This notebook walks through a complete pipeline—from configuring Isaac Lab Arena environments and downloading datasets, to using Mimic for large‑scale data augmentation, fine‑tuning a GR00T‑N1.5 policy, and performing closed‑loop evaluation—demonstrating how to develop and validate embodied AI tasks on PAI‑DSW.
This guide explains how to build high‑quality agent training data using ReAct trajectories, synthesize difficult samples with a data‑flywheel, and distill the knowledge into small LLMs on Alibaba Cloud PAI, covering teacher model deployment, EasyDistill installation, data generation, task solving, rubric filtering, and final model deployment.
This guide explains how to use the Newton physics engine and the lightweight Rerun visualizer with Isaac Lab on the PAI platform, covering environment setup, visualizer selection, single‑ and multi‑GPU reinforcement‑learning training, and performance analysis via TensorBoard.
This step‑by‑step tutorial shows how to quickly launch OpenAI’s open‑source gpt‑oss‑20b model on Alibaba Cloud PAI without writing code, configure the deployment, and start chatting with the model using the Cherry Studio client.
This article introduces the GSPO (Group Sequence Policy Optimization) reinforcement learning algorithm, explains its advantages over GRPO, and provides a detailed, end‑to‑end tutorial for reproducing GSPO training on Alibaba Cloud's PAI platform using the PAI‑ChatLearn framework.
This guide explains how to deploy the open‑source Qwen3‑Coder model on Alibaba Cloud's PAI platform, use the interactive PAI‑DSW environment, run code snippets, and generate notebook tutorials with the model's agentic CLI, covering both enterprise and individual developer scenarios.
This guide explains how to use the EasyDistill framework and Alibaba Cloud PAI to distill large language models for high‑quality text generation, covering model deployment, SFT and DPO training data construction, code examples, configuration files, and best practices for achieving resource‑efficient, high‑performance student models.
This article explains how Alibaba Cloud's AI platform PAI leverages the EasyDistill framework for post‑training model optimization, covering knowledge distillation concepts, data synthesis techniques, basic and advanced distillation training, the DistilQwen model family, real‑world customer cases, and step‑by‑step practical demos.
This article explains post‑training concepts, compares SFT, DPO, and GRPO fine‑tuning methods, and provides step‑by‑step guidance for using Alibaba Cloud's PAI platform—including Model Gallery and DSW—to fine‑tune large language models with code examples and practical tips.
This article introduces the integration of the open‑source distributed AI framework Ray with Alibaba Cloud’s PAI platform, detailing its advantages, architecture, fault‑tolerance, resource management, and provides a comprehensive step‑by‑step tutorial—including configuration, command examples, and code snippets—to efficiently run Ray jobs on PAI‑DLC.
This article walks through the full workflow for using Alibaba Cloud's open‑source DistilQwen2.5 models on the PAI platform, covering environment setup, model deployment, fine‑tuning with SFT and DPO, evaluation, and model compression for resource‑constrained scenarios.
This step‑by‑step guide explains how to use Alibaba Cloud's MaxCompute, DataWorks, and the AI platform PAI to build a custom dataset, fine‑tune the DeepSeek‑R1 distilled model, and deploy the resulting model for practical applications.
This guide walks you through creating a RAG‑enhanced AI solution for education using Alibaba PAI, covering prerequisite setup, knowledge‑base construction with PAI‑Designer, model deployment, connection configuration, workflow assembly, and a side‑by‑side comparison of RAG versus non‑RAG answers.
Learn how to create a Retrieval‑Augmented Generation (RAG) system for medical applications using Alibaba's PAI platform, covering knowledge‑base construction with PAI‑Designer, template setup in PAI‑LangStudio, deployment of LLM and embedding models, vector database integration, and end‑to‑end workflow configuration.
This guide walks you through setting up Alibaba Cloud Milvus, configuring public access, deploying a RAG system via PAI, uploading a knowledge base, interacting with the model through the Web UI, and inspecting vector collections with Attu, all with step‑by‑step instructions and configuration details.
Learn how to create a Retrieval‑Augmented Generation (RAG) system for financial scenarios using Alibaba’s PAI platform—covering knowledge‑base construction with PAI‑Designer, template creation in PAI‑LangStudio, deployment of LLM and embedding models, and linking vector stores for accurate, context‑aware answers.
This article introduces the Alibaba Cloud PAI platform for large language model (LLM) fine‑tuning, covering model‑training pipelines, performance‑cost trade‑offs, retrieval‑augmented generation, fine‑tuning methods such as full‑parameter, LoRA and QLoRA, model selection, data preparation, evaluation, and real‑world deployment examples.
This guide explains how to use Alibaba Cloud PAI to prepare datasets, select open‑source or fine‑tuned models, create evaluation tasks, configure resources, view detailed metrics such as ROUGE and BLEU, and compare results across multiple models for both custom and public datasets.
This guide explains how to integrate Milvus vector search with Alibaba Cloud PAI to create a Retrieval‑Augmented Generation (RAG) solution, covering background, prerequisites, deployment steps, configuration parameters, and practical usage through the Web UI.
This comprehensive guide walks AI developers through building a Retrieval‑Augmented Generation (RAG) chatbot on Alibaba Cloud PAI, covering architecture, vector store setup, model deployment, knowledge ingestion, multi‑modal retrieval, fusion, re‑ranking, prompt design, and end‑to‑end configuration with code examples.
This guide walks AI developers through downloading the Mixtral 8x7B MOE large language model, fine‑tuning it with Swift or Deepspeed on Alibaba Cloud PAI‑DSW, testing inference with Transformers, and finally deploying the tuned model as an online service using PAI‑EAS.
This guide walks you through preparing the environment, downloading Qwen‑72B‑Chat, performing LoRA fine‑tuning on PAI‑DSW, merging weights, and deploying the model for offline inference, web UI, API, and PAI SDK services on Alibaba Cloud.
This guide walks you through setting up Alibaba Cloud PAI Lingjun resources, preparing Qwen‑7B/14B/72B models, preprocessing large‑scale WuDao data, configuring distributed training with Megatron‑LM, performing continued pre‑training and supervised fine‑tuning, and finally deploying the model as an online service via PAI‑EAS.
This guide walks you through using Alibaba Cloud's PAI platform to quickly fine‑tune Llama 2 with LoRA or full‑parameter methods, deploy the models as online inference services, and launch an interactive WebUI, covering preparation, data formatting, training jobs, and deployment details.
This guide walks you through using Meta's open‑source Llama 2 models on Alibaba Cloud's PAI platform, covering low‑code LoRA fine‑tuning, full‑parameter fine‑tuning with PAI‑DSW, and rapid WebUI deployment via PAI‑EAS, complete with step‑by‑step instructions, code snippets, and resource requirements.
