gpt-neo VS simpler-state

By December 2020, EleutherAI had introduced The Pile, a comprehensive text dataset designed for training models. Subsequently, tech giants such as Microsoft, Meta, and Google used this dataset for training their models. In March 2021, they revealed GPT-Neo, an open-source model under Apache 2.0 license, which was unmatched in size at its launch. EleutherAI’s later projects include the release of GPT-J, a 6 billion parameter model, and GPT-NeoX, a 20 billion parameter model, unveiled in February 2022. Their work demonstrates the viability of high-quality open-source AI models.

Yeah, if that is your requirement you should definitely ignore chatterbot, as its older and probably not what your teacher wants. I'm looking at the gpt-neo docs right now: https://github.com/EleutherAI/gpt-neo

3.) EleutherAI's GPT-Neo and GPT-NeoX: EleutherAI is an independent research organization that aims to promote open research in artificial intelligence. They have released GPT-Neo, an open-source language model based on the GPT architecture, and are developing GPT-NeoX, a highly-scalable GPT-like model. You can find more information on their GitHub repositories: GPT-Neo: https://github.com/EleutherAI/gpt-neo GPT-NeoX: https://github.com/EleutherAI/gpt-neox

This is one of the first comprehensive plugins that has been rewritten to support multiple AI backends such as OpenAI GPT3+ and other custom sources in the future such as ChatGPT, GPT-J, GPT-neo and more.

GPT-Neo is newer than GPT-2 on the open source side of things. In my experience, it tends to give longer and more creative responses than GPT-2 but not on the level of GPT-3. I've not tried GPT-J or GPT-NeoX, but they're also open source and reportedly better than GPT-Neo (albeit less accessible).

Finally, yes-- it is trained via masked language modeling (text prediction). The approach has been fairly standard for years- the big difference with the GPT* models is the number of paramaters and volume of text-- we still haven't reached a ceiling with LLM parameters- they appear to keep improving with size. This training allows the model to learn a strong representation of language. Their training approach is published and open-source GPT* versions have already been made and released (https://github.com/EleutherAI/gpt-neo). However, the models are huge and can't be run locally for hobbyists. This gets at larger issues in democratization of ML.

- GPT-Neo 2.7B, released Mar/2021, and unmaintained/unsupported as of Aug/2021? or;

The link go to a 404. There is the correct link : https://github.com/arnelenero/simpler-state

Would that be the transform function from here [1]?

I do agree too that this definitely should be a library on its own.

Anyway thanks for your work; I'll definitely check SimpleR in my project.

[1] https://github.com/arnelenero/simpler-state/blob/f6336817c9e...

Show HN: SimpleR State – simplest state management for React\ (18 comments)

Hi. There are LOTS of much more popular libs you can choose from. My library does not intend to compete as the "best" library, but it has a very specific set of goals, the topmost of which is simplicity through a minimalist API.

Everyone has different priorities when choosing a library, so I suggest going through the design goals that I highlighted in the README file, or complete documentation here:

https://simpler-state.js.org

and see if it fits what you're specifically looking for in a library.

At the end of the day, all these libraries are just React code. In that sense they differ in terms of patterns/principles behind their implementation, but maybe more importantly, in the API/syntax (which is what immediately matters to the developer using it). This is what I can confidently say that SimpleR State delivers... one of the simplest APIs you can find.

I opened a Request For Comments (here) on GitHub. Your comments and suggestions would be greatly appreciated. Feel free to drop me a comment here 👇 too.