Open Source PyTorch Projects
PyTorch is an open source machine learning framework for building and training neural networks with tensor-based computations. It supports automatic differentiation, GPU acceleration, and flexible model development, helping researchers and developers create, test, and deploy systems for tasks such as image recognition, language processing, and scientific computing. Its Python-first interface makes it possible to inspect and modify models while they run, which is useful for both experimentation and production workflows.
Open source tools in this area include model libraries, training utilities, data pipelines, optimization kernels, and deployment tools. When choosing one, consider its license, maintenance activity, documentation, hardware and software requirements, and compatibility with your existing training or inference stack. These tools can help learners, researchers, and engineering teams work across the machine learning lifecycle, from prototyping and fine-tuning to serving models.
28 repositories · updated October 3, 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.
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.
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 It is aimed at developers who want editable recipes and model-specific configs, but is no longer actively maintained.
torchchat: Run PyTorch LLMs on Desktop, Server, and Mobile
torchchat is a PyTorch codebase for running and interacting with language models locally through Python, native C++ runners, and mobile apps. It supports several execution and export paths, but is no longer under active development.
TensorRT-LLM: Optimize LLM Inference on NVIDIA GPUs
TensorRT-LLM is a Python framework and runtime for efficient LLM and visual-generation inference on NVIDIA GPUs. It suits teams deploying models on NVIDIA hardware that need optimized kernels and configurable single- or distributed-GPU execution.
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.
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.
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.
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.
PyTorch Image Models (timm): The Ultimate Collection of Image Encoders
PyTorch Image Models (timm) is an extensive library offering the largest collection of PyTorch image encoders and backbones. It provides a wide array of state-of-the-art models, complete with pretrained weights, training, evaluation, and inference scripts. This makes it an invaluable resource for researchers and developers working with computer vision tasks in PyTorch.
notebooks: Learn and Apply Computer Vision Models
Roboflow notebooks is a hands-on tutorial collection for computer vision, covering model training, inference, detection, segmentation, and related tasks. Use it to explore techniques and run examples in hosted notebook environments.