This guide walks you through preparing the environment, installing Docker, cloning the DeepSeek repository, running the model with Docker or Ollama for quick start, using the enterprise API, and deploying the same model on Alibaba Cloud's free Bailei service within minutes.
DeepSeek‑R1 excels at deep reasoning but lacks native structured output; this guide explains why structured output matters, outlines common API‑level techniques, and provides three practical solutions—using an auxiliary model with a LangChain chain, a LangGraph workflow, and a ReAct agent—complete with code snippets and JSON‑mode tips.
This article presents 17 practical prompt‑engineering strategies—ranging from zero‑shot and few‑shot prompting to role, style, and chain‑of‑thought methods—explaining their usage, ideal scenarios, and concrete examples to help you obtain higher‑quality responses from large language models.
This article reviews a new risk‑sensitive trading agent that combines reinforcement learning with large language models to extract stock recommendations and news‑based risk scores, describes the extended CVaR‑PPO algorithm, presents extensive experiments on the FNSPID dataset, and discusses the resulting performance gains and future work.
The article examines the end‑to‑end architecture for large‑model inference, detailing seven layers—from chip hardware and programming toolkits to deep‑learning frameworks, inference accelerators, model providers, compute platforms, application orchestration, and traffic management—highlighting key vendors, open‑source projects, and performance‑optimizing techniques.
This article explains how language models evolved from character‑level embeddings to word‑level and finally to sub‑word tokenizers, highlighting the efficiency, vocabulary coverage, and practical engineering challenges of sub‑word segmentation in modern AI systems.
JD’s Merchant Intelligent Assistant leverages a large‑language‑model‑based multi‑agent architecture to provide 24/7 e‑commerce support, detailing its evolution, planning techniques, online inference, evaluation methods, sample generation, and practical insights for scalable AI‑driven operations.
This article provides a comprehensive, step‑by‑step guide to training and fine‑tuning a miniature large‑language model called MiniMind, covering lightweight model design, open‑source training pipelines, required datasets, tokenizer options, and deployment via a web UI, all using PyTorch on modest hardware.
This guide walks you through deploying the DeepSeek‑R1 large‑language‑model inference service on Alibaba Cloud ACK One registered clusters using ACS GPU compute, covering model preparation, OSS storage setup, PersistentVolume configuration, arena‑based service deployment, and verification steps with concrete commands and parameters.
This article explains how to quickly deploy the full‑parameter DeepSeek‑R1 671B model in a multi‑node GPU‑enabled Kubernetes cluster on Alibaba Cloud ACK, covering prerequisites, model parallelism, vLLM‑Ray distributed deployment, service verification, and integration with Dify to build a private AI Q&A assistant.
This article introduces the DeepSeek family of models, explains the concepts of online search and deep reasoning, details the two‑phase training pipeline with data augmentation and reinforcement learning, and showcases practical experiments and deployment examples for the R1 and distilled variants.
This article explains what reasoning language models are, when they are needed, and reviews four main techniques— inference‑time scaling, pure reinforcement learning, combined SFT + RL, and distillation—illustrated with DeepSeek‑R1’s development, cost analysis, and low‑budget alternatives.
The X‑R1 open‑source framework demonstrates that a 0.5B language model can achieve rapid reasoning improvements and observable "Aha Moments" using reinforcement learning on a modest 4‑GPU setup, detailing its design, performance metrics, installation steps, and future roadmap.
The paper proposes a bidirectional optimization framework that fine‑tunes the low‑resource NLLB‑200 translation model with LoRA using data generated by ChatGPT, while also translating low‑resource prompts with NLLB before feeding them to LLMs, thereby improving multilingual translation quality yet requiring careful validation of noisy synthetic data.
This guide explains how to prepare an Alibaba Cloud GPU instance, install Docker and NVIDIA tools, pull or build a container image, and run the FP8‑quantized DeepSeek‑R1‑Distill‑Qwen‑32B model using vLLM and OpenWebUI for both offline and online inference.
This article details Didi's journey since early 2023 in building ChatBI, covering the evolution of BI platforms, the technical advances behind intelligent BI such as LLM‑driven NL2SQL, two main product paths, practical implementations, key challenges, and future directions for AI‑enhanced data analysis.
JD Advertising accelerates its generative‑recall recommendation system by integrating NVIDIA TensorRT‑LLM, which simplifies the pipeline, injects LLM knowledge, scales to billions of parameters, and delivers over five‑fold throughput gains, one‑fifth the cost, and significant CTR improvements in both recommendation and search.
This article explains how to design and implement an on‑premise enterprise knowledge base by covering data preprocessing, vector database selection, LLM integration, system architecture, security, deployment, testing, and cost‑control, providing practical code snippets and best‑practice recommendations.
This guide walks you through installing Ollama, pulling the open‑source DeepSeek R1 model, and using LangChain and Streamlit to create a locally hosted Retrieval‑Augmented Generation (RAG) system that can answer questions from uploaded PDFs without any cloud API.
This article explains how the key LLM parameters—Temperature, Top‑K, Top‑P, and MaxOutputTokens—affect randomness, creativity, candidate selection, and output length, and provides practical guidance on tuning them for different AI text generation tasks.
This article reviews the development of DeepSeek's Mixture-of-Experts (MoE) models, tracing their evolution from the original DeepSeekMoE V1 through V2 to V3, detailing architectural innovations such as fine‑grained expert segmentation, shared‑expert isolation, load‑balancing losses, device‑limited routing, and the shift from softmax to sigmoid gating.
This article presents a hybrid‑cloud solution that uses ACK Edge and KServe to dynamically allocate on‑premise and cloud GPU resources for large‑language‑model inference, addressing tidal traffic patterns, reducing costs, and ensuring high availability through elastic scaling and custom scheduling policies.
The JD Merchant Intelligent Assistant employs a large‑language‑model‑driven multi‑agent architecture with dynamic ReAct planning, enabling merchants to query and execute store operations in under a second with over 90 % decision accuracy, while reducing inference cost, hallucinations, and engineering effort across diverse e‑commerce tasks.
Learn step‑by‑step how to prepare a JD Cloud GPU instance, install GPU drivers, deploy Ollama, run DeepSeek‑R1 models, configure graphical clients like Chatbox on Windows and macOS, and optionally feed local data using AnythingLLM to build an offline knowledge base.
This article explains how to replicate Deepseek RI's slow‑thinking inference using the GRPO reinforcement‑learning algorithm on the Qwen2.5‑7B model in a free Colab notebook, covering the underlying COT concept, reward‑function design, data preparation, training configuration, and observed results.
The article reviews DeepSeek‑R1’s training methodology—including cold‑start data collection, multi‑stage RL fine‑tuning, SFT data generation, and model distillation—highlights its performance comparable to OpenAI‑o1‑1217, and discusses key contributions, reward design, successful experiments, and failed attempts.
The article explains that DeepSeek is an open‑source LLM that many sellers monetize by offering paid installation packages, outlines the model lineup and size options, and provides a step‑by‑step guide to install and run DeepSeek locally with Ollama and Open WebUI.
This guide introduces DeepSeek, outlines its cutting‑edge open‑source LLMs, and provides step‑by‑step instructions for accessing the admin backend, adding and configuring DeepSeek models, setting API endpoints and keys, and enabling frontend access.
The article surveys the rise of multi‑agent LLM systems, detailing how specialized agents collaborate on tasks such as travel planning, outlining their workflow, comparing them with single‑agent models, listing prominent frameworks, and discussing current challenges and research citations.
This guide explains how to deploy a production‑ready DeepSeek‑R1 inference service on Alibaba Cloud ACK using KServe, covering model preparation, storage configuration, service deployment, observability, autoscaling, model acceleration, gray‑release and GPU‑shared inference.
This guide walks you through preparing a Tencent Cloud account, creating a Cloud Studio workspace, installing Ollama, downloading and running the DeepSeek‑R1 large language model, interacting via terminal or API, and managing resources and model versions.
This article walks you through everything you need to know about DeepSeek R1—including its different model sizes, hardware requirements, installation tools like Ollama, LM Studio and Docker, and how to set up a visual interface with Open‑WebUI or Dify—for offline, private, and cost‑effective AI inference.
Retrieval‑Augmented Generation (RAG) using LangChain lets developers enhance large language models by embedding user queries, fetching relevant documents from a vector store, inserting the context into a prompt template, and generating concise, source‑grounded answers, offering low‑cost, up‑to‑date knowledge while reducing hallucinations and fine‑tuning expenses.
This article provides a comprehensive tutorial on locally deploying the full‑size DeepSeek R1 671B model using Ollama, covering dynamic quantization options, hardware specifications, detailed installation commands, configuration files, performance observations, and practical recommendations for consumer‑grade systems.
This article lists ten common misconceptions about prompt engineering, explains why each is flawed, and offers practical insights and strategies—such as using the CO‑STAR framework, tailoring prompts to specific models, keeping prompts concise, and continuously testing and refining—to help readers communicate effectively with large language models.
This article explains what DeepSeek is, how its open‑source large language model works, its unique multilingual training, free access, the DeepSeek‑Coder variant, and compares its capabilities and goals with ChatGPT, highlighting strengths, limitations, and market impact.
The paper introduces MILS, a training‑free multimodal iterative LLM solver that lets large language models perceive and generate across image, video, and audio domains, achieving new state‑of‑the‑art results without any task‑specific data or fine‑tuning.
This article introduces Alibaba Cloud's PAI Model Gallery, detailing the DeepSeek-V3 and DeepSeek‑R1 large language models, their architectures and parameters, and provides a step‑by‑step guide for one‑click deployment of these models and their distilled variants using vLLM or BladeLLM.
The article conducts a practical, step‑by‑step evaluation of Cursor, Windsurf, and GitHub Copilot’s multi‑file editing capabilities using a simple web‑chat bot, revealing that Cursor completes all required UI, storage, and application changes in a single interaction, while the others need two rounds, with Copilot showing notable improvement on a retest.
This article explains how NebulaGraph’s large‑scale graph database can be used to construct real‑time risk‑control knowledge graphs, describes practical applications such as community detection and path analysis, and explores how large language models enhance graph queries through Text‑to‑GQL, agents, exploration chains, and semi‑structured knowledge extraction.
This article presents Tencent's OlaChat intelligent BI platform, detailing its evolution from traditional to intelligent BI, the impact of large language models on data analytics, the system's multi‑task dialogue, metadata retrieval enhancements, Text2SQL solutions, and real‑world deployment insights.
This article details a step‑by‑step design of a RAG‑based intelligent Q&A assistant for the DeWu Open Platform, covering background, RAG fundamentals, system architecture, technology selection, prompt engineering with CO‑STAR, data preprocessing, vector store setup, LangChain.js implementation, similarity search, runnable chaining, debugging, and future prospects.
Since the launch of ChatGPT, 58.com has built a Model‑as‑a‑Service platform called LingXi that trains and serves domain‑specific large language models, supports over a hundred internal scenarios with daily inference exceeding ten million calls, and continuously improves performance through quantization, GPU optimization, model miniaturization, and advanced AI applications such as interview assistants, voice agents, and RAG‑enabled agents.
With the rise of LLM‑based generative AI, this article examines the emerging GenBI (Generative Business Intelligence) paradigm, detailing why self‑serving analytics are needed, the progress of Text‑to‑SQL, an LLM‑driven agent architecture, practical AWS Bedrock implementation, technical choices, lessons learned, and future outlook.
This article provides a comprehensive, step‑by‑step guide to Microsoft AutoGen 0.4, explaining its layered architecture, core concepts such as Agent, Runtime, and Agent ID, and demonstrating both a simple Hello‑World multi‑agent example and an AI‑enabled agent with full Python code snippets.
This article presents NVIDIA's NeMo technology stack for end‑to‑end large language model (LLM) training, covering the full software pipeline, model alignment with reinforcement learning from human feedback (RLHF), performance optimizations such as model parallelism, FP8, TensorRT‑LLM inference, dynamic load balancing, and future research directions.
Eino is a Golang‑based, open‑source framework that streamlines the full devops lifecycle of large‑model applications by providing stable, strongly‑typed components, graph‑based orchestration, built‑in tooling, and extensible architecture to help developers quickly build reliable AI services.
This article explains how embeddings convert basic character indices into high‑dimensional vectors, describes their training via gradient descent, introduces the embedding matrix, and shows how these vectors enable modern language models to capture semantic relationships and be reused across tasks.
This article analyzes Xiaohongshu’s content recommendation system by reviewing two official papers, detailing the NoteLLM framework for interest discovery and the Sliding Spectrum Decomposition (SSD) method for diversified recommendations, and explaining their underlying models, loss functions, and experimental results.
This article explains how to reduce inference cost and improve performance for large language models on Alibaba Cloud ACK by using Knative's request‑based autoscaling, custom ResourcePolicy priority scheduling, and Fluid data‑caching to achieve elastic scaling, resource pre‑emption, and faster model loading.
The article explains how AI agents can move beyond single‑turn responses by using two enhanced reflection strategies—external tool validation and multi‑path optimization (LATS)—to iteratively improve output quality, reliability, and applicability in complex, high‑stakes tasks.
This article reviews the evolution of Multi‑Token Prediction (MTP) techniques—from early blockwise parallel decoding to Meta's and DeepSeek's implementations—explaining their architectures, training and inference workflows, and the speed‑up gains they offer for large language models.
This guide explains how to create a Retrieval‑Augmented Generation (RAG) solution for education on Alibaba PAI, covering knowledge‑base construction with PAI‑Designer, model deployment, connection setup in LangStudio, workflow configuration, online deployment, and a legal‑domain case comparison that highlights RAG's accuracy benefits.
We built an LLM‑powered system for Bilibili that automatically creates ad titles from user keywords, employing fluency, style, and quality classifiers, mixed domain data cleaning, and alignment methods such as SFT, DPO and KTO, resulting in a product that now generates about ten percent of daily titles and drives significant ad spend.
This article explains how generative recommendation systems powered by large language models simplify the recommendation pipeline, integrate world knowledge, benefit from scaling laws, and require specialized engineering optimizations such as TensorRT‑LLM deployment, inference acceleration, and hybrid model strategies to achieve low latency and high throughput in real‑world e‑commerce scenarios.
This article explains how large language models reshape recommendation systems by simplifying pipelines, integrating world knowledge, and leveraging scaling laws, and details the engineering steps for deploying generative recall models—including product encoding, user prompting, model training, TensorRT‑LLM optimization, and continuous performance improvements.
This article explains how a simple neural network trained for classification can be adapted to generate natural language by expanding its output layer, encoding characters as numbers, using a sliding‑window context, and recursively predicting the next token, illustrating each step with diagrams and concrete examples.
This article shares practical experiments and insights on using Qwen2ForSequenceClassification for short‑ and long‑text sentiment tasks, compares it with BERT, outlines improvement strategies such as generative fine‑tuning and LoRA, and provides end‑to‑end code, training details, and evaluation results.
This guide explains why structured JSON output from LLMs is essential, walks through installing and running Ollama, and provides a complete Java Spring Boot implementation—including POJOs, service code, and best‑practice tips—to retrieve AI‑generated data in a reliable, parsable format.
This report details how large language models (LLMs) were integrated into Tencent's search advertising pipeline—from early extraction‑distillation experiments in 2023 to a 2024 end‑to‑end generative recall architecture—showing significant improvements in relevance, diversity, and revenue through knowledge injection, supervised fine‑tuning, constrained beam‑search decoding, and high‑performance inference services.
A technical walkthrough shows how to use vLLM to load multiple LoRA adapters for role‑playing LLMs, analyzes the massive GPU and labor costs of naïve deployment, and presents a hosted multi‑LoRA platform as a cost‑effective solution.
This article provides a comprehensive overview of LLM-based Text-to-SQL technology, covering its background, evolution, challenges, various LLM-driven methods, benchmark datasets, evaluation metrics, and future research directions to guide researchers and practitioners in advancing natural language interfaces for databases.
The author experiments with GitHub Copilot Workspace to automatically generate a WeChat mini‑program for family library management, documents the prompting process, code generation, bug fixes, UI tweaks, and reflects on the broader impact of AI‑driven development on programmers' future jobs.
This article presents the Tencent PCG OlaChat team's research and practice in intelligent data analysis, covering the DIKW model, evolution of BI platforms, the impact of large language models, challenges of third‑generation data products, detailed product features, agent architecture, system design, and related academic publications.
This article reviews ten mainstream LLM deployment solutions—including WebLLM, LM Studio, Ollama, vLLM, LightLLM, OpenLLM, HuggingFace TGI, GPT4ALL, llama.cpp, and Triton Inference Server—detailing their technical characteristics, strengths, drawbacks, and example deployment workflows for both personal and enterprise environments.
The paper describes building a Retrieval‑Augmented Generation assistant for the Dewu Open Platform that leverages GPT‑4o‑mini, OpenAI embeddings, Milvus vector store, and LangChain.js to semantically retrieve API documentation, structure user queries, and generate accurate, JSON‑formatted answers, thereby reducing manual support and hallucinations.
This article explains the Evaluator‑Optimizer reflection pattern for LLM agents, shows how it can improve output quality in single‑ or multi‑agent tasks, and provides a step‑by‑step PydanticAI implementation with code examples and practical usage tips.
Salesforce’s Agentforce 2.0 positions AI agents as a limitless digital labor platform, reshaping enterprise software with a new agent‑first model, consumption‑based pricing, and real‑world case studies that illustrate productivity gains, cost reductions, and strategic advantages in today’s AI‑driven market.
This guide explains how to use Alibaba Cloud Service Mesh (ASM) to add infrastructure‑level observability for large language model (LLM) traffic, covering custom access‑log fields, new Prometheus metrics for token usage, and adding model dimensions to native Istio metrics, with step‑by‑step commands and configuration examples.
This article explains the Orchestrator‑Workers pattern from Anthropic’s “Build effective agents”, compares it with routing and parallel modes, distinguishes it from Supervisor agents, and provides a step‑by‑step Python implementation using Pydantic AI, including model definitions, prompts, orchestration logic, worker execution, and a test example.
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.
In 2025, large language models will see three key trends—agents becoming pervasive in daily life and industry, the emergence of efficient small models for edge and specialized tasks, and the integration of multimodal capabilities that combine text, images, and audio to enable more natural human‑machine interaction.
The article outlines the rapid progress of LLM agents in 2024 while highlighting key difficulties in planning capabilities, evaluation methods, dataset generation, and metric design, and suggests practical combinations and product‑level enhancements to improve efficiency, accuracy, and usability.
This article evaluates the Cursor AI coding assistant, describing its context‑aware indexing, Composer panel, and code‑generation features, while outlining practical scenarios such as Q&A, test creation, language conversion, and prototype development, and discussing its inherent randomness, domain‑knowledge gaps, and best‑practice recommendations for developers.
This article outlines a comprehensive approach to identifying and resolving both error‑related and slow‑request problems in online systems by leveraging trace data, log correlation, method‑stack analysis, unified entity models, and large‑language‑model assistance to accelerate root‑cause diagnosis.
This tutorial walks through building semantic routing and parallel execution patterns for LLM agents using the lightweight PydanticAI framework, providing step‑by‑step code, example configurations, and practical observations to help developers create flexible AI‑driven workflows.
This issue surveys recent AI developments, covering OpenAI's renewed robot program, the ModernBERT encoder upgrade, spatial reasoning advances in multimodal models, automated environment generation for LLM agents, and a novel GNN‑LLM approach for label‑free node classification.
This article examines the current challenges of enterprise data analysis platforms, outlines three traditional analysis modes, and presents a next‑generation solution that combines Headless BI’s semantic modeling with Chat BI’s large‑language‑model interaction to deliver a more efficient, secure, and user‑friendly analytics experience.
This article summarizes ByteDance’s quality‑efficiency expert Zhao Liang’s QCon presentation on using large language models to automatically generate unit tests, covering pain points, goals, data‑quality engineering, model‑analysis fusion, architecture, evaluation metrics, and future plans for a production‑grade testing tool.
Anthropic argues that building effective AI agents should start with simple, enhanced LLMs and only add workflow or autonomous agent complexity when necessary, emphasizing a “Less is More” approach to reduce latency, cost, and debugging difficulty.
This article outlines a practical, end‑to‑end approach for identifying and resolving both error‑related and slow‑request problems in online systems by leveraging trace links, correlated logs, entity relationships, and large‑language‑model‑driven analysis to achieve rapid root‑cause isolation.
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.
The Qwen2.5 Technical Report introduces a new series of large language models with up to 72 B parameters, expanded pre‑training data to 18 trillion tokens, advanced supervised fine‑tuning and reinforcement learning pipelines, and demonstrates strong performance across comprehension, reasoning, coding, and long‑context tasks.
This article chronicles the development of 1688’s AI buyer assistant “Yuanbao”, detailing why an e‑commerce AI assistant is needed, its functional design, MVP constraints, the shift to a data‑driven 2.0 version, future prospects, and a Q&A, providing practical insights for AI product rollout in B‑to‑C platforms.
The article reviews the STAR framework, a training‑free recommendation approach that leverages large language model embeddings and collaborative co‑occurrence scores to retrieve and rank items, and evaluates its performance, hyper‑parameter effects, and ablation studies against existing LLM‑based recommender methods.
Midscene.js leverages multimodal AI to simplify web UI automation by providing .ai, .aiQuery and .aiAssert methods, supporting JavaScript and YAML integrations, a Chrome extension, and detailed cost analysis while acknowledging latency, interaction limits, and prompt‑engineering challenges.
This article compares five LangChain text splitters—CharacterTextSplitter, RecursiveCharacterTextSplitter, TokenTextSplitter, MarkdownTextSplitter, and LatexTextSplitter—by examining their principles, pros and cons, and ideal use cases, helping developers select the most suitable splitter for their frontend large‑model applications.
This article examines the rapid growth of AI agents, outlines typical privacy and security challenges such as data leakage, model attacks, and prompt injection, and proposes comprehensive governance and technical measures to mitigate these risks in enterprise deployments.
This article examines the rapid growth of AI agents, outlines their core components and classifications, analyzes a wide range of privacy and security threats—including data leakage, prompt injection, jailbreak, backdoor, hallucination, and memory attacks—and proposes practical governance measures to mitigate these risks.
This article surveys the post‑training pipelines of major open‑source large language models released this year, detailing their alignment algorithms, data synthesis, reward modeling, DPO/GRPO variants, long‑context handling, tool use, and model‑averaging techniques, and highlights emerging trends such as data‑centric pipelines and iterative weak‑to‑strong alignment.
This guide walks you through creating a Retrieval‑Augmented Generation (RAG) question‑answering application by integrating Alibaba Cloud Milvus vector search, DashScope embedding models, and PAI EAS LLM services, covering prerequisites, service deployment, configuration, Python code setup, and execution steps.
This issue reviews OpenAI’s twelve‑day product launch, LeCun’s surprising AGI timeline, a new Chinese text‑to‑image capability from ByteDance’s Doubao, and the open‑source EchoMimic V2 digital‑human system, highlighting trends, technical details, and industry reactions across the AI landscape.
This comprehensive study evaluates various components of Retrieval‑Augmented Generation pipelines—including query classification, chunking, embedding models, vector databases, retrieval, re‑ranking, summarization, and generator fine‑tuning—identifies optimal configurations, and proposes best‑practice guidelines for both performance‑maximizing and efficiency‑balanced RAG systems.
Sequoia Capital’s 2025 AI outlook argues that the industry is shifting from early excitement and massive spending to a phase focused on differentiated large‑model providers, AI‑search as a killer app, and a more disciplined, ROI‑driven investment climate.
Andrew Ng highlights that large language models are shifting from optimizing simple question‑answering for consumers to supporting complex agentic workflows, including tool usage, computer interaction, and multi‑agent collaboration, signaling a major evolution in AI capabilities.
AutoGen, Microsoft’s advanced multi‑agent framework, lets developers quickly assemble collaborative LLM agents—supporting chat, tool use, and hierarchical group chats—through concise Python code, with examples ranging from simple two‑agent dialogues to complex three‑agent reporting pipelines, while outlining its strengths, limitations, and upcoming v0.4 enhancements.
Airbnb’s Automation Platform v2 replaces the rigid, workflow‑driven architecture of v1 with an LLM‑centric design that orchestrates context gathering, chain‑of‑thought reasoning, tool execution, and guardrails, enabling more natural, scalable, and safe conversational AI while preserving the reliability of traditional workflows.
The article explains how tokenization mismatches and biased next‑token prediction cause LLMs to miscount letters in “Strawberry” and incorrectly compare 9.9 versus 9.11, and shows that step‑by‑step Chain‑of‑Thought prompting with reason‑first output dramatically improves accuracy.
By breaking iterable inputs into parallel LLM calls and batching 20 items across three languages within Dify’s platform limits, the workflow achieves 43‑64% average runtime reductions and markedly higher success rates, demonstrating that request‑level concurrency dramatically improves throughput for large‑scale translation tasks.
This article presents the YiNian LLM framework, detailing how KVCache, prefill/decoding separation, continuous batching, PageAttention, and multi‑hardware scheduling are used to speed up large language model inference while managing GPU memory and latency.
