Best AI tools for< Wrap Pytorch Models >

Pattern Cafe is an AI-powered tool that helps users create unique and seamless patterns for various purposes such as fabric, gift wrap, wallpaper, and game textures. With Pattern Cafe, users can generate high-quality patterns in seconds, making it an efficient and convenient solution for designers and creatives.

Car Concepts AI is an innovative AI-driven application that allows users to create stunning car wrap designs effortlessly. With over 10,000 car concepts created and 9,100 happy customers, Car Concepts AI is the go-to platform for transforming your vehicle's appearance. The application offers both Basic and Advanced modes for generating single image designs, catering to both simple and complex projects. Users can upload images, provide additional details, and let the AI algorithm generate unique and creative car wrap designs based on their preferences. With a user-friendly interface and a wide range of customization options, Car Concepts AI is the ultimate tool for elevating your car's style.

Elf Help is a free gift-giving assistant that offers personalized and creative suggestions for everyone on your list. It is designed to help users save time and stress during the holiday season by providing unique gift ideas. Elf Help is easy to use and convenient, making it a valuable tool for finding thoughtful gifts for even the hardest-to-shop-for people.

Elythea is a next-generation maternal care application that provides 24/7 maternal concierge care by detecting and preventing pregnancy risks before they occur. The platform offers unlimited wrap-around care with a personalized team of specialists who proactively monitor your health. Elythea's approach involves early identification of complications, proactive contact through digital care managers, and personalized care through a team of specialists available anytime, anywhere. The application is designed to be user-friendly and accessible, offering tailored support to expectant mothers throughout their pregnancy journey.

Agenda Runner is an AI-powered tool that allows users to quickly build meeting agendas for free. Users can describe their meeting, include general details, and specific topics, and with a click, a public agenda is generated. The tool focuses on enhancing meeting efficiency through improved agenda planning and execution, providing guidance on setting objectives, time management, interactive components, follow-up actions, and wrap-up procedures.

The Neutone SDK is a tool designed for researchers to wrap their own audio models and run them in a DAW using the Neutone Plugin. It simplifies the process by allowing models to be built using PyTorch and minimal Python code, eliminating the need for extensive C++ knowledge. The SDK provides support for buffering inputs and outputs, sample rate conversion, and profiling tools for model performance testing. It also offers examples, notebooks, and a submission process for sharing models with the community.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

org-ai is a minor mode for Emacs org-mode that provides access to generative AI models, including OpenAI API (ChatGPT, DALL-E, other text models) and Stable Diffusion. Users can use ChatGPT to generate text, have speech input and output interactions with AI, generate images and image variations using Stable Diffusion or DALL-E, and use various commands outside org-mode for prompting using selected text or multiple files. The tool supports syntax highlighting in AI blocks, auto-fill paragraphs on insertion, and offers block options for ChatGPT, DALL-E, and other text models. Users can also generate image variations, use global commands, and benefit from Noweb support for named source blocks.

The 'gritlm' repository provides all materials for the paper Generative Representational Instruction Tuning. It includes code for inference, training, evaluation, and known issues related to the GritLM model. The repository also offers models for embedding and generation tasks, along with instructions on how to train and evaluate the models. Additionally, it contains visualizations, acknowledgements, and a citation for referencing the work.

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. Ollama is designed to be easy to use and accessible to developers of all levels. It is open source and available for free on GitHub.

NExT-GPT is an end-to-end multimodal large language model that can process input and generate output in various combinations of text, image, video, and audio. It leverages existing pre-trained models and diffusion models with end-to-end instruction tuning. The repository contains code, data, and model weights for NExT-GPT, allowing users to work with different modalities and perform tasks like encoding, understanding, reasoning, and generating multimodal content.

rwkv.cpp is a port of BlinkDL/RWKV-LM to ggerganov/ggml, supporting FP32, FP16, and quantized INT4, INT5, and INT8 inference. It focuses on CPU but also supports cuBLAS. The project provides a C library rwkv.h and a Python wrapper. RWKV is a large language model architecture with models like RWKV v5 and v6. It requires only state from the previous step for calculations, making it CPU-friendly on large context lengths. Users are advised to test all available formats for perplexity and latency on a representative dataset before serious use.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

The .NET Conf Focus on AI 2024 repository contains content from the event focusing on incorporating AI into .NET applications and services. It includes slides and demos showcasing various AI-powered web apps, AI models, generative AI apps, and more. The repository serves as a resource for developers looking to explore AI integration with .NET technologies.

This repository contains the official implementation of RPG, a powerful training-free paradigm for text-to-image generation and editing. RPG utilizes proprietary or open-source MLLMs as prompt recaptioner and region planner with complementary regional diffusion. It achieves state-of-the-art results and can generate high-resolution images. The codebase supports diffusers and various diffusion backbones, including SDXL and SD v1.4/1.5. Users can reproduce results with GPT-4, Gemini-Pro, or local MLLMs like miniGPT-4. The repository provides tools for quick start, regional diffusion with GPT-4, and regional diffusion with local LLMs.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

This AI-powered git commit plugin for VSCode streamlines your commit and push processes, eliminating the need for manual confirmation. With a focus on minimizing keystrokes, the plugin leverages LLM to generate commit messages and automate the entire process. Key features include AI-assisted git commit and push, eliminating the need for the 'git add .' command, and customizable OpenAI model selection. The plugin supports multiple languages, making it accessible to developers worldwide. Additionally, it offers advanced settings for specifying the OpenAI API key, base URL, and conventional commit format. Developers can contribute to the project by following the provided development instructions.

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