aitextgen
alpaca.cpp
aitextgen | alpaca.cpp | |
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19 | 94 | |
1,828 | 9,878 | |
- | - | |
1.8 | 9.4 | |
10 months ago | about 1 year ago | |
Python | C | |
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!)
alpaca.cpp
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LLaMA Now Goes Faster on CPUs
Where's the 30B-in-6GB claim? ^FGB in your GH link finds [0] which is neither by jart nor by ggerganov but by another user who promptly gets told to look at [1] where Justine denies that claim.
[0] https://github.com/antimatter15/alpaca.cpp/issues/182
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Is there potential to short NVDA?
You can just download the language model, dude!!! Everyone doesn’t need to make their own and the open source models literally get better every day.
- [Oobabooga] Alpaca.cpp est extrêmement simple à travailler.
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Hollywood’s Screenwriters Are Right to Fear AI
Alpaca
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Square Enix’s AI Tech Demo Is a Staggering Failure
Square could have also trained a more specific data source for their NLP, very similar to Alpaca. Alpaca was trained from interactions from a larger dataset. So while it isn't as smart, it's still able to understand instructions and act upon them.
- [Singularity] Ich bin Alpaka 13B - Frag mich alles
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Alpaca Vs. Final Jeopardy
The model I found was in 8 parts. The alpaca.cpp chat client (chat.cpp) needs to be modified to run the 8 part model, documented here: https://github.com/antimatter15/alpaca.cpp/issues/149
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LocalAI: OpenAI compatible API to run LLM models locally on consumer grade hardware!
try the instructions on this github repo https://github.com/antimatter15/alpaca.cpp, its not the best one but I was able to run this model on my linux machine with 16GB memory, I think its a good starting point.
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What educational materials do you think would be most useful during/after collapse?
Doesn't run offline. If you're running something without a beefy-ish GPU, there's https://github.com/antimatter15/alpaca.cpp .
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ChatGPT Reignited My Passion For Coding
Ye, atm. toying with alpaca 7B/13B in a local install.
What are some alternatives?
lm-evaluation-harness - A framework for few-shot evaluation of language models.
gpt4all - gpt4all: run open-source LLMs anywhere
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)
llama.cpp - LLM inference in C/C++
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
coral-pi-rest-server - Perform inferencing of tensorflow-lite models on an RPi with acceleration from Coral USB stick
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
ggml - Tensor library for machine learning
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
trump_gpt2_bot - aitextgen (aka GPT-2) Twitter bot
alpaca-lora - Instruct-tune LLaMA on consumer hardware