Instructions to use IngeniumDL/ingenium-expert-lms-3b-instruct-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 IngeniumDL/ingenium-expert-lms-3b-instruct-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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

Use Docker

docker model run hf.co/IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

LM Studio
Jan
Ollama
How to use IngeniumDL/ingenium-expert-lms-3b-instruct-gguf with Ollama:
```
ollama run hf.co/IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
```

Unsloth Studio

How to use IngeniumDL/ingenium-expert-lms-3b-instruct-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 IngeniumDL/ingenium-expert-lms-3b-instruct-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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IngeniumDL/ingenium-expert-lms-3b-instruct-gguf to start chatting

How to use IngeniumDL/ingenium-expert-lms-3b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

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": "IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IngeniumDL/ingenium-expert-lms-3b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0
```

Lemonade

How to use IngeniumDL/ingenium-expert-lms-3b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

Run and chat with the model

lemonade run user.ingenium-expert-lms-3b-instruct-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IngeniumDL/ingenium-expert-lms-3b-instruct-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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

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 IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IngeniumDL/ingenium-expert-lms-3b-instruct-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0

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 "IngeniumDL/ingenium-expert-lms-3b-instruct-gguf:Q8_0" \
  --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"

ingenium-expert-lms-3b-instruct-gguf / data /procedures /core.learner.assignment.submit.yaml

ArnaudIngenium

Paquet complet : harnais, docker-compose CPU, CLI, registres

3238f46 verified about 1 month ago

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	schema_version: 1
	id: core.learner.assignment.submit
	product: moodle_lms
	branch: '4.5'
	tested_versions:
	- '4.5.12 (Build: 20260608)'
	locale: fr
	theme: boost
	audience: learner
	required_capabilities:
	- mod/assign:submit
	source_refs:
	- url: https://github.com/moodle/moodle/blob/b9315ff5cb42e11b40c972f3f7194ba8b0c0bbf8/mod/assign/view.php
	revision: b9315ff5cb42e11b40c972f3f7194ba8b0c0bbf8
	license: GPL-3.0-or-later
	steps:
	- order: 1
	instruction: Ouvrez le cours puis le devoir concerné.
	- order: 2
	instruction: Vérifiez l'état de la remise, la date limite et les consignes.
	- order: 3
	instruction: Sélectionnez Ajouter un travail.
	- order: 4
	instruction: Ajoutez le texte en ligne ou le fichier demandé, selon les types de remise activés.
	- order: 5
	instruction: Sélectionnez Enregistrer.
	- order: 6
	instruction: Si le devoir utilise les brouillons, sélectionnez Envoyer le devoir puis confirmez.
	expected_result: L'état de la remise indique qu'un travail a été remis, avec la date et les fichiers ou
	textes enregistrés.
	review:
	status: in_review
	reviewer: null
	verified_at: null