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Showing 1407 open source projects for "delphi code source"
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OpenCommit
AI tool that automatically generates meaningful Git commit messages
OpenCommit is an open source developer tool that automatically generates meaningful Git commit messages using large language models. It analyzes the staged changes in a repository and produces a descriptive commit message in seconds, helping developers avoid vague messages such as “fix” or “update.” OpenCommit can be used as a command-line utility that replaces the traditional Git commit command, allowing developers to commit changes with a generated message instantly. -
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ReactAgent
The open-source React.js Autonomous LLM Agent
React-Agent is a framework for integrating AI-driven agents into React applications. It provides an intuitive way to build interactive UI components powered by AI models, enabling dynamic and intelligent user interfaces. -
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AutoGPT-Next-Web
Assemble, configure, and deploy autonomous AI Agents in your browser
One-Click to deploy well-designed AutoGPT-Next-Web web UI on Vercel. -
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ChatLLM Web
Chat with LLM like Vicuna totally in your browser with WebGPU
Chat with LLM like Vicuna totally in your browser with WebGPU, safely, privately, and with no server. Powered By web-llm. To use this app, you need a browser that supports WebGPU, such as Chrome 113 or Chrome Canary. Chrome versions ≤ 112 are not supported. You will need a GPU with about 6.4GB of memory. If your GPU has less memory, the app will still run, but the response time will be slower. The first time you use the app, you will need to download the model. For the Vicuna-7b model that... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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Codiga VS Code
VS Code plugin that suggests code blocks as you type and check
VS Code plugin that suggests code blocks as you type and check for errors. Works for JavaScript, TypeScript, Python, Java, Scala, Ruby, PHP, Apex, Docker. -
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onepoint
Assistant tool that integrates coding, writing, and reading functions
Onepoint is an open-source AI assistant based on Electron, designed to create the ultimate desktop productivity tool. Its initial goal was to develop a smart floating window similar to Apple's intelligent assistant that does not take up desktop space or system performance and can be quickly accessed through global hotkeys for user convenience. With ChatGPT technology, users can continuously train onepoint to generate and reconstruct content with greater accuracy (onpoint), thereby improving... -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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LLaMA.go
llama.go is like llama.cpp in pure Golang
llama.go is like llama.cpp in pure Golang. The code of the project is based on the legendary ggml.cpp framework of Georgi Gerganov written in C++ with the same attitude to performance and elegance. Both models store FP32 weights, so you'll needs at least 32Gb of RAM (not VRAM or GPU RAM) for LLaMA-7B. Double to 64Gb for LLaMA-13B. -
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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... -
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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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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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Bot on Anything
Large model-based chatbot builder that can quickly integrate AI models
Bot on Anything is a versatile open-source AI chatbot builder that lets developers connect large language models such as ChatGPT, Claude, and Gemini to virtually any messaging platform, website, or interface with minimal configuration. At its heart, the project abstracts away the glue logic between AI model APIs and disparate application “channels,” enabling the same bot logic to run in Slack, Telegram, Gmail, enterprise tools, web UIs, or command-line terminals. -
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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. ... -
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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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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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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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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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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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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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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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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...