Instructions to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF",
	filename="mistral-7b-instruct-v0.3-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF to start chatting

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M

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": "dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-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 dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF

This model was converted to GGUF format from mistralai/Mistral-7B-Instruct-v0.3 using llama.cpp via Convert Model to GGUF.

Key Features:

Quantized for reduced file size (GGUF format)
Optimized for use with llama.cpp
Compatible with llama-server for efficient serving

Refer to the original model card for more details on the base model.

Usage with llama.cpp

1. Install llama.cpp:

brew install llama.cpp  # For macOS/Linux

2. Run Inference:

CLI:

llama-cli --hf-repo dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q4_k_m.gguf -p "Your prompt here"

Server:

llama-server --hf-repo dynomite567/Mistral-7B-Instruct-v0.3-Q4_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q4_k_m.gguf -c 2048

For more advanced usage, refer to the llama.cpp repository.

Downloads last month: 8

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

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