Search Results for "example" - Page 9
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Showing 512 open source projects for "example"
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Plugins Quickstart
Get a ChatGPT plugin up and running in under 5 minutes
plugins-quickstart is a starter project created by OpenAI to help developers build and deploy ChatGPT plugins quickly. It provides a minimal but complete example of how to structure a plugin, implement an API, and define the necessary configuration files. The repository demonstrates how a plugin can be served, authenticated, and integrated with ChatGPT for real-world use. By including both the backend code and plugin manifest, it guides developers through the end-to-end development workflow. ... -
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clockwize-timer
Smart timer to get you notified
...A good area of use is on student computers in elementary school, high school or other post-secondary education. For people who want to keep track of time on their own instead of, for example, timers in the classroom or at home. Developed in Python by Sigge Arkestål, a 5:th grade student from Sweden. -
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GPT Discord Bot
Example Discord bot written in Python that uses the completions API
GPT Discord Bot is an example project from OpenAI that shows how to integrate the OpenAI API with Discord using Python. The bot uses the Chat Completions API (defaulting to gpt-3.5-turbo) to carry out conversational interactions and the Moderations API to filter user messages. It is built on top of the discord.py framework and the OpenAI Python library, providing a simple, extensible template for building AI-powered Discord applications. -
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nb2mail
Send a notebook as an email
This repo contains a jupyter nbconvert exporter to convert notebooks to multipart MIME, and a postprocessor to send it via smtp. nb2mail does not do anything by itself. It provides an export format ("mail") and postprocessor ("SendMailPostProcessor"). Please see the nbconvert documentation and example configuration for more information. -
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Graphtage
A semantic diff utility and library for tree-like files such as JSON
...Its name is a portmanteau of “graph” and “graftage”, the latter being the horticultural practice of joining two trees together such that they grow as one. Graphtage performs an analysis on an intermediate representation of the trees that is divorced from the filetypes of the input files. This means, for example, that you can diff a JSON file against a YAML file. Also, the output format can be different from the input format(s). By default, Graphtage will format the output diff in the same file format as the first input file. But one could, for example, diff two JSON files and format the output in YAML. There are several command-line arguments to specify these transformations, such as --format; please check the --help output for more information. -
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DeepSeek MoE
Towards Ultimate Expert Specialization in Mixture-of-Experts Language
DeepSeek-MoE (“DeepSeek MoE”) is the DeepSeek open implementation of a Mixture-of-Experts (MoE) model architecture meant to increase parameter efficiency by activating only a subset of “expert” submodules per input. The repository introduces fine-grained expert segmentation and shared expert isolation to improve specialization while controlling compute cost. For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B or LLaMA2 7B using about 40% of the total compute. The repo publishes both Base and Chat variants of the 16B MoE model (deepseek-moe-16b) and provides evaluation results across benchmarks. It also includes a quick start with inference instructions (using Hugging Face Transformers) and guidance on fine-tuning (DeepSpeed, hyperparameters, quantization). ... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
...Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD. -
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YYeTsBot
Renren Film and Television bot, fully connected to Renren resources
...When searching for resources, it will be searched according to my predetermined priority (everyone video offline, subtitle man), of course, you can also use commands to force a subtitle group. Due to the difference in translations, it is recommended to enter a partial translation and then select from the list. For example, if you want to watch the fourth season of Game of Thrones, just search for "Game of Thrones". Want to keep a resource for yourself, but don't know how to program? It doesn't matter! There are currently two methods available, please choose according to your own situation. -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
Mixtral-Offloading is an open-source project designed to enable efficient inference of large Mixture-of-Experts language models such as Mixtral-8x7B on hardware with limited GPU memory. The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model. This approach takes advantage of the sparse activation properties of mixture-of-experts... -
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Alpaca-CoT
We unified the interfaces of instruction-tuning data
Alpaca-CoT is an open research project focused on improving reasoning capabilities in language models through chain-of-thought training data. The project builds upon the Alpaca instruction-tuning approach by introducing datasets and methods that encourage models to produce intermediate reasoning steps when solving problems. Instead of generating answers directly, the model learns to produce logical reasoning sequences that lead to the final solution. This chain-of-thought supervision helps... -
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ProPainter
Improving Propagation and Transformer for Video Inpainting
...It is designed to remove objects, complete missing regions, and fill masked areas in videos while preserving temporal consistency. The project accepts video input or split frames along with frame-wise masks that define the areas to reconstruct. It provides pretrained models, example inputs, inference scripts, and an interactive demo workflow for object removal. The repository also includes memory-efficient inference features to reduce GPU out-of-memory issues during video processing. ProPainter is useful for research, video editing experiments, object removal prototypes, and completion tasks where consistent motion is important. -
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DeepSearch5Plus
Search recursively all files, text inside files, and bookmarks
...You can launch the program associate with the file or open it in its container folder. For audio/video/text files you can configure for each of them a program to use and configure options to pass to the program. For example you could use, for example, Notepad++ and pass in its option "-n<number>" to open at certain row. -
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CPAT
RNA coding potential assessment tool
...While still very useful, these approaches have several limitations: Most lncRNAs are less conserved and tend to be lineage specific which greatly limit the discrimination power of alignment-based methods. For example, of 550 lncRNAs detected from zebrafish, only 29 of them had detectable sequence -
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EvaDB
Database system for building simpler and faster AI-powered application
...This programming complexity prevents people who are experts in other domains from benefiting from these models. Running these deep learning models on large document or video datasets is costly and time-consuming. For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. Besides the money spent on hardware, these models also increase the time that you spend waiting for the model inference to finish. -
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Parallel WaveGAN
Unofficial Parallel WaveGAN
...It includes a large collection of “Kaldi-style” recipes for many datasets such as LJSpeech, LibriTTS, VCTK, JSUT, CMU Arctic, and multiple singing voice corpora in Japanese, Mandarin, Korean, and more. The project provides pre-trained models, Colab demos, and example configurations, allowing researchers to quickly evaluate vocoder quality or adapt models to new datasets. -
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ConsistencyDecoder
Consistency Distilled Diff VAE
ConsistencyDecoder is a Python package from OpenAI that introduces an improved decoding method for variational autoencoders (VAEs) used in Stable Diffusion pipelines. Instead of relying solely on the standard GAN or VAE decoder, this approach leverages a Consistency Distilled Diff VAE, designed to produce higher-quality and more stable outputs from encoded latents. The project provides a simple API for encoding with a Stable Diffusion VAE and decoding using the new consistency model,... -
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odd-collector
Open-source metadata collector based on ODD Specification
ODD Collector is a lightweight service that gathers metadata from all your data sources. Push-client is a provider which sends information directly to the central repository of the Platform. ODDRN (Open Data Discovery Resource Name) is a unique resource name that identifies entities such as data sources, data entities, dataset fields etc. It is used to build lineage and update metadata. -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
...The example trains an SDE as the generator of a GAN, whilst using a neural CDE [4] as the discriminator. -
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ReplitLM
Inference code and configs for the ReplitLM model family
ReplitLM is a family of open-source language models developed by Replit for assisting with programming tasks such as code generation and completion. The project includes model checkpoints, configuration files, and example code that enable developers to run and experiment with the models locally or within machine learning frameworks. These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development environments. The repository also includes documentation and tutorials for integrating the models into development tools, APIs, or research pipelines. ... -
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llama2-webui
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere
Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac). -
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CostPal
CostPal is your personal finance manager
CostPal is an application for managing your expenses, incomes and other events. CostPal is free, open-source and written in python. The current version supports only Windows, comes as portable package and was tested on Windows7 (64bit) and WindowsXP (32bit). This application is distributed under GNU GPL version 2 license. CostPal Mobile is an additional application for your smartphone to interoperate with CostPal on your PC (currently available only for Android OS). With it's smooth... -
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LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
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LLaMA
Inference code for Llama models
...It provides utilities to load pre-trained LLaMA model weights, run inference (text generation, chat, completions), and work with tokenizers. Tokenizer utilities, download scripts, shell helpers to fetch model weights with correct licensing/permissions. Includes example scripts for chat completions and text completions to show how to call the models in code. This repo is a core piece of the Llama model infrastructure, used by researchers and developers to run LLaMA models locally or in their infrastructure. It is meant for inference (not training from scratch) and connects with aspects like model cards, responsible use, licensing, etc. -
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OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
...While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence-to-sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings. Sequence to sequence models can be trained with guided alignment and alignment information are returned as part of the translation API. -
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Language Models
Explore large language models in 512MB of RAM
...It is particularly useful for educational purposes, as it demonstrates the fundamental mechanics of language model inference and prompt-based applications. The repository includes multiple example applications such as chatbots, document question answering systems, and information retrieval tools.
