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llmbench

v1.0.2

Published

benchmark the llm api user want to test...

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Links

Git

Maintainers

eric8810eric8810

Keywords

Readme

LLM API Benchmark

A command-line tool for benchmarking various Large Language Model (LLM) APIs. This tool allows you to test different LLM models with standardized prompts to evaluate their performance and capabilities.

Features

  • Support for different LLM models and APIs
  • Multiple testing modes:
    • Chat completion testing
    • Reasoning capability testing
  • Configurable API endpoints and parameters
  • Streaming response support
  • Easy-to-use command-line interface

Usage

Install and run directly using npx:

npx llmbench run [options]

Options

  • -m, --model <model>: Specify the LLM model name to test
  • -u, --url <url>: Specify the LLM API URL to test
  • -k, --api-key <api-key>: Provide the LLM API key
  • -t, --type <type>: Choose the type of test (default: "chat")
    • Available types: "chat", "reason"

Examples

  1. Run a basic chat completion test:
npx llmbench run -m gpt-4 -u https://api.openai.com/v1 -k your-api-key
  1. Run a reasoning capability test:
npx llmbench run -m gpt-4 -u https://api.openai.com/v1 -k your-api-key -t reason

Test Types

Chat Completion Test

The chat completion test uses a simple prompt asking for a Shakespeare-style poem about a cat. This test evaluates the basic generation capabilities of the model.

Reasoning Test

The reasoning test uses a more complex system prompt that evaluates the model's ability to follow guidelines and provide structured responses while maintaining specific operational parameters.

Development

Local Installation

  1. Clone the repository:
git clone <repository-url>
cd llm-api-benchmark
  1. Install dependencies:
npm install
  1. Build the project:
npm run build

This project is built with TypeScript and uses the following key dependencies:

  • Commander.js for CLI interface
  • OpenAI SDK for API interactions
  • Chalk for colored console output

To contribute to the project:

  1. Fork the repository
  2. Create your feature branch
  3. Make your changes
  4. Submit a pull request

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.