Introduction to BenchLLM
BenchLLM is an innovative and open-source Python library designed to enhance the testing and development of Large Language Models (LLMs) and AI-driven applications. It serves as a tool to measure model accuracy by validating responses across multiple test scenarios, using LLMs to assist in these evaluations. BenchLLM finds its roots in V7 Labs, where it significantly contributes to advancing AI applications.
Purpose and Features
BenchLLM provides users with several key functionalities:
- Prompt Testing: It enables developers to test the responses of their LLMs through a variety of prompts.
- Continuous Integration: The library facilitates continuous integration for notable chains like Langchain, agents such as AutoGPT, or prominent LLMs including Llama and GPT-4.
- Error Identification: Users can detect inaccuracies and computational hallucinations within their applications.
- Code Assurance: By eliminating unreliable operations, BenchLLM helps build trust in the deployed applications.
Early Stage Development
BenchLLM is in its developmental stages, which suggests that rapid updates and changes are to be expected. Users are invited to partake in reporting bugs or suggesting improvements through GitHub.
Testing Methodology
BenchLLM employs a robust two-step process to validate the performance of machine learning models:
- Testing: The initial phase involves executing code against a suite of test responses to gather model predictions.
- Evaluation: These predictions are then assessed against expected outcomes, utilizing LLMs or manual comparisons to ascertain their factual integrity. This step generates detailed reports that highlight the pass/fail status and other crucial metrics.
This methodological distinction aids developers in gaining a thorough understanding of their models' capabilities and allows for precise fine-tuning.
How to Install and Use
To begin using BenchLLM, it can be easily installed via pip:
pip install benchllm
Once installed, users can start by importing the library and marking desired functions for testing with the @benchllm.test decorator. Tests are crafted with YAML/JSON files, specifying input queries and the expected model outputs.
BenchLLM offers diverse evaluative options to verify whether predictions align with test expectations. The standard evaluator uses OpenAI's GPT-3, for which an API key is required. Developers can also choose from semantic, embedding, string-match, interactive, and web-based evaluation methods. Parallel execution support is available through the --workers parameter to expedite evaluations.
Caching, Mocking, and Evaluation
BenchLLM incorporates caching to enhance the performance and efficiency of evaluations:
- Types of Caches: Users can choose between memory, file, and none caches, depending on whether they want temporary or persistent data storage.
- Mocking External Functions: The tool supports the mocking of external functions that models might interact with during testing, making tests more predictable.
- Separate Evaluation: For flexibility, users can perform testing and evaluation steps independently, allowing for manual evaluations or the application of various evaluation methods at different times.
Available Commands
BenchLLM offers several handy commands to manage the testing process:
bench add: Add a new test to a suite.bench tests: List all tests in a suite.bench run: Execute all or selected tests.bench eval: Evaluate the results from previous test executions.
Contribution and Community
BenchLLM welcomes contributions, encouraging community members to help further its development. It is optimized for Python 3.10 and follows the PEP8 coding standards. Contributors are urged to make adjustments and submit pull requests after proper testing.
For additional support or to contribute, users can reach out via GitHub issues or join the conversation on Discord or Twitter.
In sum, BenchLLM is a powerful, yet developing tool, aiming to simplify and refine the testing of AI models and applications through methodical and systematic processes. Its open-source nature and community-driven approach promise continual growth and enhancement of functionalities.