Instructions to use NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use NathMen12/Mistral-7B-Instruct-v0.3-GGUF with Ollama:
ollama run hf.co/NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use NathMen12/Mistral-7B-Instruct-v0.3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NathMen12/Mistral-7B-Instruct-v0.3-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": "NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use NathMen12/Mistral-7B-Instruct-v0.3-GGUF with Docker Model Runner:
docker model run hf.co/NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
- Lemonade
How to use NathMen12/Mistral-7B-Instruct-v0.3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-7B-Instruct-v0.3-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-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 NathMen12/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use NathMen12/Mistral-7B-Instruct-v0.3-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NathMen12/Mistral-7B-Instruct-v0.3-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 "NathMen12/Mistral-7B-Instruct-v0.3-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"
NathMen12/Mistral-7B-Instruct-v0.3-GGUF
English
GGUF quantized version of mistralai/Mistral-7B-Instruct-v0.3, for use with
llama.cpp, Ollama, LM Studio, koboldcpp, and other GGUF-compatible runtimes.
📋 Details
- Base model: mistralai/Mistral-7B-Instruct-v0.3
- Method: GGUF quantization (Q4_K_M, Q5_K_M) via llama.cpp
- Total size: ~8.86 GB
- Generated on: 2026-08-26
- Generated with: automatic GGUF quantization Colab notebook (CPU, no GPU required)
📁 Files
| File | Type | Size |
|---|---|---|
Mistral-7B-Instruct-v0.3-Q4_K_M.gguf |
Q4_K_M | 4.07 GB |
Mistral-7B-Instruct-v0.3-Q5_K_M.gguf |
Q5_K_M | 4.78 GB |
🚀 Usage
# With llama.cpp
./llama-cli -m Mistral-7B-Instruct-v0.3-Q4_K_M.gguf -p "Hello"
# Or with Ollama (minimal Modelfile)
# FROM ./Mistral-7B-Instruct-v0.3-Q4_K_M.gguf
⚠️ Disclaimer
This model was quantized automatically. Always check output quality against the original model before using it in production.
Français
Version quantizée en GGUF de mistralai/Mistral-7B-Instruct-v0.3, pour une
utilisation avec llama.cpp, Ollama, LM Studio, koboldcpp et autres runtimes compatibles GGUF.
📋 Détails
- Modèle de base : mistralai/Mistral-7B-Instruct-v0.3
- Méthode : Quantization GGUF (Q4_K_M, Q5_K_M) via llama.cpp
- Taille totale : ~8.86 GB
- Date de génération : 2026-08-26
- Généré avec : notebook Colab de quantization GGUF automatique (CPU, sans GPU)
📁 Fichiers
| Fichier | Type | Taille |
|---|---|---|
Mistral-7B-Instruct-v0.3-Q4_K_M.gguf |
Q4_K_M | 4.07 GB |
Mistral-7B-Instruct-v0.3-Q5_K_M.gguf |
Q5_K_M | 4.78 GB |
🚀 Utilisation
# Avec llama.cpp
./llama-cli -m Mistral-7B-Instruct-v0.3-Q4_K_M.gguf -p "Bonjour"
# Ou avec Ollama (Modelfile minimal)
# FROM ./Mistral-7B-Instruct-v0.3-Q4_K_M.gguf
⚠️ Avertissement
Ce modèle a été quantizé automatiquement. Vérifie toujours la qualité des sorties par rapport au modèle original avant toute utilisation en production.
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Base model
mistralai/Mistral-7B-v0.3