aitextgen
dalai
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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!)
dalai
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Ask HN: What are the capabilities of consumer grade hardware to work with LLMs?
I agree, I've definitely seen way more information about running image synthesis models like Stable Diffusion locally than I have LLMs. It's counterintuitive to me that Stable Diffusion takes less RAM than an LLM, especially considering it still needs the word vectors. Goes to show I know nothing.
I guess it comes down to the requirement of a very high end (or multiple) GPU that makes it impractical for most vs just running it in Colab or something.
Tho there are some efforts:
https://github.com/cocktailpeanut/dalai
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Meta to release open-source commercial AI model
If you're just looking to play with something locally for the first time, this is the simplest project I've found and has a simple web UI: https://github.com/cocktailpeanut/dalai
It works for 7B/13B/30B/65B LLaMA and Alpaca (fine-tuned LLaMA which definitely works better). The smaller models at least should run on pretty much any computer.
- How can I run a large language model locally?
- meirl
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FreedomGPT: AI with no censorship
I am not against easy mode options dude, for example I used to run GANs through command line. I replaced them with Upscayl when I found it. Convenience is king after all. Something about this one isn't right though. They are advertising it as a model they built meanwhile their own github show it to be a frontend of LLAMA. Why aren't they honest about it? Why use bots to spam about it? This causes me to not trust the executable they share to 1 to 1 compliation of the source code neither. I would still recommend looking for more decent alternatives. Btw, running it directly isn't that complicated
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Google removes the waitlist on Bard today and will be available in 180 more countries
https://github.com/ggerganov/llama.cpp https://github.com/oobabooga/text-generation-webui https://github.com/mlc-ai/mlc-llm https://github.com/cocktailpeanut/dalai https://github.com/ido-pluto/catai (this is super easy to install but it doesnt provide an api or have integration with langchain)
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ChatGPT Data Breach BreakDown - Why it Should be a Concern for Everyone!
This was easy to get running: https://github.com/cocktailpeanut/dalai with alpaca 13B (on my 16GB or ram)
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A brief history of LLaMA models
I had it running before with Dalai (https://github.com/cocktailpeanut/dalai) but have since moved to using the browser based WebGPU method (https://mlc.ai/web-llm/) which uses Vicuna 7B and is quite good.
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Meet Atom the GPT Assistant, an AI-powered Smart Home Assistant. It's like Google Assistant but with endless possibility of ChatGPT, it's like Siri but with extensibility of Open Source power.
https://github.com/nsarrazin/serge let's you pick which model and runs in a container. For API https://github.com/cocktailpeanut/dalai looks super promising.
- Mercredi Tech - 2023-04-26
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)
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
llama - Inference code for Llama models
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
llama.cpp - LLM inference in C/C++
trump_gpt2_bot - aitextgen (aka GPT-2) Twitter bot
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.