aitextgen
cramming
aitextgen | cramming | |
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19 | 6 | |
1,828 | 1,239 | |
- | - | |
1.8 | 7.3 | |
10 months ago | about 1 month ago | |
Python | Python | |
MIT License | MIT License |
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aitextgen
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Where is the engineering part in "prompt engineer"?
It's literally a wrapper for the ChatGPT API (currently). I have another library for training models from scratch but haven't had time to work on it.
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self-hosted AI?
I'm experimenting with https://github.com/minimaxir/aitextgen for some some simple tasks. It is pretty much a wrapper around gpt2 and gpt neox models.
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How would I go about implementing warmup steps from the Transformers library?
I'm sorry if this is the wrong place to ask, but I wasn't sure where else to turn. Several of us have already opened an issue with AITextGen, but it seems that the maintainer isn't particularly active these days. I'm a fairly proficient developer (self-taught), and I know my way around ML, but I was not formally-educated in deep learning. A lot of Pytorch-Lightning looks like black magic, to me. I suspect that I'm missing an important detail that would be fairly simple for many of you to identify.
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NanoGPT
To train small gpt-like models, there's also aitextgen: https://github.com/minimaxir/aitextgen
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Neuro-sama sings "Take On Me" with her Angelic Voice
It's actually relatively easy to train your own GPT model and there are multiple tools out there that make it almost just plug and play: https://github.com/minimaxir/aitextgen
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Is there a place with all the models indexed?
I've been learning python and for the past few days, I've been playing around with the aitextgen library.
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I built an AI model to auto-generate Dominion cards. Here are the hilariously bad results.
Then I ran that through the ai and got it to spit out cards that looked like that training data. I used aitextgen. So I let it run for like 4 hours and it thinks it has made 10,000 rows of cards. But some of these cards are duplicates to each other or to cards that already exist, or use a card name that already exists in the original game, or have like 20 '|' characters in one row, or have zero '|'. So I run a script to remove all of these cards like that, and I end up with like 2,000-4,500 cards that are "functional".
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Thoughts on GPT3?
If you search this subreddit, you should find lots of discussions about it, as well as alternatives like GPT-J (open source). If you'd like to experiment with GPT-2 for text generation, try https://github.com/minimaxir/aitextgen. It's fun to play with.
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Show HN: Tensorpedia – Using GPT-2 to synthesize Wikipedia articles
Hey HN! I've been lurking for a while now and I've finally created something that I feel is worth sharing.
I've called this project "Tensorpedia." At its core, Tensorpedia takes in a title and utilizes it as a prompt for GPT-2 to synthesize the introductory part of a Wikipedia article. The machine learning stuff is written using a wonderful library called aitextgen [0], using Wikipedia's "Vital Articles" as a data set [1]. The server is written in Node, and it uses Redis as an article cache. If you want to read my article about it (for some reason), you can check it out here [2].
I created this project to get more experience with server technologies. While I wouldn't say it's a complicated application, I learned quite a lot from it.
Additionally, as I was inspired by all of those this-x-doesn't-exist projects from a while back, this project is mostly for fun. As such, I don't know how much practical use it has, but I've generated some pretty hilarious articles from it.
[0] https://github.com/minimaxir/aitextgen
[1] https://en.wikipedia.org/wiki/Wikipedia:Vital_articles/Level...
[2] https://jonahsussman.net/posts/2022-01-this-wiki-dne/
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Downloaded GPT-2, Encode.py, and Train.py not found.
If by downloaded you mean clone the gpt-2 github repo it doesn't come with those scripts. I personally played around with https://github.com/minimaxir/aitextgen which is a simple wrapper around the gpt-2 code, it comes with some very clear usage. (Shout out to minimaxir and everyone else involved in aitextgen for making using gpt-2 easy to use!)
cramming
- [P] Notes on training BERT from scratch on an 8GB consumer GPU
- Cramming the training of a (BERT-type) language model into limited compute
- NanoGPT
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New AI Research from the University of Maryland Investigates Cramming Challenge for Training a Language Model on a Single GPU in One Day
Quick Read: https://www.marktechpost.com/2023/01/03/new-ai-research-from-the-university-of-maryland-investigates-cramming-challenge-for-training-a-language-model-on-a-single-gpu-in-one-day/ Paper: https://arxiv.org/pdf/2212.14034.pdf Github: https://github.com/JonasGeiping/cramming
- Lucas Beyer on Twitter: “How Good of a Bert Can One Get in One Day on One GPU?
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Cramming: Training a Language Model on a Single GPU in One Day - Jonas Geiping and Tom Goldstein University of Maryland 2022
Github: https://github.com/JonasGeiping/cramming
What are some alternatives?
lm-evaluation-harness - A framework for few-shot evaluation of language models.
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
DiscordChatAI-GPT2 - A chat AI discord bot written in python3 using GPT-2, trained on data scraped from every message of my discord server (can be trained on yours too)
askai - Command Line Interface for OpenAi ChatGPT
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
english-lang - The English Programming Language
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
flan-ul2-alpaca
askai - Your simple terminal helper - A CLI integration with OpenAI's GPT3
trump_gpt2_bot - aitextgen (aka GPT-2) Twitter bot
extreme-bert - ExtremeBERT is a toolkit that accelerates the pretraining of customized language models on customized datasets, described in the paper “ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT”.