bocoel
opencompass
| bocoel | opencompass | |
|---|---|---|
| 1 | 1 | |
| 259 | 2,968 | |
| - | 13.8% | |
| 9.4 | 9.7 | |
| about 1 month ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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bocoel
opencompass
-
Show HN: Times faster LLM evaluation with Bayesian optimization
Fair question.
Evaluate refers to the phase after training to check if the training is good.
Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!
So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.
What are some alternatives?
lm-evaluation-harness - A framework for few-shot evaluation of language models.
deepeval - The LLM Evaluation Framework
promptbench - A unified evaluation framework for large language models
evals - Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.