vidcutter VS evals

Check out losslesscut, vidcutter and avidemux.

Try out vidcutter, based on the popular media player mpv.

I use it to trim all my game clips. It also supports displaying HDR videos properly, something I'm not aware if similar programs like VidCutter have.

Not sure exactly what your use case is, but I use vidcutter for lossless clip extraction and it works great and does not require Electron (it's QT based I believe). https://github.com/ozmartian/vidcutter

Some that seems suitable for your use case is avidemux and vidcutter, you might want to check them up

VidCutter: https://github.com/ozmartian/vidcutter

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.

OpenAI open sourced their evals framework. You can use it to evaluate different models but also your entire prompt chain setup. https://github.com/openai/evals

They also have a registry of evals built in.

"What if" is all these "existential risk" conversations ever are.

Where is your evidence that we're approaching human level AGI, let alone SuperIntelligence? Because ChatGPT can (sometimes) approximate sophisticated conversation and deep knowledge?

How about some evidence that ChatGPT isn't even close? Just clone and run OpenAI's own evals repo https://github.com/openai/evals on the GPT-4 API.

It performs terribly on novel logic puzzles and exercises that a clever child could learn to do in an afternoon (there are some good chess evals, and I submitted one asking it to simulate a Forth machine).

You can get GPT 4 access by submitting an eval if gets merged (https://github.com/openai/evals). Here's the one that got me access[1]

Although from the blog post it looks like they're planning to open up to everyone soon, so that may happen before you get through the evals backlog.

1: https://github.com/openai/evals/pull/778

Yeah, this is the repo -> openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. (github.com)

OpenAI Evals

Clone OpenAI's Evals project: First, head to the OpenAI's evals repo at https://github.com/openai/evals. Clone the repo, navigate to the project's examples directory, and create a new directory called code_generators.