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Showing 1303 open source projects for "can="
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Free-Auto-GPT
Free AutoGPT enables autonomous AI tasks without paid APIs
Free Auto GPT is an open source project that delivers a simplified version of an autonomous AI agent capable of completing tasks independently. It allows users to run an AutoGPT-style system without relying on paid OpenAI APIs, making it more accessible for experimentation and personal use. Free Auto GPT can take a goal, break it into smaller steps, and execute actions in a loop to achieve results with minimal human input. Designed for ease of use, the project focuses on removing cost barriers while still demonstrating how autonomous agents function. It is suitable for developers, learners, and hobbyists who want to explore AI-driven automation without subscription requirements. ... -
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Audio Webui
A webui for different audio related Neural Networks
Audio Webui is a Gradio-based web user interface that unifies a wide range of audio-related neural networks under a single, accessible front end. It is designed as an “all-in-one” environment where users can experiment with text-to-speech, voice cloning, generative music, and other neural audio models without writing boilerplate code. The project supports multiple back-end models and toolchains (such as Bark, RVC, AudioLDM, Audiocraft, and other text-to-audio or voice-cloning tools), exposing them through a consistent UI for inference and experimentation. ... -
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
...For example, for image segmentation where your data is stored in folders, you would use the from_folders method of the SemanticSegmentationData class. Our tasks come loaded with pre-trained backbones and (where applicable) heads. You can view the available backbones to use with your task using available_backbones. With Flash, swapping among 40+ optimizers and 15 + schedulers recipes are simple. -
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learn2learn
A PyTorch Library for Meta-learning Research
Learn2Learn is a PyTorch-based library focused on meta-learning and few-shot learning research. It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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Prime QA
State-of-the-art Multilingual Question Answering research
PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly downloadable. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. ... -
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AI-powered enterprise search engine
AI-powered enterprise search engine
...The platform indexes content from connected systems rather than relying on their native search capabilities, resulting in faster and more relevant results across large datasets. Gerev is built with a strong emphasis on privacy and control, as it can be fully self-hosted, ensuring that sensitive company data remains. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. ... -
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CausalNex
A Python library that helps data scientists to infer causation
CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Poe is a platform for interacting with AI-based bots. That includes well-known chat bots like OpenAI's ChatGPT and Anthropic's Claude, but anyone can create their own bot by implementing the protocol described in this repository.
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langchain-prefect
Tools for using Langchain with Prefect
...It provides a framework for creating deployments on a whole slew of runtime environments (from Lambda to Kubernetes), and is cloud agnostic (best supports AWS, GCP, Azure). For this reason, it could be a great fit for observing apps that use LLMs. RecordLLMCalls is a ContextDecorator that can be used to track LLM calls made by Langchain LLMs as Prefect flows. Run several LLM calls via langchain agent as Prefect subflows. -
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lora-svc
Singing voice change based on whisper, lora for singing voice clone
singing voice change based on whisper, and lora for singing voice clone. You will feel the beauty of the code from this project. Uni-SVC main branch is for singing voice clone based on whisper with speaker encoder and speaker adapter. Uni-SVC main target is to develop lora for SVC. With lora, maybe clone a singer just need 10 stence after 10 minutes train. Each singer is a plug-in of the base model. -
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Lumi-HSP
This is an AI language model that can predict Heart failure or stroke
Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone. -
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It is a powerful data scraping tool that helps you extract information from various online sources. Easily collect data from Google SERP, Maps, Shopify, Zillow, and more. With a user-friendly interface, you can scrape and save data in JSON or Excel formats. Unlock insights from the web effortlessly with scrape-it.cloud API.
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ParlAI
A framework for training and evaluating AI models
ParlAI is a comprehensive research platform for building, training, and evaluating dialogue agents across a wide variety of tasks and datasets. It provides a unified interface—agents, teachers, and worlds—so the same model can be trained on multi-turn chit-chat, question answering, task-oriented dialogue, retrieval, or safety-focused datasets without changing core code. The library integrates tightly with PyTorch and supports both generative and retrieval-augmented models, along with utilities for multitask training and model selection. A large set of built-in tasks and dataset loaders (with consistent preprocessing and metrics) makes it easy to compare methods under shared conditions. ... -
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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
...The repository releases the raw labels and the labeler instructions used in two project phases, enabling researchers to study how human raters graded intermediate reasoning. Data are stored as newline-delimited JSONL files tracked with Git LFS, where each line is a full solution sample that can contain many step-level labels and rich metadata such as labeler UUIDs, timestamps, generation identifiers, and quality-control flags. Each labeled step can include multiple candidate completions with ratings of -1, 0, or +1, optional human-written corrections (phase 1), and a chosen completion index, along with a final finish reason such as found_error, solution, bad_problem, or give_up. -
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TensorFlow Ranking
Learning to rank in TensorFlow
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. Commonly used loss functions including pointwise, pairwise, and listwise losses. Commonly used ranking metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library... -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. Spektral implements some of the most popular layers for graph deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. ... -
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Image to Text
Convert an image to text to spot intelligible words.
...Use the program with photos of clouds, sea, soil, vegetation or any other photo of natural or man-made semi-homogeneous configuration, to reveal the hidden universal-philosophical messages of the image. You can also use it on photos of people or art pieces to have a psychological insight of the person portrayed or of the image author. The resulting text will be a long string of mostly repeating characters but, every so often, you will notice an intelligible word, either spelled normally or with repeated letters, missing letters or anagrammed letters. -
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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
...It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. ... -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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alfred-ai
The development of my ai assistant, Alfred
The development of my ai assistant, Alfred. -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
...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. -
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AnnLite
A fast embedded library for approximate nearest neighbor search
...The library allows you to search for nearest neighbors within a subset of the dataset. Smooth integration with neural search ecosystem including Jina and DocArray, so that users can easily expose search API with gRPC and/or HTTP. The library is easy to install and use. It is designed to be used with Python. To support search with filters, the annlite must be created with colums parameter, which is a series of fields you want to filter by. -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
...The repository includes examples of widely used reinforcement learning methods such as REINFORCE, Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic architectures. Most experiments are designed to run quickly using the CartPole environment so that users can focus on understanding algorithm logic rather than computational infrastructure.
