gpt-neo VS mesh-transformer-jax

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;

GPT-J is a LLM case study with two goals: Training a LLM with a data source containing unique material, and using the training frameworkMesh Transformer JAX to achieve a high training efficiency through parallelization. There is no research paper about GPT-J, but on its GitHub pages, the model, different checkpoints, and the complete source code for training is given.

This idea has already been proposed in ViT-22B and GPT-J-6B.

Sure. Here's the repo I used for the fine-tuning: https://github.com/kingoflolz/mesh-transformer-jax. I used 5 epochs, and appart from that I kept the default parameters in the repo.

You can skip to step 4 using something like GPT-J as far as I understand: https://github.com/kingoflolz/mesh-transformer-jax#links

The pretrained model is already available.

The Github repo suggests it's possible you can change the number of checkpoints to make it run on a GPU.

GPT-J, which uses mesh-transformer-jax: https://github.com/kingoflolz/mesh-transformer-jax

They hinted at it in the screenshot, but the goods are linked from the https://6b.eleuther.ai page: https://github.com/kingoflolz/mesh-transformer-jax#gpt-j-6b (Apache 2)