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https://github.com/arthur-ai/bench
A tool for evaluating LLMs
https://github.com/arthur-ai/bench
llm mlops
Last synced: 6 days ago
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A tool for evaluating LLMs
- Host: GitHub
- URL: https://github.com/arthur-ai/bench
- Owner: arthur-ai
- License: mit
- Created: 2023-07-07T14:40:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-10T14:20:46.000Z (6 months ago)
- Last Synced: 2024-11-08T14:58:55.883Z (8 days ago)
- Topics: llm, mlops
- Language: TypeScript
- Homepage: https://bench.readthedocs.io
- Size: 10.4 MB
- Stars: 389
- Watchers: 10
- Forks: 37
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-llm-eval - Arthur Bench - 10-06) | (Tools)
README
# Bench
Bench is a tool for evaluating LLMs for production use cases. Whether you are comparing different LLMs, considering different prompts, or testing generation hyperparameters like temperature and # tokens, Bench provides one touch point for all your LLM performance evaluation.
If you have encountered a need for any of the following in your LLM work, then Bench can help with your evaluation:
- to standardize the workflow of LLM evaluation with a common interface across tasks and use cases
- to test whether open source LLMs can do as well as the top closed-source LLM API providers on your specific data
- to translate the rankings on LLM leaderboards and benchmarks into scores that you care about for your actual use case
Join the bench community on [Discord](https://discord.gg/tdfUAtaVHz).
For bug fixes and feature requests, please file a Github issue.
## Package installation
Install Bench to your python environment with optional dependencies for serving results locally (recommended):
`pip install 'arthur-bench[server]'`
Alternatively, install Bench to your python environment with minimum dependencies:
`pip install arthur-bench`
For further setup instructions visit our [installation guide](https://bench.readthedocs.io/en/latest/setup.html)
## Using Bench
For a more in-depth walkthrough of using bench, visit our [quickstart walkthrough](https://bench.readthedocs.io/en/latest/quickstart.html) and our [test suite creation guide](https://bench.readthedocs.io/en/latest/creating_test_suites.html) on our docs.
To make sure you can run test suites in bench, you can run the following code snippets to create a test suite and run it to give a score to candidate outputs.
```python
from arthur_bench.run.testsuite import TestSuite
suite = TestSuite(
"bench_quickstart",
"exact_match",
input_text_list=["What year was FDR elected?", "What is the opposite of down?"],
reference_output_list=["1932", "up"]
)
suite.run("quickstart_run", candidate_output_list=["1932", "up is the opposite of down"])
```
Saved test suites can be loaded later on to benchmark test performance over time, without needing to re-prepare reference data:
```python
existing_suite = TestSuite("bench_quickstart", "exact_match")
existing_suite.run("quickstart_new_run", candidate_output_list=["1936", "up"])
```
To view the results for these runs in the local UI that comes with the `bench` package, run `bench` from the command line (this requires the bench optional server dependencies to be installed):
```
bench
```
Viewing examples in the bench UI will look something like this:
## Running Bench from source
To launch Bench from source:
1. Install the dependencies
* `pip install -e '.[server]'`
2. Build the Front End
* `cd arthur_bench/server/js`
* `npm i`
* `npm run build`
3. Launch the server
* `bench`
Because the server was installed with `pip -e`, local changes will be picked up. However, the server will need to be restarted between
changes in order for those changes to be picked up.