UCloud is a leading neutral cloud provider in China, developing its own IaaS, PaaS, AI service platform, and big data exchange platform, and delivering comprehensive industry solutions for public, private, hybrid, and dedicated clouds.
This guide walks through training custom locomotion policies for the $399 Microduck bipedal robot using reinforcement learning in MuJoCo simulation on UCloud GPU instances, covering environment setup, walking and running policy training, ONNX export, simulation validation, and Hugging Face deployment.
The article demonstrates how the AstraFlow Xingtu client automates systematic literature reviews by generating database-specific queries, retrieving and deduplicating records, applying evidence gates to prevent premature conclusions, and delivering structured outputs including evidence matrices and Zotero-ready citations, with human oversight at critical decision points.
The article examines how the ucloud-dsh-plugin integrates DeepSeek Harness to turn natural‑language requirements into structured cloud‑resource actions, lowering the entry barrier for AI developers while highlighting necessary operational safeguards, permission checks, and the limits of automated deployment.
The article analyzes how AI agents are moving from code generation to full cloud operations, identifies shortcomings of current cloud consoles, proposes an Agent‑Ready CLI with comprehensive resource coverage, structured JSON output, OAuth authentication, and best‑practice defaults, and demonstrates its use through three practical deployment scenarios.
The article analyzes why relying on a single AI model for all programming tasks leads to cost waste, quality issues, and missing engineering controls, and proposes a concrete multi‑model agent architecture, configuration guidelines, verification processes, security policies, and a phased rollout plan to achieve cost‑effective, reliable AI‑assisted development.
The article explains why enterprises need separate fee permissions on the AstraFlow platform, compares system and custom IAM policies, presents a single Deny policy that blocks 20 billing‑related APIs, and provides a three‑step guide to create, bind, and verify the policy, showing the resulting access matrix.
This article introduces parameter‑efficient fine‑tuning (PEFT) techniques—including additive adapters, soft‑prompt methods, selection‑based BitFit, and re‑parameterization approaches like LoRA and AdaLoRA—explains their architectures, experimental results, and provides end‑to‑end code for fine‑tuning ChatGLM2‑6B on a Chinese medical QA dataset.
This article walks through constructing a domain‑specific, soul‑healing chatbot using LangChain and Llama 2, comparing fine‑tuning versus external knowledge bases, detailing environment setup, data loading, text splitting, embedding with a Chinese model, vector store creation, prompt engineering, inference, and optimization strategies.
This article explores Meta's Llama 2 large language model—its performance, expanded training data, architectural details, evaluation results, RLHF fine‑tuning process, and step‑by‑step deployment on UCloud UK8S using Docker and Kubernetes—providing a comprehensive guide for AI practitioners.
This article reviews the architectures, training methods, and key characteristics of major open‑source large language models such as BERT, GPT, T5, LLaMA and ChatGLM, and then provides a step‑by‑step tutorial for deploying ChatGLM2‑6B on UCloud's UK8S platform using UFS storage, Kubernetes manifests, and command‑line tools.
