6 个 · 工程化 · 模型 · AI 核心
Pax 是一个基于 Jax 的机器学习框架，用于训练大规模模型。Pax 支持先进且完全可配置的实验与并行化，并已展现出业界领先的模型 FLOP 利用率。Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
[CoLM 2026] MoANT 官方代码：面向多任务大语言模型微调的语义感知秩一专家混合模型[CoLM 2026] Official code for MoANT: Mixture-of-Rank-One-Experts with semantic-aware Intuition for Multi-task Large Language Model Finetuning
从零开始构建开源预训练 LLM，附带已发布的数据、训练代码、消融实验与结果，以高效提升模型质量。Build open pretraining LLMs from scratch with released data, training code, ablations, and results to improve model quality efficiently
用知识图谱刻画 AI/ML 模型从创建到部署的完整生命周期。Knowledge Graph to capture AI/ML model lifecycle from creation through deployments.
Same Targets, Different Computation 的代码与 artifact：后训练如何在模型各层之间划分计算Code and artifacts for Same Targets, Different Computation: how post-training divides work across model layers.
🚀 使用 Fast-LLM 加速 LLM 训练，一个面向高速、可扩展、灵活模型开发的开源库🚀 Accelerate LLM training with Fast-LLM, an open-source library for high-speed, scalable, and flexible model development.