Open Source Deep Learning Projects
Deep learning is a branch of machine learning that uses neural networks with many layers to learn patterns and representations from data. It is useful for tasks where hand-written rules are difficult to create, including image and speech recognition, language processing, forecasting, and scientific modeling. Training typically involves large datasets and substantial computing resources, while pretrained models can make some applications more accessible.
Open source tools in this area include frameworks for building and training networks, pretrained models, data and evaluation utilities, and guides for learning core concepts. When choosing a tool, consider its license, maintenance activity, documentation, hardware and software requirements, and compatibility with your data and deployment environment. These resources can help learners, researchers, and developers explore neural networks, adapt models, and build or evaluate machine learning systems.
73 repositories · updated October 3, 2026
ds4: A Fast Local Inference Engine for DeepSeek V4 Flash and PRO on Metal, CUDA, ROCm
ds4 is a highly optimized, native inference engine designed for DeepSeek V4 Flash and PRO models. It provides efficient local inference across various hardware platforms, including Apple Silicon (Metal), NVIDIA GPUs (CUDA), and AMD ROCm. This project focuses on delivering high performance for large language models on consumer-grade machines.
AWESOME-OCR-LLM: Curated Reading List for OCR in the LLM Era
AWESOME-OCR-LLM is a continuously updated reading list focusing on Optical Character Recognition (OCR) in the era of large language models (LLMs). It covers key areas like document parsing, understanding, visual text generation, and benchmarks, highlighting research from the past five years. This resource is invaluable for anyone tracking the rapid advancements in multimodal document AI.
Awesome-pytorch-list: Find PyTorch Libraries, Tutorials, and Papers
A categorized directory of PyTorch libraries, learning materials, and paper implementations. Use it to discover resources across NLP, computer vision, probabilistic modeling, and other deep-learning topics.
ds-cheatsheets: Your Ultimate Collection of Data Science Cheatsheets
The ds-cheatsheets repository by FavioVazquez offers an extensive collection of quick reference guides for data science. It covers a broad spectrum of topics, including programming languages like Python and R, and advanced concepts in Machine Learning and Deep Learning. This resource is perfect for anyone needing a handy guide to navigate the complex world of data science.
axolotl: Fine-Tune Large Language Models
Axolotl is a Python framework for fine-tuning and post-training language models through configurable workflows. It supports methods from LoRA and QLoRA to preference tuning and reinforcement learning, with options for multimodal and distributed training.
ludwig: Configure and Train AI Models with YAML
Ludwig is a Python framework for configuring, training, evaluating, and deploying AI models through declarative YAML rather than custom training loops. It suits teams that want one workflow for LLM fine-tuning, tabular prediction, and multimodal modeling.
xTuring: Build, Personalize, and Control Your Own LLMs
xTuring is an open-source framework designed to simplify the process of building, personalizing, and controlling Large Language Models (LLMs). It provides an easy way to fine-tune open-source LLMs on your own data, offering features from data pre-processing to efficient training and inference. This tool empowers developers to create private, personalized LLMs locally or in their private cloud environments.
torchtune: Fine-Tune and Post-Train Large Language Models
torchtune is a PyTorch library for configuring and running LLM post-training workflows, from supervised fine-tuning to preference optimization. It is aimed at developers who want editable recipes and model-specific configs, but is no longer actively maintained.
LightLLM: Serve Large Language Models with a Python Framework
LightLLM is a Python framework for LLM inference and serving, designed for scalable deployment and fast generation. It suits teams operating model-serving systems and researchers building on inference components.
DataDreamer: Generate Synthetic Data and Train LLMs
DataDreamer is a Python library for building LLM workflows, generating synthetic datasets, and training or aligning models. It suits researchers and developers who want reproducible, resumable workflows across open-source and API-based models.
LazyLLM: Low-Code Development for Multi-Agent LLM Applications
LazyLLM offers a low-code development tool designed for building multi-agent LLM applications with ease. It simplifies the creation of complex AI applications, providing a streamlined workflow for rapid prototyping, data feedback, and iterative optimization. Developers can leverage its extensive features for deployment, cross-platform compatibility, and efficient model fine-tuning.
promptbench: Evaluate LLMs and Test Prompt Robustness
PromptBench is a Python library for evaluating language and multimodal models across datasets, prompting methods, and adversarial attacks. It suits researchers and developers comparing model behavior or studying robustness and dynamic evaluation.