Google’s Gemma 3, a lightweight open‑source model with up to 27 billion parameters, offers multimodal input, 128K token context, and broad language support, outperforming leading rivals on single‑GPU benchmarks and providing flexible deployment options for developers and researchers alike.
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 article provides a comprehensive analysis of AI agents, covering their historical origins, three‑layer technology stack, market size forecasts, evolution from training to inference, interaction modes, core modules, and the full industry chain from infrastructure providers to downstream applications.
The article outlines three practical approaches to get the most out of DeepSeek—using it as a conversational assistant, integrating its API to power AI tools such as the Chrome immersive‑translation plugin, and leveraging it for AI‑assisted programming—while comparing the V3 and R1 models and offering concrete configuration steps.
Alibaba has released the open‑source Qwen QwQ‑32B model, a 32‑billion‑parameter LLM that matches DeepSeek‑R1's performance while being deployable on consumer‑grade GPUs, and the announcement is accompanied by extensive promotional offers for AI‑related products and services.
This issue reviews the hype around China’s Manus AI Agent and its invitation‑code controversy, critiques OpenAI’s GPT‑4.5 performance versus DeepSeek, showcases industry solutions using AI agents, and introduces the LightThinker method for dynamically compressing LLM inference chains to boost efficiency.
Alibaba’s new QwQ‑32B large‑language model, with 32 billion parameters, delivers performance comparable to or surpassing the 671‑billion‑parameter DeepSeek‑R1 across math, coding, and general benchmarks, and is available via HuggingFace, ModelScope, and a DashScope API demo with example Python code.
This article explains the technical principles behind Function Call and ReAct in large language models, provides code samples, compares their strengths and limitations, and illustrates each approach with practical scenarios such as smart customer service and financial analysis assistants.
This guide walks you through the end‑to‑end process of deploying the open‑source QwQ‑32B inference model on Volcengine's cloud platform, covering GPU ECS selection, VKE cluster creation, continuous delivery CP setup, vLLM service launch, and API gateway exposure.
QwQ-32B, Alibaba’s new inference‑optimized large language model built on the Qwen2.5 architecture, outperforms DeepSeek‑R1 across math reasoning, code generation, and safety benchmarks while requiring only 24 GB vRAM, and the article provides detailed performance data, resource‑efficiency analysis, and step‑by‑step Java and Ollama integration instructions.
Alibaba’s newly released 32‑billion‑parameter QwQ‑32B model matches the performance of 670‑billion‑parameter rivals like DeepSeek‑R1, integrates agent‑based reasoning, runs on consumer hardware, and has sparked strong open‑source community adoption, as shown by benchmark results and download statistics.
The QwQ-32B model, released by Alibaba Cloud, delivers DeepSeek‑R1‑level results with only 32 billion parameters, offers integrated agent capabilities, is open‑source under Apache 2.0, and can be quickly deployed locally via Ollama or integrated into Java applications using Spring AI.
Alibaba has open‑sourced its new QwQ‑32B inference model, a 32.5‑billion‑parameter transformer that rivals top models like DeepSeek‑R1 and o1‑mini, features integrated agent abilities for tool use and critical thinking, and offers a low inference barrier with extensive technical specifications and RL‑based training details.
The author experiments with reinforcement‑learning‑from‑human‑feedback on a 0.5B Qwen instruct model using Logic‑RL and Open‑R1, discovers that reward mis‑design and curriculum learning cause the model to produce overly short or incorrect reasoning chains on knight‑and‑knave puzzles, and analyses the underlying causes.
The article analyzes DeepSeek‑R1’s low‑cost inference architecture, Chinese language optimizations, novel prompt‑engineering techniques, and the practical challenges of deploying large domestic models, offering insights into vertical AI applications and the evolving open‑source ecosystem in China.
The article analyzes DeepSeek’s latest V3 conversational model and R1 inference model, detailing their MoE architecture, training on H800 GPUs costing about $558 k, comparing compute expenses to Meta’s Llama 3.1, and showing that their API pricing is roughly one‑tenth of GPT‑4o for dialogue and one‑twentieth of OpenAI o1 for inference.
The article provides an in‑depth overview of DeepSeek’s large language model, detailing its mixture‑of‑experts and Transformer foundations, novel attention mechanisms, load‑balancing, multi‑token prediction, FP8 mixed‑precision training, and various training regimes such as knowledge distillation and reinforcement learning.
Thirteen engineers praise DeepSeek’s open‑source, reinforcement‑learning‑driven architecture—using FP8 storage and SFT‑free training—to deliver GPT‑4‑level reasoning at one‑twentieth the cost, enabling single‑GPU deployment, lowering barriers for academia and startups, and prompting notable market reactions that could democratize advanced AI.
This article provides an in‑depth overview of DeepSeek, covering its core multimodal and multilingual features, long‑context capabilities, domain optimizations, security, main functions, diverse application scenarios, and practical usage via web interface or API integration.
This article summarizes a Peking University lecture on DeepSeek‑R1, detailing its core concepts, advantages, and historical significance, then explains the underlying mechanisms of large‑model AI and AIGC tools, and finally offers practical guidance for selecting and efficiently applying AI solutions.
GraphRAG, the next generation of Retrieval‑Augmented Generation, combines large language models, knowledge graphs, and graph databases to overcome traditional RAG’s knowledge gaps, hallucinations, and context limitations, and the article reviews its architecture, core modules, a recent 2025 paper, and six notable open‑source implementations.
The article details the launch of Grok 3 by Elon Musk’s xAI, highlighting its massive GPU infrastructure, benchmark dominance over GPT‑4o, multiple model variants, pricing for Premium+ users, upcoming API and voice features, and the team’s plan to open‑source Grok 2 once the new model stabilises.
This article explains how to integrate DeepSeek's large language models into a Spring AI application, covering model selection, API key configuration, URL setup, dependency inclusion, and providing complete Java code examples for both synchronous and streaming chat interactions.
This guide explains how to download, quantize, and run the full‑size 671‑billion‑parameter DeepSeek R1 model on local hardware using Ollama, covering model selection, hardware requirements, step‑by‑step deployment commands, optional web UI setup, performance observations, and practical recommendations.
This tutorial series provides a step‑by‑step technical guide to training, deploying, and fine‑tuning the MiniDeepSeek V3+R1 large language model, covering model performance, open‑source details, API usage, parameter explanation, multi‑turn chatbot construction, function calling, integration with Open WebUI, GraphRAG, Swarm, and various deployment and optimization techniques.
This note surveys four open‑source reproductions of DeepSeek R1/R1‑zero reinforcement‑learning pipelines, re‑implements their training on math and logic datasets using Qwen‑based models, shows that format‑plus‑accuracy rewards improve long‑chain reasoning though stability and scaling remain challenges, and outlines future directions for large‑scale RL and business deployment.
Elon Musk’s xAI introduced the Grok 3 family—trained on roughly 200,000 GPUs and offered in standard, mini and Reasoning versions—that claims top‑slot performance on math, science and coding benchmarks, outpacing Google Gemini, DeepSeek V3, Claude and OpenAI GPT‑4o, while pricing starts at $30 per month and drawing both praise for its speed and criticism for lingering hallucinations and ethical sensitivities.
This guide walks you through using Alibaba Cloud's PAI‑RAG platform to deploy a DeepSeek large‑language‑model assistant that combines real‑time web search with an enterprise knowledge‑base, covering deployment, network‑search configuration, testing, and advanced enterprise features.
This article explains how to obtain a DeepSeek API key, configure Spring AI with the appropriate base URL and model, and provides Java code examples for both synchronous and streaming chat interactions using the DeepSeek large‑language model.
This guide explains how to deploy large‑scale AI models such as Ollama and DeepSeek‑R1 on a Kubernetes 1.30 cluster, covering hardware requirements, PVC and deployment manifests, service exposure, image pulling, verification steps, API access, and monitoring with Prometheus and Grafana.
JD’s advertising R&D team created the JDZunTong Intelligent Assistant by engineering a modular Agent platform that combines advanced Retrieval‑Augmented Generation (RAG 1.0 → 2.0) and Function‑Call capabilities, a visual designer, custom tool registration, and a native Python workflow engine to deliver intelligent customer service, data queries, and ad creation for merchants.
This article introduces Plug, a unified front‑end tool that combines non‑intrusive interface capture, flexible mocking, and large‑model assistance to streamline API development for both mini‑programs and PC, while addressing HTTPS proxy challenges and performance considerations.
VARGPT is a novel multimodal large language model that unifies visual understanding and autoregressive image generation within a single architecture, extending LLaVA with next‑token and next‑scale prediction, trained through three staged data‑curated phases and achieving superior performance on numerous vision‑language benchmarks.
This article explains the FlashAttention algorithm, its memory‑efficient tiling and recomputation techniques, and how enabling the flash_attn flag dramatically speeds up Qwen‑series large models while outlining hardware, software requirements and potential trade‑offs.
This article explains how to integrate DeepSeek's large language models—both the chat‑oriented deepseek‑chat and the reasoning‑focused deepseek‑reasoner—into a Spring AI application, covering API key setup, base‑URL configuration, model selection, and providing full code examples for dependency, configuration, and a simple chat controller.
This article demystifies DeepSeek R1 by explaining key concepts such as online search integration and the R1 model, detailing its two‑phase training pipeline, core techniques like iterative data enhancement, and showcases practical reproductions, benchmark tests, and deployment examples for AI developers.
This guide walks you through configuring Tencent Cloud AI Code Assistant to use DeepSeek models—either via the DeepSeek public API or a locally‑deployed Ollama instance—covering prerequisites, step‑by‑step setup, required hardware, and command‑line examples.
This article compiles a curated reading list of foundational and recent research papers—from the original Transformer to chain‑of‑thought, mixture‑of‑experts, and reinforcement‑learning studies—that together explain the breakthroughs behind DeepSeek‑R1 and guide readers through the technical evolution of modern large language models.
The article examines DeepSeek‑V3’s low‑cost training using 2048 H800 GPUs, explains how knowledge distillation and high‑quality data improve efficiency, discusses expert concerns about training on AI‑generated content, and outlines the limitations and ceiling effect of distillation techniques.
The article outlines how to build a scalable, modular AI code‑review agent using LangChain, detailing stages from naive prompting to advanced prompt engineering, architecture with six core modules, strategies to curb hallucinations, improve reliability, performance, and human‑AI collaboration, and future RAG integration.
The article outlines eight distinct enterprise strategies for adopting DeepSeek, categorizing them by model maturity, available data types, and specific business challenges, and maps these approaches onto four capability tiers—from basic compliance requirements to advanced multimodal, low‑cost solutions.
This article presents the ORLM intelligent decision‑making large model, detailing how real‑world decision problems are formalized and solved, the training difficulties and data synthesis methods, the transition from academic research to commercial platforms, and future technical improvement plans.
This article breaks down the hardware and software expenses required to deploy a complete DeepSeek large‑language model on‑premises, revealing a total cost of roughly $110,000 and explaining why such an investment is prohibitive for most individual developers but may be justified for well‑funded research or corporate projects.
This guide presents seven practical prompt engineering tricks—ranging from precise requirement definition and contextual background provision to step‑by‑step decomposition, keyword tagging, iterative follow‑ups, tone/style adjustments, and model switching—that dramatically improve the relevance and quality of DeepSeek’s responses for work, learning, and creative tasks.
The article presents Ola, an open‑source full‑modal LLM that uses progressive modality alignment to jointly process text, images, video, and audio, and demonstrates competitive performance across image, video, and audio benchmarks, surpassing many specialized models.
This article examines DeepSeek's advanced Transformer‑based architecture, dynamic routing, MoE system, multi‑stage training, efficient inference, multimodal capabilities, real‑world applications, technical challenges, and future prospects, providing a comprehensive technical analysis of the model's strengths and limitations.
The article evaluates DeepSeek‑R1’s strong reasoning, high performance, native Chinese training and low cost, then explores how such large language models can reshape B2C and B2B services, propose a new “intelligent data store” architecture, and outline comprehensive organizational and strategic changes enterprises must adopt to thrive in the AI era.
This article provides a comprehensive analysis of DeepSeek's large‑language‑model technology, covering the company's background, model capabilities, remarkably low training and inference costs, and the core architectural and algorithmic innovations such as MoE, MLA attention, FP8 mixed‑precision, and the DualPipe pipeline that enable efficient large‑scale AI deployment.
This step‑by‑step guide shows non‑technical users how to install Ollama, pull the desired DeepSeek‑R1 model version, run it from the terminal, and optionally connect the free Chatbox desktop client for a visual chat interface, all without external network dependencies.
This article presents ten detailed techniques for effectively using DeepSeek's large language models—including mode selection, model comparisons, knowledge updates, prompt engineering, RAG, file uploads, API access, and open‑source resources—while offering concrete examples and code snippets for each feature.
DeepSeek’s debut marks a watershed for China’s AI, offering low‑cost, Chinese‑native reasoning that outperforms foreign models and prompting enterprises to restructure development around demand‑engineering, AI‑assisted low‑code, intelligent data stores, and a shift from “how to code” to “why to code” across a three‑phase transformation roadmap.
DeepSeek‑R1, an open‑source inference model released under the MIT license, matches or surpasses OpenAI’s O1 on math, coding, and reasoning benchmarks, offers multiple scaled versions, runs at lightning speed, and is rapidly adopted worldwide, signaling a shift toward more accessible, high‑performance AI.
This article introduces ChatBI, NetEase’s AI‑driven BI solution that combines large‑model capabilities with traditional data analytics, detailing its product features, AI‑enabled opportunities and challenges, the underlying NL2SQL model, technical architecture, performance optimizations such as materialized views, open APIs, and several enterprise deployment cases.
The article examines current limitations of AI agents in planning and complex reasoning, critiques existing methods like COT/TOT and ReAct, and proposes practical strategies—including combined COT‑Reflection approaches, structured memory algorithms, and white‑box interaction designs—to enhance agent performance within the DataFun knowledge map framework.
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.
DeepSeek‑R1 achieved a 1357‑point Arena score, ranking third overall and tying OpenAI o1 for first in StyleCtrl, while its open‑source MIT‑licensed release—including distilled variants—and low‑cost API service aim to democratize advanced AI inference for developers worldwide.
This article explores how AI transforms next‑generation sales by detailing project background and goals, core technologies such as efficient sample generation, model training and evaluation, system deployment impact, practical case studies, challenges, solutions, and future directions across multiple industries.
KwaiCoder-23BA4-v1 is a 23B wide MoE code‑completion model that achieves state‑of‑the‑art performance on HumanEval, BigCodeBench and Fill‑in‑Middle benchmarks by using high‑quality data, a cost‑effective training pipeline that combines model pruning, knowledge distillation and fine‑grained merging, and extensive ablation studies.
DeepSeek‑R1’s open‑source series demonstrates that reinforcement‑learning‑only training can match top‑tier models like OpenAI‑o1, while a small amount of SFT further improves readability; the article dissects its technical report, training pipeline, reward design, distillation strategy, benchmark outcomes, and remaining challenges.
Faced with billions of legal documents and the shortcomings of keyword search, Chinese legal professionals are turning to the AI‑powered YuanDian WenDa engine, which leverages Baidu's Wenxin model, a structured legal database, and prompt‑engineering to deliver trustworthy, citation‑rich answers and rapid research reports.
Shanghai AI Laboratory’s InternLM 3.0 upgrade demonstrates that a refined 4 TB token dataset can boost a large‑language model’s performance beyond that of open‑source peers trained on 18 TB, cutting training cost by over 75% while merging regular dialogue with deep reasoning capabilities.
Shanghai AI Laboratory’s InternLM 3.0 upgrade demonstrates that refining data quality—measured as intelligence‑per‑token—can replace massive datasets, achieving higher reasoning and dialogue capabilities with just 4 TB of tokens, cutting training cost by over 75 % while approaching GPT‑4‑level performance.
InternLM 3.0 (InternLM‑3) upgrades the Shusheng‑PuYu model by refining data to boost "thinking density", using only 4 TB of tokens to surpass peer open‑source models, cutting training cost by over 75% while merging ordinary dialogue with deep reasoning capabilities.
This guide explains how to use Alibaba Cloud Function Compute together with the Baileian large‑model platform to create a highly available, cloud‑native AI summarization service that automatically extracts key information from massive documents.
This guide walks through preparing a XiaoAI speaker, selecting a free LLM service, creating an API key, installing Docker, running the MiGPT server, and configuring the speaker to query the chosen large language model.
This article walks Java developers through the fundamentals of Retrieval‑Augmented Generation (RAG), explains the LangChain4j framework, compares large‑model development with traditional Java coding, and provides step‑by‑step code examples for environment setup, document splitting, embedding, vector‑store operations, and LLM interaction.
This article explores large‑model capabilities through semantic‑vector theory, outlines business‑scenario focus, presents practical case studies such as AI customer‑service bots, and details prompt‑engineering techniques and optimization workflows to help practitioners effectively apply foundation models in real‑world tasks.
This article derives DeepSeek V3's training Model FLOPs Utilization (MFU) using publicly available data, showing an MFU of roughly 37%—about a 60% improvement over V2—and provides detailed formulas, parameter settings, and a reproducible Python script.
This article presents a systematic root‑cause diagnosis framework for online applications, covering how to identify and resolve both error ("wrong") and performance ("slow") problems using trace links, associated data, high‑quality observability, and large‑language‑model‑driven intelligence.
This article explains how large‑model capabilities combined with metric and tag platforms can be used to build intelligent data‑analysis products for enterprises, covering challenges, solution routes such as NLP2SQL, NLP2API, NLP2Python, agent design, planning, and future outlooks.
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.
Project BaixiaoSheng, iQIYI’s AI‑powered project management assistant unveiled at the 13th TOP 100 Global Software Case Study Summit, uses a Retrieval‑Augmented Generation framework with static knowledge Q&A, dynamic data consulting, and scenario‑assistant automation to cut context‑switching, streamline data flow, and boost cross‑system efficiency, while future plans target fine‑tuned LLMs, multi‑model fusion, and AI‑agent orchestration.
Alibaba’s AI digital employee “AI XiaoWan” uses a native multi‑agent architecture where a Controller Agent interprets intent, plans tasks, and orchestrates execution while an Executable Agent performs domain‑specific operations, communicating via a standardized Agent Communication Protocol, leveraging a centralized Tool Center, a retrieval‑augmented knowledge base, and a data‑flywheel feedback loop to continuously improve and evolve toward memory‑based reasoning and self‑learning.
This article explores Tencent's large language model deployments across various business scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG for role‑playing, and Agent technologies, while also covering model fine‑tuning, knowledge‑base construction, and evaluation methods.
This article explains why GUI agents are needed, defines their multimodal capabilities, walks through a high‑level automation scenario, details the architecture of large‑model‑driven GUI agents, highlights recent open‑source projects, and compares them with traditional RPA solutions.
This article details Tencent's shift from traditional BI to an AI‑driven intelligent BI platform, describing the challenges of architecture, large‑language‑model integration, and data integration, and presenting the OlaChat framework, unified orchestration, atomic agents, DSL conversion, monitoring, and future roadmap.
The article analyses the rapid growth of Retrieval‑Augmented Generation (RAG) in enterprises, explains why self‑built RAG solutions often collapse under cost and maintenance pressures, and demonstrates how Tencent LeXiang AI Assistant addresses these issues through a robust knowledge‑management core, extensive industry experience, scalable resources, and advanced multimodal capabilities.
Faced with overloaded job listings and low offer rates, a group of students built “WenZhi,” an AI‑driven job‑matching app that leverages Baidu’s ERNIE SDK, generative recommendation, and workflow orchestration to deliver personalized role suggestions and interview advice within minutes.
Tencent Cloud AI Code Assistant has evolved from token‑level IDE completions to LLM‑driven multi‑modal coding and chat features, employing a dual‑loop R&D system, Hunyuan‑based code models, and sophisticated trigger, prompt, stop, and display strategies to deliver context‑aware, secure, and efficient code generation within IDE and review environments.
AutoConsis is a research‑driven, AI‑powered workflow that automatically detects UI content inconsistencies across mobile app pages by combining target region recognition, OCR‑based extraction, and large language model reasoning, achieving low cost, high generalization, and high confidence as demonstrated on Meituan's large‑scale marketing scenarios.
This guide explains how to fix the ArkNotFoundError in the 02_DocQA.py script by configuring a Doubao‑embedding endpoint, setting up a Conda environment with the latest LangChain packages, and provides step‑by‑step code examples for invoking both Zhipu glm‑4 and Volcano large language models via LangChain.
At the 2024 World Internet Conference in Wuzhen, Baidu’s Wenxin AI Agent technology earned the Leading Science and Technology Award, marking its second consecutive win and highlighting the system’s brain‑inspired “System 2” architecture that enhances large‑model reasoning, accelerates diverse applications, and drives significant social and economic value.
At Baidu World 2024, CTO Wang Haifeng revealed that Wenxin Yiyan has reached 430 million users, detailed the model’s retrieval‑augmented and multimodal generation breakthroughs, showcased intelligent‑agent‑driven coding tools, and highlighted expanding AI applications across education, sports, and industry.
This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), its four‑step workflow, architecture, and how Intel’s hardware and software optimizations—including vector search, quantized embeddings, and advanced inference extensions—enhance performance, security, and scalability for enterprise LLM applications.
ChatDBA, developed by Shanghai Aikesheng, is an AI-driven database operation assistant that leverages large language models and Retrieval‑Augmented Generation to provide fault diagnosis, knowledge learning, SQL generation and optimization, addressing challenges such as vague outputs, complex troubleshooting logic, and memory management through a structured architecture and multi‑modal retrieval strategies.
Tencent has open‑sourced its 389‑billion‑parameter Hunyuan Large Mixture‑of‑Experts model—featuring 52 B active parameters, 256 K token context, novel routing, KV‑cache compression, and advanced training optimizations that beat leading open‑source models—and its first text‑to‑3D/image‑to‑3D Hunyuan 3D Generation model, both downloadable via GitHub, Hugging Face, and Tencent Cloud.
This article describes Siemens' journey in applying generative AI and Retrieval‑Augmented Generation to create an internal knowledge chatbot, detailing the business challenges, technical architecture, data integration, multi‑modal capabilities, deployment outcomes, and strategic lessons for enterprise AI adoption.
This article details Bilibili’s implementation of a large‑language‑model‑driven intelligent assistant for its massive big‑data platform, covering background, problem analysis, architectural design, knowledge‑base construction, precision and recall challenges, deployment across offline and real‑time Spark/Flink diagnostics, and future outlooks.
This article introduces Retrieval‑Augmented Generation (RAG) as a solution to the hallucination, freshness, and data‑privacy issues of large language models, details its modular architecture, explains the layered system design and hybrid retrieval pipeline, and shares the practical challenges and engineering tricks encountered when deploying RAG in enterprise office scenarios.
This article explains how the open‑source Yuan 2.0 large language model is fully integrated with Baidu’s PaddleNLP, covering its capabilities, fine‑tuning optimizations, step‑by‑step deployment instructions, interaction examples, and training/finetuning results with loss‑curve visualizations.
This article details Ximalaya’s ChatBI project, describing how large language models are integrated into a BI platform to improve data accessibility, reduce development effort, and enhance query accuracy through prompt engineering, RAG, fine‑tuning, and multi‑agent architectures.
Google has signed a $2.7 billion agreement to rehire AI pioneer Noam Shazeer—co‑author of the seminal “Attention is All You Need” paper and creator of the Meena chatbot—bringing him back from his Character.AI venture to serve as vice president overseeing the Gemini generative‑AI project alongside DeepMind leaders, thereby bolstering Google’s competitive edge in the field.
This article presents a method for massive multi-label text classification on Zhihu content by combining a semantic retrieval model with a proprietary large AI model, detailing the challenges of large label spaces, model architecture, loss optimization, and experimental results showing significant accuracy gains.
This article describes how a commercial search platform iteratively upgrades its system by structuring business knowledge into a knowledge graph, applying multi‑stage entity extraction (CRF, Electra‑CRF, GLM‑3, OCR), and leveraging large language models to improve relevance, user experience, and revenue.
OpenAI’s new o1 series, including o1‑preview and o1‑mini, leverages reinforcement‑learning‑based chain‑of‑thought reasoning to achieve superior performance on academic exams, coding contests, and safety benchmarks, offering faster, cost‑effective options while advancing AI alignment and human‑preference evaluation.
OpenAI’s newly released o1 series introduces a reinforcement‑learning‑trained LLM that generates long chain‑of‑thought reasoning, achieving top‑50% scores on IOI contests, high rankings on Codeforces and AIME, and dramatically outperforming GPT‑4o across scientific and mathematical tasks.
An AI-driven test case generation platform at KuJiaLe leverages multiple large language models, offering three input methods, online editing, and dual export options, while addressing stability, length limits, and security challenges to improve testing efficiency and achieve over 80% success rate.
This article analyzes how large‑model AIGC reshapes the production, evaluation, and deployment of advertising creatives on Xiaohongshu, detailing the business motivations, technical pipeline, controllable generation, reward‑model filtering, and experimental results that balance commercial efficiency with community tone.
This article details the creation of a multi‑model AI chatbot, covering its core features, workflow, prompt role configuration, parameter tuning, anti‑reverse‑engineering measures, competitive landscape, and reflective insights for developers building large‑model applications.
This article explores the free GLM-4-Flash API from Zhipu AI, detailing its lightweight architecture, performance specs, a logic‑puzzle demonstration, and provides a comprehensive step‑by‑step tutorial—including data upload, model fine‑tuning, deployment commands and example code for building a LangChain‑based knowledge‑base retrieval system.
