In the ever-evolving landscape of artificial intelligence, benchmarking local language models (LLMs) is essential to ensure they perform optimally on your machine. The llm-benchmark tool provides a seamless way to benchmark your models through the Ollama platform. In this guide, we will walk you through the installation and usage of the llm-benchmark tool while addressing common issues you may encounter along the way.

Installation Prerequisites

Before diving into the installation, ensure you have a working installation of Ollama.

Installation Steps

Depending on your Python environment, you can use one of the following commands:

• For Pip: bash pip install llm-benchmark
• For Pipx: bash pipx install llm-benchmark

How to Use llm-benchmark

Once the installation is complete, running the benchmark is a breeze. Simply execute the following command in your terminal:

bash llm_benchmark run

Installation and Usage in Video Format

For a practical demonstration, check out this visual guide: llm-benchmark Walkthrough.

System Requirements

The tool has been tested on Python 3.9 and above, and system requirements vary based on the model:

• 7B model: Requires 8GB of RAM
• 13B model: Requires 16GB of RAM

Usage Explanation

The llm-benchmark tool intelligently checks your system’s RAM size to download the required LLM models:

Think of it as a chef checking the size of their kitchen before preparing a feast. Depending on whether the kitchen (your machine’s RAM) is small, medium, or large, they will decide which dishes (models) to prepare (download).

• If RAM is ≥ 4GB but < 7GB: Checks for gemma:2b and pulls necessary models.
• If RAM is ≥ 7GB but < 15GB: Pulls a wider array of models including phi:3:3.8b, qwen2:7, and others.
• If RAM is > 15GB: Pulls models for the heaviest tasks ensuring your kitchen is equipped for any feast.

Advanced Installation Using Python Poetry

If you wish to set up llm-benchmark with Python Poetry for advanced usage, visit this link for guidance.

Usage in Python Virtual Environment

To run it in a Python virtual environment, execute the following:

bash python3 -m venv .venv
bash .venv/bin/activate
bash pip install -U pip setuptools
bash pip install poetry
bash poetry shell
bash poetry install
bash llm_benchmark hello jason

Example Uses

• Send System Info and Benchmark Results to a Remote Server:

  bash llm_benchmark run

• Do Not Send System Info and Benchmark Results:

  bash llm_benchmark run --no-sendinfo

• Benchmark Run with a Custom Path to the Ollama Executable:

  bash llm_benchmark run --ollamabin=~/code/ollama/ollama

Troubleshooting

If you encounter any issues during the installation or usage of llm-benchmark, consider the following troubleshooting tips:

• Ensure you are running an appropriate version of Python (3.9 or above).
• Check your system’s RAM to ensure compatibility with the models you want to use.
• If the command returns errors, recheck the command syntax and ensure the required dependencies are installed.
• Consult the official Ollama documentation for additional details.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.