gpt_index
gpt-2-simple
gpt_index | gpt-2-simple | |
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48 | 13 | |
7,332 | 3,366 | |
- | - | |
9.8 | 0.0 | |
about 1 year ago | over 1 year ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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gpt_index
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Basic links to get started with Prompt Programming
LLAMA Index Github repository
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Leak: Metas GPT-Herausforderer LLaMA als Torrent verfügbar
Zuwendungen kommen auch so langsam ( LLamaIndex ) https://github.com/jerryjliu/gpt_index
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Large language models are having their Stable Diffusion moment
This is exactly what LlamaIndex is meant to solve!
A set of data structures to augment LLM's with your data: https://github.com/jerryjliu/gpt_index
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ChatGPT's API Is So Good and Cheap, It Makes Most Text Generating AI Obsolete
This is what we've designed LlamaIndex for! https://github.com/jerryjliu/gpt_index. Designed to help you "index" over a large doc corpus in different ways for use with LLM prompts.
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Is there a way I can have ChatGPT look at a document of mine?
https://github.com/jerryjliu/gpt_index might be close to what you need.
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AI is making it easier to create more noise, when all I want is good search
I would start with https://gpt-index.readthedocs.io/en/latest/ and https://langchain.readthedocs.io/en/latest/
- GitHub - jerryjliu/gpt_index: LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.
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Using OpenAI with self hosted knowledge database
People have been doing this with https://github.com/jerryjliu/gpt_index
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Long form content
Here is a link to the repository. Take a look at the overview section of the readme. https://github.com/jerryjliu/gpt_index
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LLaMA: A foundational, 65B-parameter large language model
(creator of gpt index / llamaindex here https://github.com/jerryjliu/gpt_index)
Funny that we had just rebranded our tool from GPT Index to LlamaIndex about a week ago to avoid potential trademark issues with OpenAI, and turns out Meta has similar ideas around LLM+llama puns :). Must mean the name is good though!
Also very excited to try plugging in the LLaMa model into LlamaIndex, will report the results.
gpt-2-simple
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Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations
Tap the contact's name in WhatsApp (I think it only works on a phone) and at the bottom of that screen there's Export Chat.
For finetuning GPT-2 I think I used this thing on Google Colab. (My friend ran it on his GPU, it should be doable on most modern-ish GPUs.)
https://github.com/minimaxir/gpt-2-simple
I tried doing something with this a few months ago though and it was a bit of a hassle to get running (needed to use a specific python version for some dependencies...), I forget the details sorry!
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indistinguishable
I mentioned in a different reply that I used https://github.com/minimaxir/gpt-2-simple
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training gpt on your own sources - how does it work? gpt2 v gpt3? and how much does it cost?
You will need a few hundred bucks, python experience, and a simple implementation such as this repo https://github.com/minimaxir/gpt-2-simple
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I (re)trained an AI using the 36 lessons of Vivec, the entirety of C0DA, the communist manifesto and the top posts of /r/copypasta and asked it the most important/unanswered lore questions. What are the lore implications of these insights?
I just used the gpt-2-simple python package and ran it overnight in an jupyter notebook, but you could copy the code to any python compiler and it should also work.
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How do I start a personal project?
I'll note that if you're just doing text generation it is a simple project as far as ML goes, there are some nice libraries you can use that require minimal ML knowlege -eg https://github.com/minimaxir/gpt-2-simple
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I created a twitter account that posts AI generated Canucks related tweets. I call it "Canucks Artificial Insider".
Then, I use the GPT-2 AI libraries, wrapped in a python library GPT-2 Simple to generate the content. My actual code is basically just their code sample, so basically 6 lines of python. With GPT-2, you train the existing AI to your specific dataset, which in my case is this text file of tweets.
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Training GPT-2 with HuggingFace Transformers to sound like a certain author
gpt_2_simple is your best bet! Its super easy to use, you just need to downgrade TensorFlow and some other packages in your environment.
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These Magic cards don't exist - Generating names for new cards using machine learning and GPT-2.
I used the GPT-2 Simple program by minimaxir to train the algorithm on every card in Magic's history that was released in a main expansion. Then I generated about 2,000 (it was actually more, but the algorithm really liked giving me cards that already exist) new names which I searched through to find the best ones.
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No rush, mostly curious (training/finetuned models)
Might I suggest starting Starting here, to learn on Simple GPT2. They have a Google Colab Notebook if your CPU GPU is shit, and what helped me learn best is dissect the code, and basically make my own Colab notebook piece by piece, learning what each function does.
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Selecting good hyper-parameters for fine-tuning a GPT-2 model?
The last couple of months, I've been running a Twitter bot that posts GPT-2-generated content, trained off of Tweets from existing accounts using gpt-2-simple. In my more recent training sessions, it seems like the quality of the output has been decreasing; it often gives outputs that are just barely modified from the original training data, if not verbatim.
What are some alternatives?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
Style-Transfer-in-Text - Paper List for Style Transfer in Text
llama - Inference code for Llama models
textgenrnn - Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better.
ctrl-sum - Resources for the "CTRLsum: Towards Generic Controllable Text Summarization" paper
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
openai-api-py-lite - OpenAI API Python bindings with no dependencies
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
AIdegger - Extended publications of Martin Heidegger uncovered using machine learning.