promptbench VS tbls

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.

Autogenerated database documentation is often pretty hit and miss but tbls[1] does a pretty good job in that space. Especially when you comment on your tables, fields, views, functions etc (which is a good habit anyway!) the output is quite useful

[1] https://github.com/k1LoW/tbls

In-database comments combined with something like https://github.com/k1LoW/tbls make for very cheap database documentation.

No affiliation with tbls except that I'm a big fan

Check out tbls. You can create a ERD in one command