Search Results for "model-builder" - Page 68
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Showing 2224 open source projects for "model-builder"
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ImPromptu
Domain Agnostic Prompts for Savvy Professionals
A community-driven wiki of sorts full of your favorite prompts for various Large Language Models such as ChatGPT, GPT-3, MidJourney, and soon (Google's Bard) and more! Choose a subject area you are interested in, and click the link below to go to the page with prompts for that subject. If that page is empty, then you can help by adding prompts to that page. If you are not sure how to do that, you can read the contributing guidelines. If you are feeling like having your mind melt into magic... -
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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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Point-E
Point cloud diffusion for 3D model synthesis
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. ... -
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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 computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. ... -
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Riffusion
Real-time music generation using stable diffusion techniques AI
...It includes both developer-oriented tools and user-facing components such as a command-line interface and an interactive Streamlit application for experimentation. Additionally, it can run as a Flask server to expose model inference through an API, enabling integration with other applications or services. -
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TextBox
A text generation library with pre-trained language models github.com
...From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. ... -
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Multi-Agent Particle Envs
Code for a multi-agent particle environment used in a paper
...The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to model cooperative, competitive, and mixed interactions among agents, making it useful for testing algorithms in multi-agent settings. The project includes built-in scenarios such as navigation to landmarks, cooperative tasks, and adversarial setups. Although archived, its concepts and code structure remain foundational for more advanced libraries like PettingZoo, which extended and maintained this environment. -
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WhisperBatchRun
This batch file will run OpenAi's whisper to transcribe (or translate)
Currently, Version #1 of the batch file does the following: (1) checks of whisper is installed, and if so, starts to run; (2) Asks if you want to process sub-folders, and if an answer is not provided in 10 seconds, defaults to "N"; (3) Applies the following command to each mp3 or wav file in the folder/sub-folders: "whisper "FILENAME" --model large-v2 --output_format vtt" (4) Creates a log file in the active directory, but only if there were any errors; (5) ends. To use, simply place it in your system's "PATH." Search online how to do this if need be - i.e. to alter your environment variables. Once in the system PATH you simply open a Command Prompt (CMD) and type the batch file name (without the extension if desired). ... -
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ImageAI
A python library built to empower developers
...ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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Karlo
Text-conditional image generation model based on OpenAI's unCLIP
Karlo is a text-conditional image generation model based on OpenAI's unCLIP architecture with the improvement over the standard super-resolution model from 64px to 256px, recovering high-frequency details only in the small number of denoising steps. We train all components from scratch on 115M image-text pairs including COYO-100M, CC3M, and CC12M. In the case of Prior and Decoder, we use ViT-L/14 provided by OpenAI’s CLIP repository. -
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ChatGPT-bot
Run your own GPTChat Telegram bot, with a single command
Go CLI to fuels a Telegram bot that lets you interact with ChatGPT, a large language model trained by OpenAI. -
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Stable Diffusion
A latent text-to-image diffusion model
Stable Diffusion is a widely used open-source latent text-to-image diffusion model developed by the CompVis group for generating high-quality images from natural language prompts. The model operates by conditioning a diffusion process on text embeddings produced by a CLIP text encoder, enabling detailed and controllable image synthesis. It was trained on large-scale image datasets and later fine-tuned to produce 512×512 images with strong visual fidelity. -
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AI Models
A repository of trained models
All models (at least currently) are supported by chaiNNer, an upscaling GUI that allows for both very simple and very complex tasks to be completed in a nice manner where you "chain" nodes together. Highly recommended for images. If you're looking to upscale videos using the models then use enhancr simply due to the fact that it supports TensorRT, which will allow you to upscale videos at incredible speeds! The GUI is one of the best looking applications out there and is personally my go to... -
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G-Diffuser Bot
Discord bot and Interface for Stable Diffusion
The first release of the all-in-one installer version of G-Diffuser is here. This release no longer requires the installation of WSL or Docker and has a systray icon to keep track of and launch G-Diffuser components. The infinite zoom scripts have been updated with some improvements, notably a new compositer script that is hundreds of times faster than before. The first release of the all-in-one installer is here. It notably features much easier "one-click" installation and updating, as well... -
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2020 Machine Learning Roadmap
A roadmap connecting many of the most important concepts
...The repository organizes machine learning knowledge into a structured roadmap that helps learners understand how different concepts connect within the field. It outlines the typical workflow of solving machine learning problems, starting from problem formulation and data preparation to model training and evaluation. The roadmap also highlights the major technologies and frameworks commonly used in machine learning development. In addition to describing technical tools, the project includes recommended learning resources that help users study the underlying mathematics and algorithms behind machine learning systems. The roadmap is often used as a high-level orientation tool for beginners who want to understand the broader landscape of machine learning. -
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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. -
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LightSeq
A High Performance Library for Sequence Processing and Generation
Lightseq is a high-performance library focused on efficient inference and training for deep learning models, especially large language models (LLMs) and transformer-based architectures. Its goal is to optimize both memory usage and computational throughput, enabling faster training or inference on limited hardware while maintaining model quality. Lightseq provides optimized CUDA kernels, quantization strategies, and runtime optimizations tailored for transformer operations — which often are bottlenecks in conventional frameworks — thereby reducing memory footprint, improving speed, and making deployment of large-scale models more accessible. Because of this, it’s particularly useful for researchers and developers who want to fine-tune or run transformer-based models without requiring top-tier GPUs or massive computational resources. -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
...It can run frame-by-frame or with temporal smoothing, and includes demo apps for live webcam capture as well as batch processing. Outputs include textured meshes, joint locations, and model parameters that can be exported to common DCC tools and game engines. The codebase offers pretrained models, clear inference scripts, and utilities to visualize results, making single-camera motion capture approachable on commodity hardware. Researchers and creators use it for motion studies, AR/VR prototyping, character animation, and human-in-the-loop editing. -
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DialoGPT
Large-scale pretraining for dialogue
DialoGPT is an open-source conversational language model developed by Microsoft Research for generating natural dialogue responses using large-scale transformer architectures. The system is built on the GPT-2 architecture and is designed specifically for multi-turn conversation tasks, enabling machines to produce coherent responses during interactive dialogue. The model was trained on a massive dataset of approximately 147 million conversational exchanges extracted from Reddit discussion threads, allowing it to learn patterns of natural human conversation. ... -
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learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
...The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow. It progressively moves from foundational theory to more advanced subjects including regression, classification, neural networks, and model deployment. The repository emphasizes understanding the underlying principles of machine learning while also providing practical exercises and examples that allow learners to build and experiment with real models. Many sections include notebooks and code examples that demonstrate how algorithms are implemented and trained using modern machine learning frameworks. -
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LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the use of sequential neural network architectures, the system learns patterns in motion data that correspond to activities such as walking, sitting, standing, or climbing stairs. ... -
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Emb-GAM
An interpretable and efficient predictor using pre-trained models
...In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs. Across a variety of natural-language-processing datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability. -
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Menagerie
A collection of high-quality models for the MuJoCo physics engine
...The repository aims to improve reproducibility and quality across robotics research by providing verified models that adhere to consistent design and physical standards. Each model directory contains its 3D assets, MJCF XML definitions, licensing information, and example scenes for visualization and testing. The collection spans a wide range of categories including robotic arms, humanoids, quadrupeds, mobile manipulators, drones, and biomechanical systems. Users can access models directly via the robot_descriptions Python package or by cloning the repository for use in interactive MuJoCo simulations. -
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abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
Enter your abstract into the little doohicky here, and quicker'n you can blink your eyes1, a shiny new paper'll come right out for ya! What are you waiting for? Click the "doohicky" link above to get started, and then click the link to open the demo notebook in Google Colaboratory. To run the demo as a Jupyter notebook (e.g., locally), use this version instead. Note: to compile a PDF of your auto-generated paper (when you run the demo locally), you'll need to have a working LaTeX... -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
...It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs.
