awesome-golem VS gpt-neo

If you want to run scientific calculations or similar, I highly recommend Golem. Right now, its best applications are ones that can scale by sharding, to use parallel computations. Think doing 100 similar small jobs on 100 computers instead of 1 large job on 1 computer. One average CPU-month costs $3.17, or you can rent 100 CPU-hours for $0.44. Notable examples are blender_cuda which runs on a GPU, and the entirety of awesome-golem.

Adding the repository to the awesome-golem repository. Doing this makes it possible to scout a neat and organized list of projects, without having to do much digging. There are categories that help go through the list quickly, for those that are looking for something specific.

There are lots of use cases on Golem. Many of which were created by the community. You can keep track of them relatively easily on Awesome Golem (all the applications on that list were created within the last year, for the latest implementation, Yagna).

See everything you can do with Golem: https://github.com/golemfactory/awesome-golem

Golem as a project is larger than it's ever been, there's an awesome list of community created apps and tools, the network has more nodes this year than it has before (stats), there's been over 300k GNT / GLM given out to the community in the GLM Rewards Program, not including bounties and hackathons in the last year (48,000 GLM + 83,180 USDC).

The Golem ecosystem is growing steadily (here’s our Awesome list in case you missed it, recently listed on the overall Awesome repository with over 160k stars!)

There are actually already quite a few things you can do with Golem already before Thorg. It's part of the reason why Awesome Golem, containing all the useful stuffs is already listed on the overall Awesome repo which is the 8th highest starred repo on GitHub.

It's also much easier to build applications on the new network as a potential requestor. We have a lot of community-created apps that people can run and/or learn from. There are also tools that are community-created which never existed around Clay Golem. You can find most of them listed here on Awesome Golem.

Awesome-Golem - for pre-existing applications.

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;