Instructions to use EnlistedGhost/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 EnlistedGhost/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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
Use Docker
docker model run hf.co/EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EnlistedGhost/Mistral-7B-Instruct-v0.3-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": "EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
- Ollama
How to use EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF with Ollama:
ollama run hf.co/EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
- Unsloth Desktop
- Pi
How to use EnlistedGhost/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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
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": "EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF with Docker Model Runner:
docker model run hf.co/EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
- Lemonade
How to use EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Mistral-7B-Instruct-v0.3-GGUF-Q4_K_S
List all available models
lemonade list
- Hermes Agent
How to use EnlistedGhost/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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use EnlistedGhost/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 EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S
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 "EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF:Q4_K_S" \ --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"
------------------------------------------------
- Model Details and Specifications: -
------------------------------------------------
This release contains:
Llama.cpp and Ollama compatible GGUF converted and Quantized model files
(Compatible with both Ollama, and Llama.cpp)
Quantized GGUF version of:
- mistralai/Mistral-7B-Instruct-v0.3
(by MistralAI)
Original Model Link:
Citation (Original Paper)
-------------------------------------------------------------
- GGUF Conversion and Quantization Details: -
-------------------------------------------------------------
Software used to convert Safetensors to GGUF:
Software used to create Quantized GGUF Files:
Specific GitHub Commit Point:
Converted to GGUF and Quantized by:
--------------------------
---- Original Info ----
--------------------------
(Crossposted from the link in the above section: "Model Details"):
Model Card for Mistral-7B-Instruct-v0.3
The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
Mistral-7B-v0.3 has the following changes compared to Mistral-7B-v0.2
- Extended vocabulary to 32768
- Supports v3 Tokenizer
- Supports function calling
Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall
- Downloads last month
- 55
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
Model tree for EnlistedGhost/Mistral-7B-Instruct-v0.3-GGUF
Base model
mistralai/Mistral-7B-v0.3