Search Results for "repository" - Page 15
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Showing 504 open source projects for "repository"
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Consistency Models
Official repo for consistency models
consistency_models is the repository for Consistency Models, a new family of generative models introduced by OpenAI that aim to generate high-quality samples by mapping noise directly into data — circumventing the need for lengthy diffusion chains. It builds on and extends diffusion model frameworks (e.g. based on the guided-diffusion codebase), adding techniques like consistency distillation and consistency training to enable fast, often one-step, sample generation. -
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Bard
Python SDK/API for reverse engineered Google Bard
The Bard repository from acheong08 is an unofficial client library and set of tools designed to interact programmatically with Google Bard, Google’s conversational AI service, allowing developers to automate queries, integrate Bard into their own applications, and experiment with scripting around Bard-style prompts without relying on a formal, official API. -
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CodeFormer
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
...The model uses a codebook lookup transformer approach to balance perceptual quality and identity fidelity through a configurable fidelity weight. It supports cropped face restoration, whole-image enhancement, video enhancement, face colorization, and face inpainting. The repository provides pretrained model download scripts, inference commands, training code, and configuration files. It is useful for research, restoration workflows, creative tooling, and applications that need robust face recovery from poor visual inputs. -
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Language Models
Explore large language models in 512MB of RAM
...The package provides simple APIs that allow developers to generate text, perform semantic search, classify text, and answer questions using local models. 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. -
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5
FastEdit
Editing large language models within 10 seconds
...It implements practical editing algorithms that insert or revise knowledge with targeted parameter updates, aiming to preserve model quality outside the edited scope. This approach is valuable when you need urgent corrections—think product names, APIs, or fast-changing facts—without retraining on large corpora. The repository provides evaluation harnesses so you can measure locality (does the change stay contained?) and generalization (does the change apply where it should?). It’s structured for repeatable experiments, making side-by-side comparisons of editing methods and hyperparameters straightforward. For applied teams, FastEdit offers a toolbox to keep models current and compliant while minimizing collateral damage to overall performance. -
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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. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
...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. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. ... -
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...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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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
PRM800K is a process supervision dataset accompanying the paper Let’s Verify Step by Step, providing 800,000 step-level correctness labels on model-generated solutions to problems from the MATH dataset. 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. ... -
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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. -
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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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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. -
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LM Human Preferences
Code for 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 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. ... -
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Img2Txt
Img2Txt - Extract Text From Images using AI
...Support me at : https://www.buymeacoffee.com/zsynctic it will motivate me and it will make me create more projects Support For any questions or issues, please open an issue on the Img2Txt GitHub repository. Warning: When running Img2Txt.exe a Blue Window Might Popup. To Run The Application You Have To Press More Info And Then Run Anyways. © zSynctic -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
Optimate is an open source collection of libraries designed to optimize the performance and cost efficiency of artificial intelligence models across different stages of the machine learning lifecycle. It groups several internal optimization tools developed by Nebuly AI into a single repository that focuses on improving inference speed, reducing infrastructure usage, and streamlining model training workflows. Its modules help developers automatically apply optimization techniques that better align AI models with the capabilities of the underlying hardware such as GPUs and CPUs. One of the core components, Speedster, focuses on accelerating model inference by applying state of the art optimization techniques to increase performance while lowering operational costs. ... -
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PromptCraft-Robotics
Community for applying LLMs to robotics and a robot simulator
The PromptCraft-Robotics repository serves as a community for people to test and share interesting prompting examples for large language models (LLMs) within the robotics domain. We also provide a sample robotics simulator (built on Microsoft AirSim) with ChatGPT integration for users to get started. We currently focus on OpenAI's ChatGPT, but we also welcome examples from other LLMs (for example open-sourced models or others with API access such as GPT-3 and Codex). -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. -
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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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ConvNeXt V2
Code release for ConvNeXt V2 model
...A key innovation is a new Global Response Normalization (GRN) layer added to the ConvNeXt backbone, which enhances feature competition across channels. The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. It supports both self-supervised pretraining and supervised fine-tuning. -
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NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
...You can easily do this as well, given you train a VQGanVAE on the sketches beforehand. Then, you will use NUWASketch instead of NUWA, which can accept the sketch VAE as a reference. This repository will also offer a variant of NUWA that can produce both video and audio. For now, the audio will need to be encoded manually. -
21
Point-E
Point cloud diffusion for 3D model synthesis
...While it does not match the fine detail of some slower methods, the tradeoff in speed makes it practical for prototyping and interactive 3D generation. The repository includes inference scripts, utilities for converting point clouds to meshes (e.g. via signed distance function regression), sample notebooks, and weight checkpoints. It also provides documentation on limitations, usage instructions, and example outputs. -
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CPT
CPT: A Pre-Trained Unbalanced Transformer
A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation. We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. Position Embeddings We extend the max_position_embeddings from 512 to 1024. We... -
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Karlo
Text-conditional image generation model based on OpenAI's unCLIP
...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. Unlike the original implementation of unCLIP, we replace the trainable transformer in the decoder into the text encoder in ViT-L/14 for efficiency. In the case of the SR module, we first train the model using the DDPM objective in 1M steps, followed by additional 234K steps to fine-tune the additional component. -
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Screen Translate
An OCR translator tool made by utilizing tesseract & python-opencv
STL is an easy to use and light OCR translator tool that can be use to translate your screen. Made with python by utilizing Tesseract and opencv-python. For full view of the project you can check the Github repository: https://github.com/Dadangdut33/Screen-Translate REQUIREMENTS - Tesseract : https://github.com/UB-Mannheim/tesseract/wiki. Needed for the ocr. Install it with all the language pack. - Libretranslate (Optional for offline translation support) - Internet connection for translation if not using libretranslate # Tutorial on How To Setup https://github.com/Dadangdut33/Screen-Translate#installation-and-setup -
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Stable Diffusion
A latent text-to-image diffusion model
...It was trained on large-scale image datasets and later fine-tuned to produce 512×512 images with strong visual fidelity. Because the system runs efficiently on consumer hardware compared to earlier generative models, it helped popularize local AI image generation workflows. The repository includes reference scripts and model configurations that allow researchers and developers to reproduce, modify, or extend the architecture. Overall, stable-diffusion has become a foundational tool in the generative AI ecosystem for art creation, research, and multimodal experimentation.