Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 42
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Showing 1509 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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DataHub is the leading open-source data catalog helping teams discover, understand, and govern their data assets.Built on an open source foundation with a thriving community of 13,000+ members, DataHub gives you unmatched flexibility to customize and extend without vendor lock-in. DataHub Cloud is a modern metadata platform with REST and GraphQL APIs that optimize performance for complex queries, essential for AI-ready data management and ML lifecycle support.
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Stigg | SaaS Monetization and Entitlements APIA monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
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1
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. -
2
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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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... -
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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. -
Create custom docs, forms, apps, e-signatures, and surveys with Titan.Create custom docs, forms, apps, e-signatures, and surveys with Titan’s full-suite of enterprise applications designed to integrate seamlessly with Salesforce data across your entire organization. #1 on the Salesforce appexchange
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5
AI-Agent-Host
The AI Agent Host is a module-based development environment.
...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 Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. ... -
6
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... -
7
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 at once. ... -
8
Cheetah
AI macOS app for real-time coding interview coaching assistance
...It then leverages language models to generate suggested answers, refinements, or explanations tailored to the ongoing discussion. Cheetah also connects with live coding environments through a browser extension, allowing it to analyze code and logs directly from supported platforms. Cheetah provides simple controls for generating answers, refining responses, and analyzing technical output, making it interactive during mock interviews. -
9
Intelligent Java
Integrate with the latest language models, image generation and speech
...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 quality results without tuning. Generate text; Cohere allows you to generate a language model to suit your specific needs. Generate audio from text; Access DeepMind’s speech models. ... -
Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
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10
picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
...It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. Because the code is intentionally lightweight, it is often used as a teaching resource for students learning about natural language processing and deep learning architectures. Developers can explore the repository to understand how language models generate text and how transformer components interact within the architecture. -
11
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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ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
...By centralizing community contributions, the repository highlights practical applications of plugins across domains such as productivity, data access, and automation. The project also serves as a starting point for developers interested in building their own custom plugins, offering inspiration and code samples. With its open structure, it encourages collaboration and knowledge sharing in the growing ecosystem of ChatGPT extensions. -
13
LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
...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 reinforcement learning (or related techniques) guided by that reward model. The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). It includes utilities for launching experiments, sampling from policies, and simple experiment orchestration. -
14
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.... -
15
Bot on Anything
Large model-based chatbot builder that can quickly integrate AI models
...Configuration is handled simply through a central JSON file where you define which model and which application channel you want to glue together, so developers can create sophisticated AI assistants without rewriting integration code from scratch. The architecture emphasizes reusability and extensibility, allowing the addition of new model backends or new channels with relative ease. It supports switching between multiple AI models and targets within the same project. -
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...Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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17
Snipe Chan Discord Bot
Snipe Chan is a Discord Bot that snipes deleted/edited messages
What is Snipe Chan? Snipe Chan is a Discord Bot that snipes messages, you will have to run your own snipe bot with the code provided because of the limitations of Discord TOS. Features - Snipe messages - Snipe files - Sniped cache -Editable settings in config.yml -[prefix]help - help menu -[prefix]snipe - shows the latest snipe -[prefix]sniped [index] - shows a snipe in sniped cache - [prefix]snipelist [index | nothing] - shows interactive snipe list (Prev | Next | Hide | Remove) ---[Prev] - browse previous snipe --- [Next] - browse next snipe --- [Hide List] - hide embed --- [Remove Snipe] - remove from cache - [prefix]remove [index] - removes index from cache - [prefix]clear - clears cache - [prefix]version - check for newer versions -
18
chatgpt HTML
PHP version calls the OpenAI interface for question and answer
...The core code has only a few files, and no frame is used. It is convenient to modify the debugging. It only needs to modify the API_KEY in stream.php to use it. -
19
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... -
20
Alpa
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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Understand Prompt
Structured exploration and summary of prompt engineering practices
Understand Prompt is a repository by Phodal Huang that serves as a structured exploration and summary of prompt engineering practices across coding, art, and writing contexts, especially in the era of AI models like StableDiffusion and ChatGPT. It’s part tutorial, part reflection: the author writes about how prompts work (for image generation, article generation, code auto-generation) and shares notebooks, examples, and insights into how to design effective prompts, iterate them, and integrate them into workflows. The material is both conceptual (why prompts matter, how to think of them) and practical (code notebooks, examples). It aims to elevate prompts from ad-hoc text inputs to a disciplined part of the toolchain, helping developers/artists/writers get more reliable results from AI models. ... -
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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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OpenAI Web Application
A web application that allows users to interact with OpenAI's models
...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 click on the send button to make a request to the OpenAI API. Use control+enter to add line breaks in the input field. Responses are displayed in the chat-like format on top of the page. Generate code, including translating natural language to code. Take advantage of DALL·E models to generate AI images. ... -
24
AICommand
ChatGPT integration with Unity Editor
AICommand is a proof-of-concept integration that lets you control the Unity Editor using natural language via ChatGPT. Instead of manually hunting through menus or writing editor scripts, you can prompt the editor to perform tasks, generate snippets, and automate actions. The project showcases an emerging workflow where LLMs augment game and tooling development by understanding intent and producing editor-side outcomes. It provides a minimal setup that connects your OpenAI API key and... -
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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.
