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
- llama.cpp
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
- Pi
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"
| schema_version: 1 | |
| id: core.admin.tasks.view | |
| product: moodle_lms | |
| branch: '4.5' | |
| tested_versions: | |
| - '4.5.12 (Build: 20260608)' | |
| locale: fr | |
| theme: boost | |
| audience: admin | |
| required_capabilities: | |
| - moodle/site:config | |
| source_refs: | |
| - url: https://github.com/moodle/moodle/blob/b9315ff5cb42e11b40c972f3f7194ba8b0c0bbf8/admin/tool/task/scheduledtasks.php | |
| revision: b9315ff5cb42e11b40c972f3f7194ba8b0c0bbf8 | |
| license: GPL-3.0-or-later | |
| steps: | |
| - order: 1 | |
| instruction: Ouvrez Administration du site, puis Serveur > Tâches > Tâches programmées. | |
| - order: 2 | |
| instruction: Recherchez la tâche par son nom ou son composant. | |
| - order: 3 | |
| instruction: Contrôlez sa planification, sa dernière exécution, sa prochaine exécution et son délai | |
| d'échec. | |
| - order: 4 | |
| instruction: Ouvrez les journaux de tâches si un diagnostic détaillé est nécessaire. | |
| expected_result: L'état et la planification de la tâche sont visibles sans modifier son exécution. | |
| 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' | |