evals VS reflex

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).

Ah well, I think you just got lucky then, I did the same with the survey. I'll be compulsively checking mine all day today lol. People on Reddit like to say that if you did an Eval which is basically a performance test natively run using code on GPT models, then OpenAI is more likely to favor you when they’re releasing new features. If ydk, then I guess that answers that.

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

来源：GitHub - openai/evals: Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. 8

Hey thanks for the feedback. We're working on relaxing our dependencies [1] to make reflex more compatible. Do you remember what libraries you had the conflict with?

[1] https://github.com/reflex-dev/reflex/pull/2796

Thanks! Pue looks cool, thanks for sharing. I see some similarities to https://reflex.dev in terms of providing a declarative dom expression language with built-in conditionals and loop primitives.

In the dynamic world of web development, Python has emerged as a dominant force, especially in backend development – the primary focus of this blog post. Although it's worth mentioning that there are ongoing efforts to use Python for the frontend as well, like Reflex (previously known as Pynecone, they presumably had to change their name because of Pinecone vector database), which even garnered support from Y Combinator. Samuel Colvin (creator of Pydantic) is also working on FastUI (he literally just released the first version in December 2023).

They have a ready to use LLM chat App, which makes it more likely I will check it out.

https://github.com/reflex-dev/reflex

We will use Reflex to build this chatbot.

To learn more about Reflex, you can read here: https://reflex.dev/

Reflex is an open-source, full-stack Python framework that makes it easy to build and deploy web apps in minutes. You have most of the features of a frontend library like Reactjs and a backend framework like Django in one with ease in development and deployment. All while developing in a single language PYTHON.

Repo : https://github.com/reflex-dev/reflex