original/lfm2.5-230m-code-math
tags:
  - lfm2
  - lfm2.5
  - liquid
  - code
  - math
  - gguf
  - llama.cpp
language:
  - en
pipeline_tag: text-generation

LFM2.5-230M-Code-Math-GGUF

GGUF quantized versions of hauser458original/lfm2.5-230m-code-math, a code/math-focused fine-tune of LiquidAI/LFM2.5-230M (instruct). See the base fine-tune's model card for full training details, evaluation notes, and known limitations.

For use with llama.cpp, Ollama, LM Studio, or any other GGUF-compatible runtime.

Files

File	Quantization	Approx. size	Notes
`lfm2.5-230m-code-math-F16.gguf`	F16	~460 MB	Full precision, largest, highest fidelity
`lfm2.5-230m-code-math-Q8_0.gguf`	Q8_0	~245 MB	Near-lossless, good default if size isn't a concern
`lfm2.5-230m-code-math-Q5_K_M.gguf`	Q5_K_M	~165 MB	Good balance of size/quality
`lfm2.5-230m-code-math-Q5_K_S.gguf`	Q5_K_S	~155 MB	Slightly smaller than Q5_K_M, marginal quality trade-off
`lfm2.5-230m-code-math-Q4_K_M.gguf`	Q4_K_M	~135 MB	Smallest here, most aggressive quantization, best for constrained/edge devices

(Sizes are approximate — check actual file sizes in the repo.)

Usage

llama.cpp

./llama-cli -m lfm2.5-230m-code-math-Q5_K_M.gguf -p "Write a Python function to check if a number is prime."

Ollama

ollama run hf.co/hauser458original/lfm2.5-230m-code-math-GGUF:Q5_K_M

LM Studio

Search for hauser458original/lfm2.5-230m-code-math-GGUF in the LM Studio model browser, or download a .gguf file directly and load it manually.

Which quant should I use?

Q4_K_M: smallest footprint, best for very constrained devices (older phones, low-RAM edge hardware). Some quality loss vs. higher quants.
Q5_K_S / Q5_K_M: good middle ground — recommended default for most laptop/desktop CPU inference.
Q8_0: near-lossless, use if you have the RAM/storage headroom and want output as close as possible to the original safetensors model.
F16: full precision GGUF, only needed if you plan to re-quantize yourself or want the highest possible fidelity in llama.cpp.

License

Inherits the LFM Open License v1.0 from the base model.