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

File size: 1,254 Bytes

3238f46

schema_version: 1
id: core.learner.assignment.replace
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 devoir et vérifiez que la remise est encore modifiable.
- order: 2
  instruction: Sélectionnez Modifier le travail.
- order: 3
  instruction: Supprimez le fichier à remplacer dans la zone de remise.
- order: 4
  instruction: Ajoutez le nouveau fichier et attendez la fin de son dépôt.
- order: 5
  instruction: Sélectionnez Enregistrer.
- order: 6
  instruction: Si les brouillons sont activés, remettez de nouveau le devoir et confirmez.
expected_result: Le nouveau fichier apparaît dans la remise à la place de l'ancien, avec l'état de remise
  attendu.
review:
  status: approved
  reviewer: Claude Fable 5 — revue experte IA (5 relecteurs parallèles), dérogation à la double revue
    humaine actée par le propriétaire le 2026-07-11
  verified_at: '2026-07-11T20:30:00Z'