Ray project (Anyscale and contributors) · Distributed & parallel computing
In-depth entry
Open-source Python framework for distributing tasks, actors and ML workloads across a cluster, with libraries for data processing, training, tuning, serving and reinforcement learning
BSD-licensed Python machine-learning library for classification, regression, clustering, dimensionality reduction, preprocessing and model selection on NumPy and SciPy
Python library for computing and training embedding, cross-encoder reranker, sparse and multi-vector retrieval models, maintained by Hugging Face with thousands of pretrained models on the Hub
Hugging Face's model-definition library for text, vision, audio, video and multimodal models, with Pipeline, Trainer and generate APIs used as the reference by training and inference engines
Open-source framework for fine-tuning and running LLMs with reduced memory, covering LoRA, QLoRA, full fine-tuning and RL, GGUF export, Dynamic quantizations and a local Studio/Desktop UI
vLLM project (originated at UC Berkeley Sky Computing Lab) · Local LLM inference & serving
In-depth entry
High-throughput LLM inference and serving engine with PagedAttention, continuous batching, quantization and an OpenAI-compatible server for GPU and accelerator clusters
OpenAI's open-source multilingual speech recognition model family with MIT-licensed code and weights, a Python package and CLI for transcription, translation and language identification