Open Source Machine Learning Projects
Discover 191 open source Machine Learning repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Machine Learning projects here are most often combined with Python, Deep Learning and LLM. Last updated October 4, 2026.
191 repositories · updated October 4, 2026
skills: Give AI Agents AMD Workflow Guidance
AMD Skills is a catalog of task-focused instructions and tools that help coding agents work with AMD hardware and software. Install selected skills into compatible agents when you need guidance for workflows such as local AI, ROCm troubleshooting, or LLM serving.
SkillOpt: Train Reusable Skills for Frozen LLM Agents
SkillOpt improves natural-language agent skills using scored task trajectories and validation-gated edits, without changing the target model's weights. It is aimed at teams that can evaluate repeatable tasks and want to deploy a reusable skill document.
awesome-ai-agents-2026: Find AI Agents, Frameworks, and Tools
A categorized directory of AI agents, frameworks, protocols, models, and learning resources. Use it to survey the ecosystem, compare options by category, and find starting points for building or adopting agent-based systems.
AsterMind-ELM: Build Lightweight JavaScript ELM Models
AsterMind-ELM is a TypeScript library for training and using Extreme Learning Machines in browser and Node.js applications. It supports kernel, online, and stacked ELMs for compact, on-device classification, regression, and embedding workflows.
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: Find Data Science Reference Sheets
A curated collection of data science cheat sheets covering Python, R, statistics, machine learning, deep learning, big data, SQL, and visualization. Use it to quickly find practical PDF and image references across common tools and workflows.
Speech: Build and Deploy Speech AI Models
NVIDIA NeMo Speech is a Python framework for researchers and developers building speech recognition, text-to-speech, and speech language models. Use it to train, customize, and run speech models with PyTorch and pretrained checkpoints.
colibri: Run Large MoE Models on Local Hardware
colibri is a C inference engine that streams Mixture-of-Experts model weights from disk, so large models can run without fitting entirely in RAM or VRAM. It supports CPU-only use and optional GPU backends, with speed depending on hardware and storage.
awesome-R: Discover Curated R Packages and Tools
awesome-R is a categorized directory of R packages, tools, learning materials, and related resources. Use it to explore options across data analysis, visualization, machine learning, and R development.
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.
mergoo: Combine Fine-Tuned LLM Experts into Routed Models
Mergoo combines fine-tuned language models or LoRA adapters into routed expert models, then supports training the resulting model. It is aimed at teams that want one model to draw on specialized experts rather than use them separately.
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.