verl
🧠A flexible, efficient, and production-ready post-training reinforcement learning framework for LLMs
A flexible, efficient, and production-ready post-training reinforcement learning framework for LLMs
An advanced post-training quantization toolkit for LLMs and VLMs by Intel, leveraging SignRound optimization to support 2–4 bit weight quantization and automatic mixed-precision scheme generation across Intel CPU/GPU, NVIDIA GPU, and Habana Gaudi.
A fully disaggregated multimodal model inference and serving framework that extends vLLM to support any-to-any modality unified inference and high-performance deployment.
A KVCache-centric disaggregated architecture platform for LLM serving, providing distributed KVCache pooling, topology-aware high-speed transfer engine, and centralized scheduler, supporting Prefill-Decode separation and MoE elastic inference.
A full-stack LLM development toolkit from NVIDIA covering synthetic data generation, multi-backend inference, model training, and 11-category benchmark evaluation, scaling from single GPU to tens-of-thousands-GPU Slurm clusters.
A high-performance OpenAI-compatible API server for MLX models on Apple Silicon, supporting text, vision, audio transcription, and image generation/editing.
A fully on-device voice AI assistant for macOS Apple Silicon, integrating STT, LLM, TTS, VLM, RAG, and system control with zero cloud dependency.
Flexible and scalable reinforcement learning training infrastructure for embodied and agentic AI post-training, decoupling logical workflow composition from efficient physical execution via the M2Flow paradigm.
A distributed inference framework for running frontier LLMs across local device clusters, built on Apple MLX and libp2p, featuring automatic device discovery, topology-aware parallelism, and multi-API compatibility.
An RL training environment building library for LLMs, providing complete infrastructure from development and testing to scaled rollout collection, with built-in RLVR scenarios and tool-calling support.
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