Instructions to use Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Theboukadida/laura-fr-lfm2.5-1.2b-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": "Theboukadida/laura-fr-lfm2.5-1.2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M

Ollama
How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with Ollama:
```
ollama run hf.co/Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Theboukadida/laura-fr-lfm2.5-1.2b-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": "Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with Docker Model Runner:
```
docker model run hf.co/Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M
```

Lemonade

How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.laura-fr-lfm2.5-1.2b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-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 Theboukadida/laura-fr-lfm2.5-1.2b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Theboukadida/laura-fr-lfm2.5-1.2b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Theboukadida/laura-fr-lfm2.5-1.2b-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 "Theboukadida/laura-fr-lfm2.5-1.2b-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"

laura-fr-lfm2.5-1.2b-GGUF / README.md

Theboukadida

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metadata

license: other
license_name: lfm1.0
license_link: LICENSE
base_model: LiquidAI/LFM2.5-1.2B-Instruct
language:
  - de
  - fr
pipeline_tag: text-generation
tags:
  - gguf
  - lfm2
  - german
  - language-learning
  - on-device

Laura — German tutor in French (LFM2.5 1.2B, GGUF)

Laura is the tutor in an offline German course app for adult beginners (A1–A2). This model talks to the learner in French and teaches German with German examples. It runs on the phone, CPU only.

This is a modified version of LiquidAI/LFM2.5-1.2B-Instruct. Changes made: a LoRA adapter (rank 16, all linear layers, 2 epochs) was trained on ~2,500–3,300 short tutoring conversations in French, merged into the weights, and the result converted to GGUF and quantised to Q4_K_M with llama.cpp. Distributed under the base model's LFM Open License v1.0 — see LICENSE.

How the app uses it

The app does not trust the model to judge German on its own. It checks the learner's sentence with its own dictionary first and adds the verdict to the message — (Check: ✗ → „corrected sentence“ · reason) or (Check: ✓ …) — plus dictionary meanings and the chapter's rule card. The model was trained with exactly those notes and explains them; without them it is a much weaker teacher.

Evaluation

Hand-graded on held-out conversations it never saw in training: correct teaching in 73.5% of replies (every German fact and reason right).

Files

laura-fr-lfm2.5-1.2b-Q4_K_M.gguf — Q4_K_M, 0.73 GB