gpt4free VS evals

I cant install

However, the translator used at that time was a third-party Linux package, and the translation quality was as poor as Google Translate. With the emergence of ChatGPT, the author thought of delegating the translation task of this project to GPT. However, due to OpenAI not being free, this idea was never implemented. Recently, I stumbled upon an open-source project called gpt4free, which essentially allows you to use gpt's API for free. It's truly remarkable... Using the open-source project gpt4free, I immediately modified the functionality of action-translate-readme from before.

answer: ChatGPT Plus gets you the access to GPT4, much more powerful than the free GPT3.5. Also you get access to powerful plugins that can run code and interact with websites. Bing Chat is also GPT4 and with a bit of computer knowledge you can get access to the raw model for free: https://github.com/xtekky/gpt4free

You could take a look here (reverse engineering freely available chatgpt web apps apis) https://github.com/xtekky/gpt4free

They are not. They were used in GPT4Free project, but the load generated by the influx of users was so high the website authors reached xtekky and asked to drop Ora as a backend for the project. More info here: https://github.com/xtekky/gpt4free/issues/125

The site is down atmo, but gpt4free have made this site that uses API's from demo sites to reach gpt4 anyway. https://chat.g4f.ai/chat/ https://github.com/xtekky/gpt4free

lien

We had gpt4Free keys but they shutdown due to legal reason.

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