Repository History
15 repositories tagged with deep-learning
Axolotl: Streamlining LLM Fine-tuning with a Powerful Open-Source Framework
Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs.
torchchat: Run PyTorch LLMs Locally on Servers, Desktop, and Mobile
torchchat is a PyTorch-native codebase designed to showcase the ability to run large language models (LLMs) seamlessly across various platforms. It enables local execution of LLMs using Python, within C/C++ applications on desktop or servers, and directly on iOS and Android devices. Although no longer under active development, it remains a valuable resource for understanding and implementing local LLM deployment strategies.
NVIDIA PhysicsNeMo: Deep Learning Framework for Physics-ML Models
NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building, training, and fine-tuning Physics AI models. It leverages state-of-the-art scientific machine learning methods, enabling real-time predictions by combining physics knowledge with data. This framework provides scalable, GPU-optimized tools for AI4Science and engineering applications.
JARVIS: Connecting LLMs with the ML Community for AGI Exploration
JARVIS is an innovative system developed by Microsoft that aims to bridge Large Language Models (LLMs) with the broader Machine Learning community. It serves as a collaborative platform, using an LLM as a controller to orchestrate numerous expert models from Hugging Face Hub, thereby facilitating the exploration of Artificial General Intelligence (AGI) and solving complex AI tasks. This system streamlines the process of task planning, model selection, execution, and response generation.
Roboflow Notebooks: Master State-of-the-Art Computer Vision Models
Roboflow Notebooks offers a comprehensive collection of tutorials designed to help users master state-of-the-art computer vision models and techniques. This repository covers a wide range of topics, from foundational architectures like ResNet to cutting-edge models such as RF-DETR, YOLO11, SAM 3, and Qwen3-VL. It serves as an invaluable resource for anyone looking to explore and implement advanced computer vision solutions.
AUTOMATIC1111/stable-diffusion-webui: Powerful AI Image Generation Web UI
The AUTOMATIC1111/stable-diffusion-webui project offers a comprehensive web interface for Stable Diffusion, simplifying AI art generation. It provides a robust set of features, including text-to-image, image-to-image, inpainting, and upscaling, all within a user-friendly environment. This Python-based UI is a popular choice for both beginners and advanced users exploring generative AI.
ML-From-Scratch: Machine Learning Models and Algorithms in NumPy
ML-From-Scratch is a comprehensive GitHub repository offering bare-bones NumPy implementations of fundamental machine learning models and algorithms. It emphasizes accessibility, making complex concepts easier to understand for learners and practitioners. This project covers a wide range of topics, from linear regression to deep learning and reinforcement learning, all implemented from scratch.
Spotlight: Deep Recommender Models with PyTorch
Spotlight is a Python library built on PyTorch for developing deep and shallow recommender models. It offers a comprehensive set of building blocks for various loss functions, representations, and utilities for handling recommendation datasets. This tool is designed for rapid exploration and prototyping of new recommender systems.
LitGPT: High-Performance LLMs for Pretraining, Finetuning, and Deployment
LitGPT, by Lightning AI, is a comprehensive GitHub repository offering over 20 high-performance Large Language Models (LLMs). It provides robust recipes and tools to pretrain, finetune, and deploy these models at scale. Designed with minimal abstractions, LitGPT ensures blazing fast, minimal, and performant solutions for enterprise-grade AI development.
TabSTAR: A Tabular Foundation Model for Data with Text Fields
TabSTAR is an innovative tabular foundation model designed to effectively process tabular data that includes text fields. It offers a user-friendly package for integrating pretrained models into your own datasets, alongside a comprehensive research mode for advanced development and benchmarking. This powerful tool simplifies the application of deep learning to complex tabular structures.
ggml: A Low-Level Tensor Library for Machine Learning
ggml is an innovative tensor library designed for machine learning, emphasizing low-level, cross-platform implementation. It offers features like integer quantization, automatic differentiation, and broad hardware support, all while maintaining zero third-party dependencies and efficient memory usage. This project is actively developed and forms the backbone for other popular projects like llama.cpp and whisper.cpp.
DragGAN: Interactive Point-Based Image Manipulation with Generative AI
DragGAN is the official code for the SIGGRAPH 2023 paper, "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold." This powerful Python-based repository enables users to precisely control and manipulate generated images using interactive dragging points. It offers an intuitive way to edit AI-generated content, making complex image transformations accessible.