Tag

Open-source deep-learning framework for building, training and deploying neural networks on GPUs and CPUs.

Agent Lightning is an open-source framework developed by Microsoft Research for optimizing and training AI agents using reinforcement learning (RL) and other techniques, supporting integration with any agent framework with minimal code changes.

nanochat is a full-stack, minimal codebase for training, fine-tuning, evaluating, and deploying a ChatGPT-like large language model (LLM) from scratch on a single 8xH100 GPU node for under $100.

End-to-end open-source machine-learning platform for building, training and deploying models across desktop, mobile, web and cloud.

nanoGPT is the simplest, fastest repository for training/finetuning medium-sized GPTs. It is a rewrite of minGPT that prioritizes practicality over education. Still under active development, but currently the file train.py reproduces GPT-2 (124M) on OpenWebText, running on a single 8XA100 40GB node in about 4 days of training.

KTransformers is a flexible framework for experiencing cutting-edge optimizations in LLM inference and fine-tuning, focusing on CPU-GPU heterogeneous computing. It consists of two core modules: kt-kernel for high-performance inference kernels and kt-sft for fine-tuning. The project supports various hardware and models like DeepSeek series, Kimi-K2, achieving significant resource savings and speedups, such as reducing GPU memory for a 671B model to 70GB and up to 28x acceleration.

Open-source Python library from Hugging Face that supplies thousands of pretrained transformer models and utilities for text, vision, audio and multimodal tasks.

A free, open-source Python library that offers a unified API for classical machine-learning algorithms, data-pre-processing, model selection and evaluation.

MiniMind is an open-source GitHub project that enables users to train a 26M-parameter tiny LLM from scratch in just 2 hours with a cost of 3 RMB. It provides native PyTorch implementations for Tokenizer training, pretraining, supervised fine-tuning (SFT), LoRA, DPO, PPO/GRPO reinforcement learning, and MoE architecture with vision multimodal extensions. It includes high-quality open datasets, supports single-GPU training, and is compatible with Transformers, llama.cpp, and other frameworks, ideal for LLM beginners.

High-level, multi-backend deep-learning API for building, training and deploying neural-network models.

Open-source framework that accelerates fine-tuning and full training of transformer LLMs by up to 30 × while cutting VRAM requirements by roughly 90 %, letting developers train custom models quickly on commodity GPUs.

Lightweight PyTorch wrapper that separates research code from engineering, enabling fast, scalable and reproducible deep-learning workflows.