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
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 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.
An LLM post-training framework for RL scaling by Tsinghua THUDM, deeply integrating Megatron-LM training with SGLang inference engine for distributed reinforcement learning on large models like GLM, Qwen, DeepSeek, and Llama.
The official inference framework for 1-bit Large Language Models by Microsoft. It features optimized kernels for lossless, high-speed inference on CPUs and GPUs, drastically reducing energy consumption and enabling 100B+ parameter models to run on local consumer hardware.
AirLLM optimizes inference memory usage, enabling 70B large language models to run on a single 4GB GPU card without quantization, distillation, or pruning. It now also supports running 405B Llama3.1 models on 8GB VRAM.
An open-source 314B parameter large language model with Mixture of Experts (MoE) architecture, providing researchers and developers with accessible implementation of ultra-large-scale AI models.
A benchmark platform featuring 100 PhD-level research tasks across 22 distinct fields, systematically evaluating Deep Research Agents (DRAs) on report generation quality and information retrieval capabilities.
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