nanoGPT
ChatGPT
nanoGPT | ChatGPT | |
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69 | 50 | |
31,914 | 46,892 | |
- | - | |
5.4 | 6.4 | |
about 1 month ago | 21 days ago | |
Python | Rust | |
MIT License | GNU Affero General Public License v3.0 |
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nanoGPT
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Show HN: Predictive Text Using Only 13KB of JavaScript. No LLM
Nice work! I built something similar years ago and I did compile the probabilities based on a corpus of text (public domain books) in an attempt to produce writing in the style of various authors. The results were actually quite similar to the output of nanoGPT[0]. It was very unoptimized and everything was kept in memory. I also knew nothing about embeddings at the time and only a little about NLP techniques that would certainly have helped. Using a graph database would have probably been better than the datastructure I came up with at the time. You should look into stuff like Datalog, Tries[1], and N-Triples[2] for more inspiration.
You're idea of splitting the probabilities based on whether you're starting the sentence or finishing it is interesting but you might be able to benefit from an approach that creates a "window" of text you can use for lookup, using an LCS[3] algorithm could do that. There's probably a lot of optimization you could do based on the probabilities of different sequences, I think this was the fundamental thing I was exploring in my project.
Seeing this has inspired me further to consider working on that project again at some point.
[0] https://github.com/karpathy/nanoGPT
[1] https://en.wikipedia.org/wiki/Trie
[2] https://en.wikipedia.org/wiki/N-Triples
[3] https://en.wikipedia.org/wiki/Longest_common_subsequence
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LLMs Learn to Be "Generative"
where x1 denotes the 1st token, x2 denotes the 2nd token and so on, respectively.
I understand the conditional terms p(x_n|...) where we use cross-entropy to calculate their losses. However, I'm unsure about the probability of the very first token p(x1). How is it calculated? Is it in some configurations of the training process, or in the model architecture, or in the loss function?
IMHO, if the model doesn't learn p(x1) properly, the entire formula for Bayes' rule cannot be completed, and we can't refer to LLMs as "truly generative". Am I missing something here?
I asked the same question on nanoGPT repo: https://github.com/karpathy/nanoGPT/issues/432, but I haven't found the answer I'm looking for yet. Could someone please enlighten me.
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A simulation of me: fine-tuning an LLM on 240k text messages
This repo, albeit "old" in regards to how much progress there's been in LLMs, has great simple tutorials right there eg. fine-tuning GPT2 with Shakespeare: https://github.com/karpathy/nanoGPT
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Ask HN: Is it feasible to train my own LLM?
For training from scratch, maybe a small model like https://github.com/karpathy/nanoGPT or tinyllama. Perhaps with quantization.
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Writing a C compiler in 500 lines of Python
It does remind me of a project [1] Andrej Karpathy did, writing a neural network and training code in ~600 lines (although networks have easier logic to code than a compiler).
[1] https://github.com/karpathy/nanoGPT
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[D] Can GPT "understand"?
But I'm still not convinced that it can't in theory. Maybe the training set or transformer size I'm using is too small. I'm using nanoGPT implementation (https://github.com/karpathy/nanoGPT) with layers 24, heads 12, and embeddings per head 32. I'm using character-based vocab: every digit is a separate token, +, = and EOL.
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Transformer Attention is off by one
https://github.com/karpathy/nanoGPT/blob/f08abb45bd2285627d1...
At training time, probabilities for the next token are computed for each position, so if we feed in a sequence of n tokens, we basically get n training examples, one for each position, but at inference time, we only compute the next token since we’ve already output the preceding ones.
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Sarah Silverman Sues ChatGPT Creator for Copyright Infringement
And there are a bunch of other efforts at making training more efficient. Here's a cool model by Karpathy (OpenAI/used to head up Tesla's efforts): https://github.com/karpathy/nanoGPT
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Douglas Hofstadter changes his mind on Deep Learning and AI risk
Just being a part of any auto-regressive system does not contradict his statement.
Go look at the GPT training code, here is the exact line: https://github.com/karpathy/nanoGPT/blob/master/train.py#L12...
The model is only trained to predict the next token. The training regime is purely next-token prediction. There is no loopiness whatsoever here, strange or ordinary.
Just because you take that feedforward neural network and wrap it in a loop to feed it its own output does not change the architecture of the neural net itself. The neural network was trained in one direction and runs in one direction. Hofstadter is surprised that such an architecture yields something that looks like intelligence.
He specifically used the correct term "feedforward" to constrast with recurrent neural networks, which GPT is not: https://en.wikipedia.org/wiki/Feedforward_neural_network
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NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.
Does anyone have or know of an example implementation in plain pytorch, not huggingface transformers. Like something you could plug into https://github.com/karpathy/nanoGPT ?
ChatGPT
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What AI assistants are already bundled for Linux?
> I wouldn't hold my breath waiting for a native Linux AI-assisted assistant.
On Mac when I press Command + Space, it brings up Spotlight search
That can't easily be added to be the equivalent of some kind of LLM prompt on GNOME/KDE/XFCE?
I don't quite know what you'd ask it/do with it that would be of much value? Seems like a quicker way/a wrapper around either asking an LLM questions via CLI or basically Electron wrapping HTML (like this https://github.com/lencx/ChatGPT)?
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The Top 10 GitHub Repositories Making Waves 🌊📊
Github | Website
- Is there a website where I can ser all the new and latest ChatGPT plugins anf their features ?
- Has anyone tried these desktop versions of ChatGPT?
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Is there a better option than ShareGPT for getting an entire conversation with formatting?
I am using lencx/ChatGPT app since its launch and I am quite happy with it. it have option to export chat in multiple types... I really like Prompt database feature. Love it.
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make text (chatgpt) wider, now is too narrow
i tried to find the workaround on google but only found the solution using bookmark technique. here is the link. https://github.com/lencx/ChatGPT/issues/430 (mattinordstrom answer). the thing is the solution is using bookmark and javascript. i read the code line and guess the class and property being used but still not working. thank you so much
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How to search all the previous chats (at least by title)?
Is there any way I can search the previous chats? I'm using a desktop app (this one: https://github.com/lencx/ChatGPT) and it does not seem to have this feature as well.
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Are there any github repos that allow you to use the ChatGPT UI as an API?
Also, I keep warning people to not put your API keys into other people's software. This is what you get: https://github.com/lencx/ChatGPT/discussions/849
- ChatGPT Plus vs API debate, how good are the available local chat clients? Does OpenAI's native client do some added magic over the API?
- Chat GPT error after 2-3 min
What are some alternatives?
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
askai - Command Line Interface for OpenAi ChatGPT
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
BetterChatGPT - An amazing UI for OpenAI's ChatGPT (Website + Windows + MacOS + Linux)
PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
chatgpt-raycast - ChatGPT raycast extension
nn-zero-to-hero - Neural Networks: Zero to Hero
PyChatGPT - ⚡️ Python client for the unofficial ChatGPT API with auto token regeneration, conversation tracking, proxy support and more.
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
chat-ai-desktop - Unofficial ChatGPT desktop app for Mac & Windows menubar using Tauri & Rust
aitextgen - A robust Python tool for text-based AI training and generation using GPT-2.
nicegui - Create web-based user interfaces with Python. The nice way.