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.
71 repositories · updated October 14, 2026
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.
MaintainedHealth 37/100
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.
ActiveHealth 67/100
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.
ActiveHealth 62/100
xTuring: Fine-Tune and Run Personalized LLMs
xTuring is a Python library for preparing data, fine-tuning, evaluating, and running open-source language models locally or in a private cloud. It is aimed at developers who want model customization through a high-level API and methods such as LoRA and quantization.
ActiveHealth 42/100
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.
QuietHealth 39/100
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.
InactiveHealth 19/100
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.
ArchivedHealth 28/100
physicsnemo: Build and Train Physics AI Models
NVIDIA PhysicsNeMo is a PyTorch framework for building and training machine-learning models for physics and engineering. It combines reusable model components with end-to-end recipes for scientific data and workloads.
ActiveHealth 68/100
VoxCPM: Generate and Clone Multilingual Speech
VoxCPM is a tokenizer-free text-to-speech system for multilingual speech generation, voice design, and voice cloning. Its VoxCPM2 release targets teams and developers who need expressive speech synthesis and can support a 2B-parameter model.
ActiveHealth 57/100
ai-engineering-from-scratch: Learn AI by Building It
A free, MIT-licensed curriculum that teaches AI engineering through runnable lessons and hands-on artifacts. It covers foundations through LLMs, agents, protocols, and production, with paths for beginners and experienced developers.
ActiveHealth 62/100
JARVIS: Connect Language Models to AI Tools
JARVIS, also known as HuggingGPT, uses an LLM to plan requests, choose expert models from Hugging Face, run tasks, and combine their results. It suits research and prototyping that explores natural-language orchestration of AI models.
InactiveHealth 49/100
peft: Fine-Tune Models with Fewer Trainable Parameters
Hugging Face PEFT adapts pretrained models by training a small set of additional parameters instead of updating the full model. It is for practitioners who want to reduce fine-tuning compute and storage costs across supported model workflows.
ActiveHealth 67/100