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Showing 1519 open source projects for "gmail source code"
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1
ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
ChatGPT-Plugins-Collection is a community-driven repository that gathers examples and resources for building, testing, and experimenting with ChatGPT plugins. The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. By centralizing community contributions, the repository... -
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Intelligent Java
Integrate with the latest language models, image generation and speech
Intelligent java (IntelliJava) is the ultimate tool to integrate with the latest language models and deep learning frameworks using java. The library provides an intuitive functions for sending input to models like ChatGPT and DALL·E, and receiving generated text, speech or images. With just a few lines of code, you can easily access the power of cutting-edge AI models to enhance your projects. Access ChatGPT, GPT3 to generate text and DALL·E to generate images. OpenAI is preferred for... -
3
VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems.... -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
lm-human-preferences is the official OpenAI codebase that implements the method from the paper Fine-Tuning Language Models from Human Preferences. Its purpose is to show how to align language models with human judgments by training a reward model from human comparisons and then fine-tuning a policy model using that reward signal. The repository includes scripts to train the reward model (learning to rank or score pairs of outputs), and to fine-tune a policy (a language model) with... -
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5
chatgpt HTML
PHP version calls the OpenAI interface for question and answer
...The PHP version calls the OpenAI interface for question and answer, uses Stream flow mode communication, and produces while exporting. EventSource is used at the front end to support Markdown format analysis, and formula display, the code is colored. The UI on the page is concise and supports continuous conversations in the context. The source code has only a few files, no frame is used, all PHP versions are supported, all open source, and it is extremely easy to open. Nanny-level tutorials, all surrounding resources, welcome to communicate, everything is free. There is no requirement for the PHP version, no database is required. ... -
6
Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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Grida Assistant
Bring your Figma design & development pipeline to the next level
Bring your Figma design & development pipeline to the next level - with design-to-code, in-design-content-management, component management, and tools for faster design. -
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artikelschreiber
Frontend and Backend Code for ArtikelSchreiber.com and UNAIQUE.NET
...All rights reserved. Frontend and Backend Source Code for Project: https://github.com/sebastianenger1981/ https://www.artikelschreiber.com/ https://www.artikelschreiben.com/ https://www.unaique.net/ https://www.artikelschreiber.com/opensource/ https://www.unaique.com/ -
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chatgpt-web
Privatized web program based on ChatGPT3.5 API
This project can deploy your own customized ChatGPT web program (compatible with gpt3.5) with one click, just download the project file of the corresponding platform in the release, modify the configuration and execute it, open http://127.0.0.1:8080, you can Get your own ChatGPT website. Note that each parameter may affect you to get a different chat effect, and you may get another answer if you change a parameter, so please try to debug it yourself, and don't complain about the artificial... -
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10
NOW
No-code tool for creating a neural search solution in minutes
One line to host them all. Bootstrap your multimodal search case in minutes. NOW gives the world access to multimodal neural search with just one command. NOW supports various formats for uploading your dataset to your search application. You may either choose a demo dataset hosted by NOW, or use your own custom dataset, to build an application. NOW can support your custom data in the form of a DocumentArray, as a path to a local folder, or S3 bucket. You can choose a demo dataset to get... -
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OpenAI Web Application
A web application that allows users to interact with OpenAI's models
A web application that allows users to interact with OpenAI's modles through a simple and user-friendly interface. This app is for demo purpose to test OpenAI API and may contain issues/bugs. User-friendly interface for making requests to the OpenAI API. Responses are displayed in a chat-like format. Select Models (Davinci, Codex, DALL·E, Whisper) based on your needs. Create AI Images (DALL·E). Audio-Text Transcribe (Whisper). Highlight code syntax. Type in the input field and press enter or... -
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FFCV
Fast Forward Computer Vision (and other ML workloads!)
ffcv is a drop-in data loading system that dramatically increases data throughput in model training. From gridding to benchmarking to fast research iteration, there are many reasons to want faster model training. Below we present premade codebases for training on ImageNet and CIFAR, including both (a) extensible codebases and (b) numerous premade training configurations. -
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ConvNeXt V2
Code release for ConvNeXt V2 model
ConvNeXt V2 is an evolution of the ConvNeXt architecture that co-designs convolutional networks alongside self-supervised learning. The V2 version introduces a fully convolutional masked autoencoder (FCMAE) framework where parts of the image are masked and the network reconstructs the missing content, marrying convolutional inductive bias with powerful pretraining. A key innovation is a new Global Response Normalization (GRN) layer added to the ConvNeXt backbone, which enhances feature... -
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CodeContests
Large dataset of coding contests designed for AI and ML model training
CodeContests, developed by Google DeepMind, is a large-scale competitive programming dataset designed for training and evaluating machine learning models on code generation and problem solving. This dataset played a central role in the development of AlphaCode, DeepMind’s model for solving programming problems at a human-competitive level, as published in Science. CodeContests aggregates problems and human-written solutions from multiple programming competition platforms, including AtCoder,... -
15
minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported.... -
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DeepMozart
Audio generation using diffusion models
Audio generation using diffusion models in PyTorch. The code is based on the audio-diffusion-pytorch repository. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large... -
18
Multi-Agent Particle Envs
Code for a multi-agent particle environment used in a paper
Multiagent Particle Environments is a lightweight framework for simulating multi-agent reinforcement learning tasks in a continuous observation space with discrete action settings. It was originally developed by OpenAI and used in the influential paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to... -
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Mintlify Writer
AI powered documentation writer
Writing documentation sucks. Let Mintlify take care of it. Just highlight code and see the magic. -
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CPT
CPT: A Pre-Trained Unbalanced Transformer
A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation. We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. Position Embeddings We extend the max_position_embeddings from 512 to 1024. We... -
21
UnionML
Build and deploy machine learning microservices
...Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. This helps you maintain consistent code across your ML stack, from training to prediction logic. -
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d2l-zh
Chinese-language edition of Dive into Deep Learning
d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
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CleanRL
High-quality single file implementation of Deep Reinforcement Learning
CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should... -
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PerlPP
Perl preprocessor - embed Perl source in any file
Translates Text+Perl to Text. It can be used for any kind of text templating, e.g. code generation. No external modules are required, just a single file. Requires Perl 5.10.1+. PerlPP runs in two passes: it generates a Perl script from your input, and then it runs the generated script. If you see error at (eval ##) (for some number ##), it means there was an error in the generated script. The -D switch defines elements of %D. If you do not specify a value, the value true (a constant in the... -
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Minimal text diffusion
A minimal implementation of diffusion models for text generation
A minimal implementation of diffusion models of text: learns a diffusion model of a given text corpus, allowing to generate text samples from the learned model. The main idea was to retain just enough code to allow training a simple diffusion model and generating samples, remove image-related terms, and make it easier to use. To train a model, run scripts/train.sh. By default, this will train a model on the simple corpus. However, you can change this to any text file using the --train_data...
