Open Source Deep Learning Projects
Discover 78 open source Deep Learning repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Deep Learning projects here are most often combined with Machine Learning, Python and PyTorch. Last updated October 3, 2026.
78 repositories · updated October 3, 2026
PETSA: Adapt Time-Series Forecasters at Test Time
PETSA is a parameter-efficient test-time adaptation method for time-series forecasting. It updates small input and output calibration modules rather than the full model, targeting non-stationary data with lower adaptation costs.
flash-attention: Compute Exact Attention Faster and with Less Memory
FlashAttention provides GPU-optimized implementations of exact attention for deep learning, reducing memory use while improving speed. It is aimed at practitioners training or serving Transformer models who can build and run GPU kernels.
aimet: Quantize and Compress Trained Neural Networks
AIMET helps ML engineers reduce the compute and memory needs of trained neural networks through quantization and compression. It supports PyTorch and ONNX workflows for preparing models for efficient deployment, including on edge devices.
LLM Reasoners: Advanced Library for Large Language Model Reasoning
LLM Reasoners is a powerful Python library designed to significantly enhance the complex reasoning capabilities of Large Language Models. It offers a comprehensive suite of cutting-edge search algorithms, intuitive visualization tools, and optimized performance for efficient LLM inference. The library prioritizes rigorous implementation and reproducibility, making it a reliable tool for researchers and developers in the AI field.
awesome-AI-books: Find AI Books, Papers, and Learning Resources
A curated directory of books, papers, online references, and practice environments across AI and related fields. It helps learners find starting points for topics from mathematics and machine learning to reinforcement learning and quantum computing.
parakeet-mlx: Nvidia's Parakeet ASR Models on Apple Silicon with MLX
parakeet-mlx is an open-source project that implements Nvidia's advanced Automatic Speech Recognition (ASR) Parakeet models for Apple Silicon, leveraging the MLX framework for optimized performance. This Python library offers both a command-line interface and a flexible Python API, enabling efficient transcription of audio files, including real-time streaming capabilities. It provides a powerful solution for developers and researchers working with speech processing on Apple hardware.
PartCrafter: Generate Structured 3D Meshes from Images
PartCrafter generates part-separated 3D objects and scenes from a single RGB image using compositional latent diffusion. It suits researchers and developers exploring image-to-3D generation who have access to a CUDA-enabled GPU.
optimum: Optimize Model Training and Inference on Target Hardware
Hugging Face Optimum adds tools for optimizing model training and inference across hardware backends. It suits teams using Transformers, Diffusers, TIMM, or Sentence Transformers who need hardware-specific deployment or training workflows.
litgpt: Train, Fine-Tune, and Deploy Large Language Models
LitGPT provides implementations and workflows for pretraining, fine-tuning, evaluating, and serving a range of large language models. It suits developers and researchers who want configurable training recipes and direct control over model code.
TabSTAR: Apply a Tabular Foundation Model to Data with Text Fields
TabSTAR is a Python model for classification and regression on tabular datasets that include text fields. Use its package to fit a pretrained model to your data, or its research tools to pretrain and evaluate on benchmarks.
big_vision: Train and Evaluate Large-Scale Vision Models
Google Research’s JAX and Flax codebase for training and evaluating vision and image-text models on GPUs and Cloud TPUs. It suits researchers running scalable experiments, but project-specific code may not stay compatible with the current core.
HunyuanVideo-Avatar: Create Audio-Driven Character Videos
HunyuanVideo-Avatar generates dynamic, emotion-controllable videos of one or more characters from avatar images and audio. It is aimed at creators and researchers who need expressive talking-avatar or dialogue video generation and have access to compatible NVIDIA GPU hardware.