ChatGPT helps you get answers, find inspiration and be more productive. It is free to use and easy to try. Just ask and ChatGPT can help with writing, learning, brainstorming and more.

With ChatGPT, you can type or start a real-time voice conversation by tapping the soundwave icon in the mobile app. Click the web search icon to get fast, timely answers with links to relevant web sources. With canvas, you can work with ChatGPT on …

LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the GPT-2 and GPT-3 miniseries, along with a parallel PyTorch reference implementation in train_gpt2.py. You'll recognize this file as a slightly tweaked nanoGPT, an earlier project of mine. Currently ...

gpt.c is a simple C implementation of OpenAI's popular GPT-2 model. This project was originally inspired by Andrej Karpathy's llm.c. However, llm.c has evolved into a great, but complex and slightly confusing, codebase over time.

Nov 30, 2022 · We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

This program is a dependency-free implementation of GPT-2. It loads the weight matrix and BPE file out of the original TensorFlow files, tokenizes the input with a simple byte-pair encoder, implements a basic linear algebra package with matrix math operations, defines the transformer architecture, performs transformer inference, and un ...

GPT‑4-assisted safety research GPT‑4’s advanced reasoning and instruction-following capabilities expedited our safety work. We used GPT‑4 to help create training data for model fine-tuning and iterate on classifiers across training, evaluations, and monitoring.

Mar 7, 2025 · To this end, we propose a Generative PrompT Compression (GPT-C) paradigm. In the absence of appropriate datasets, we design a Collaborative Ordered Agent (Co-Agent) framework to distill high-quality datasets. We then develop a task-agnostic, length-adaptive reinforcement learning strategy that controls compression length and enhances the ...

Apr 28, 2024 · llm.c takes a simpler approach by implementing the neural network training algorithm for GPT-2 directly. The result is highly focused and surprisingly short: about a thousand lines of C in a...

Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.[18]There were three main types of early GP.