Search Results for "point"

...It is licensed under Apache-2.0, open­sourced and maintained by Google, meaning it's designed with enterprise-grade practices in mind. Overall, it serves as a practical entry point and reference library for building real-world generative AI systems on Google Cloud.

...This can be done interactively instantiating a client object. This will generate an authentication token that will be used for all subsequent requests. The token will expire after 30 days, at which point the user will be prompted to re-authenticate.

This is a OSS standalone ChatGPT client. It is based on ChatGPT. The client works almost just like the original ChatGPT websites but it includes some additional features. I wanted to use ChatGPT but I didn't want to pay a fixed price if I have days where I barely use it. So I created this client that almost works like the original. The 20 dollar price tag on ChatGPT is a bit steep for me. I don't want to pay for a service I don't use. I also don't want to pay for a service that I use only a...

...As with many reverse-engineered clients, it depended on upstream behavior that could change, and the project’s lifecycle reflected that reality. The repository was later archived, meaning it is read-only and no longer actively maintained, but it remains a reference point for how developers approached unofficial access to Bing Chat. In essence, EdgeGPT served as a bridge between an interactive chat

point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second diffusion model converts that image into a 3D point cloud. ...

...This is a program for industrial creation of pseudo-unique content. Used, for example, when registering a site in multiple directories. So that in each directory the site is described by text that is unique from the point of view of search engines. Unlike similar tools (synonymizers, dorgens), it allows you to maximize the readability of the resulting texts.

This repository contains a new generative model of chatbot based on seq2seq modeling. The trained model available here used a small dataset composed of ~8K pairs of context (the last two utterances of the dialogue up to the current point) and respective response. The data were collected from dialogues of English courses online. This trained model can be fine-tuned using a closed-domain dataset to real-world applications. The canonical seq2seq model became popular in neural machine translation, a task that has different prior probability distributions for the words belonging to the input and output sequences since the input and output utterances are written in different languages. ...