Revolutionizing AI Accuracy: The Hierarchical Chunking Breakthrough You Need to Know
6 hours ago 高效码农Tongyi DeepResearch: Revolutionizing Deep Information Retrieval with Agentic Language Models
8 hours ago 高效码农Tongyi DeepResearch: The Intelligent Agent Model Ushering in a New Era of Deep Information Retrieval In today’s rapidly evolving artificial intelligence landscape, Large Language Models (LLMs) are fundamentally changing how we access and process information. However, when faced with complex, open-ended tasks that require multi-step reasoning and deep information seeking, traditional models often fall short. To address this challenge, Tongyi Lab has developed and released Tongyi DeepResearch—a massive agentic language model with 30 billion total parameters, but activating only 3 billion parameters per token. It is specifically engineered for long-horizon, deep information-seeking tasks and has demonstrated state-of-the-art performance across a …
TildeOpen 30B: Europe’s Open LLM Revolution for 90+ Languages
3 days ago 高效码农Europe’s Own 30-Billion-Parameter Open LLM Is Here: Meet TildeOpen A plain-language walk-through for college-level readers who want to understand—without the hype—why Europe built its own large language model, how to run it on your own hardware, and what it can (and cannot) do. Quick-Glance Card Question One-line answer What is it? A 30-billion-parameter, decoder-only transformer released by Latvian language-tech company Tilde; optimized for European—especially smaller—languages. Parameters & licence 30 B, dense (no mixture-of-experts), CC-BY-4.0, commercial use allowed. Languages covered 90+ European tongues including Latvian, Lithuanian, Estonian, Ukrainian, Turkish, Croatian, Icelandic, Irish, Basque, Sami and more. Training compute 2 million GPU …
mmBERT: The 3-Trillion-Token Encoder Outperforming XLM-R in Multilingual NLP
5 days ago 高效码农Meet mmBERT: The 3-Trillion-Token Encoder That Overtakes XLM-R After Six Years In one sentence: Johns Hopkins’ 307 M-parameter mmBERT trains on 3 T tokens across 1 833 languages, needs only 100 B tokens to “grow” 1 700 low-resource tongues at the very end, and still runs 2–4× faster than XLM-R while topping it on every benchmark that matters. What this article answers in plain English Why was a new multilingual encoder overdue? How does “annealed language learning” squeeze 1 833 languages into the last training stage? What tricks (inverse masking, model merging, FlashAttention2) make mmBERT both faster and stronger? How …
UltraRAG 2.0: Build Advanced RAG Systems in Dozens of Lines of Code
11 days ago 高效码农UltraRAG 2.0: Building High-Performance Retrieval-Augmented Generation Systems with Minimal Code Dozens of lines of code to implement complex reasoning pipelines like Search-o1, focusing on research innovation instead of engineering burdens. Have you ever struggled with the complex engineering implementation when building retrieval-augmented generation (RAG) systems? As RAG systems evolve from simple “retrieve + generate” approaches to complex knowledge systems incorporating adaptive knowledge organization, multi-step reasoning, and dynamic retrieval, researchers face increasing engineering challenges. Traditional methods require substantial code to implement workflow control, module integration, and experimental evaluation—not only time-consuming but also error-prone. Now, there’s a new solution: UltraRAG 2.0. What …
Mastering spaCy NLP: Your Ultimate Guide to Advanced Natural Language Processing in Python
15 days ago 高效码农Getting Started with spaCy: Your Guide to Advanced Natural Language Processing in Python Have you ever wondered how computers can understand and process human language? If you’re working with text data in Python, spaCy might be the tool you’ve been looking for. It’s a library designed for advanced natural language processing, or NLP, that combines speed, accuracy, and ease of use. In this article, we’ll walk through what spaCy offers, how to set it up, and how to make the most of its features. I’ll explain things step by step, as if we’re chatting about it over coffee, and I’ll …
Hunyuan-MT 7B: How a 7B-Parameter Model Beats Translation Giants
15 days ago 高效码农Hunyuan-MT: A 7-Billion-Parameter Translation Model That Outperforms Giants “Can a 7-billion-parameter model really beat 200-billion-parameter giants at translation?” “Is open-source finally good enough for Tibetan, Uyghur, Kazakh, and Mongolian?” “How long does it take to get it running on my own GPU?” If you have asked any of these questions, you are in the right place. This post translates the official Hunyuan-MT technical report and README into plain English. Every figure, command, and benchmark comes straight from the released files—nothing added, nothing removed. Quick overview Item Hunyuan-MT-7B Hunyuan-MT-Chimera-7B Size 7 B parameters 7 B parameters (fusion model) Languages 33, incl. …
Evidence-Based Text Generation with Large Language Models: A Systematic Study of Citations and Datasets
16 days ago 高效码农Evidence-Based Text Generation with Large Language Models: A Systematic Study of Citations, Attributions, and Quotations In the digital age, large language models (LLMs) have become increasingly widespread—powering everything from customer service chatbots to content creation tools. These models are reshaping how humans process and generate text, but their growing popularity has brought a critical concern to the forefront: How can we trust the information they produce? When an LLM generates an analysis report, an academic review, or a key piece of information, how do we verify that the content is supported by solid evidence? And how can we trace the …
LLM Question Generator: Create Custom Questions from Text in Seconds
17 days ago 高效码农Generate High-Quality Questions from Text — Practical Guide What this tool does This project generates multiple, diverse, human-readable questions from input text. It supports a range of large language model backends and providers. You feed the tool a dataset or a local file that contains text. The tool calls a model to create a set number of questions for every input item. Optionally, the tool can also generate answers for those questions. The final output is written as JSON Lines files. These files are ready for use in training, content creation, assessment generation, or dataset augmentation. Quick start — minimal …
Prompt Engineering Demystified: Master LLM Communication Like a Pro
24 days ago 高效码农A Complete Guide to Prompt Engineering: How to Communicate Effectively with Large Language Models Artificial intelligence has changed how we work, learn, and create. At the center of this change is Prompt Engineering—the practice of writing effective inputs that guide large language models (LLMs) to produce useful, accurate, and reliable outputs. This guide explores prompt engineering in detail, based entirely on the source material, while adapting it for an international audience. The focus is on clarity, practicality, and real-world usability. Introduction When interacting with a large language model, the prompt—the input you provide—is the single most important factor that influences …
DeepSeek V3.1 Redefines Open-Source AI Competition with Enhanced Reasoning & 128K Context Window
28 days ago 高效码农DeepSeek V3.1 Released: Extended Context, Enhanced Reasoning, and the New Stage of Open-Source AI Competition A longer context window, stronger reasoning capabilities, and better cost-effectiveness—DeepSeek V3.1 is redefining the competitiveness of open-source large language models. On August 19, Chinese AI company DeepSeek officially released DeepSeek V3.1, a new version of its AI model. According to official announcements and feedback from the tech community, this is an incremental upgrade based on the previous V3 model, primarily improving context length and comprehensive reasoning capabilities, while also further enhancing performance in specialized tasks such as mathematics and programming. Although not a revolutionary leap, …
Dual Chunk Attention: The Training-Free Breakthrough for 100k+ Token LLMs
1 months ago 高效码农What is Dual Chunk Attention? by @karminski-dentist dual-chunk-attention-concept (Image source: Paper “Training-Free Long-Context Scaling of Large Language Models”) DCA (Dual Chunk Attention) is a technology developed by institutions including the University of Hong Kong in 2024. It’s a training-free method to expand the context window of large language models. This means models like Llama2 70B, which originally only support a 4k token context window, can now handle more than 100k tokens without the need for any ongoing training. In simple terms, think of a language model’s context window as the “memory” it has when processing text. If you’ve ever tried …
LLM Reasoning Techniques: Unlocking Advanced AI Problem-Solving Strategies
1 months ago 高效码农Large Language Model Reasoning Techniques: From Basics to Advanced 1. What is LLM Reasoning? LLM reasoning refers to the capability of large language models to solve complex problems by generating intermediate thinking processes. Similar to how humans approach problem-solving through step-by-step analysis, models generate intermediate tokens to tackle intricate tasks. Example Illustration: Question: What is the concatenated of the last letters of each word in “artificial intelligence”? Non-reasoning answer: le Reasoning process: – Last letter of “artificial” is “l” – Last letter of “intelligence” is “e” – Concatenation result: “le” This explicit reasoning process helps models solve problems like mathematical …
MetaStone-S1: How 32B Beats OpenAI o3-mini with Draft Paper Strategy
1 months ago 高效码农From Quick Guesses to Thoughtful Drafts: How MetaStone-S1 Makes a 32 B Model Rival OpenAI o3-mini 1. Why Do Large Language Models Need Draft Paper? Imagine you are taking a tough math final. If you must write the final answer in one shot, you will probably lose points. Give yourself scratch paper, let yourself jot down three different approaches, and then hand in the cleanest version—your score jumps. Large language models (LLMs) face the same problem. Traditional models generate one answer and stop. A newer idea called Test-Time Scaling (TTS) lets the model create many “draft solutions” at inference time, …
MOSS-TTSD: Revolutionizing AI Podcasts with Open-Source Bilingual Dialogue Synthesis
1 months ago 高效码农MOSS-TTSD: Open-Source Bilingual Spoken Dialogue Synthesis for AI-Powered Podcasts MOSS-TTSD Model Overview In the rapidly evolving landscape of artificial intelligence, voice technology has moved beyond simple text-to-speech conversion to sophisticated dialogue generation. MOSS-TTSD (Text to Spoken Dialogue) represents a significant advancement in this field, offering a powerful, open-source solution for creating natural-sounding conversations between two speakers. Whether you’re a content creator looking to produce AI podcasts, a developer building conversational AI, or a researcher exploring voice synthesis, MOSS-TTSD provides a robust foundation for your projects. What is MOSS-TTSD? MOSS-TTSD is an open-source bilingual spoken dialogue synthesis model that transforms dialogue …
Master LangExtract: Transform Wall-of-Text into Structured Data in 5 Minutes
1 months ago 高效码农From Wall-of-Text to Structured Gold: A Beginner-Friendly Guide to LangExtract Audience: Junior-college graduates with basic Python Goal: Extract structured data from any long document in under 30 minutes Reading time: ~20 minutes for the first successful run Table of Contents Why LangExtract Exists What It Actually Does Your First Extraction in 5 Minutes Handling Long Documents Without Headaches Real-World Use Cases — Scripts, Medical Notes, Radiology Reports FAQ Corner Going Further — Local Models & Contributing Back 1. Why LangExtract Exists Imagine these Monday-morning requests: • “Turn this 150 000-word novel into a spreadsheet of every character and their relationships.” …
Introducing Qwen3-30B-A3B-Instruct-2507: The New Benchmark in Large Language Models
1 months ago 高效码农Qwen3-30B-A3B-Instruct-2507: A Comprehensive Guide to the Latest Large Language Model Introduction to Qwen3-30B-A3B-Instruct-2507 The Qwen3-30B-A3B-Instruct-2507 represents a significant advancement in the field of large language models (LLMs). This model, part of the Qwen series, is designed to handle a wide range of tasks with enhanced capabilities in instruction following, logical reasoning, and text comprehension. As a non-thinking mode model, it focuses on delivering efficient and accurate responses without the need for additional processing steps. This guide provides an in-depth look at the features, performance, and practical applications of Qwen3-30B-A3B-Instruct-2507, tailored for technical professionals and enthusiasts. Qwen3-30B-A3B-Instruct-2507 Model Architecture Technical Overview …
Qwen3-235B-A22B-Thinking-2507: Beating GPT at Math and Code – Open Source AI Showdown
1 months ago 高效码农Qwen3-235B-A22B-Thinking-2507: The Open-Source Reasoning Model That Actually Outperforms GPT on Math and Code A plain-English, no-hype guide for developers, researchers, and technical product managers who want to understand what this 235-billion-parameter reasoning engine can—and cannot—do. Table of Contents What Exactly Is Qwen3-235B-A22B-Thinking-2507? Three Months of Improvements: Quality, Depth, Length Model Specs at a Glance Benchmark Results in Plain Numbers Getting Started: Zero-to-First-Inference Tutorial Deployment Recipes: SGLang, vLLM, and Local Tools Turning the Model into an Agent Best-Practice Settings: Temperature, Context, and Output Length Frequently Asked Questions What Exactly Is Qwen3-235B-A22B-Thinking-2507? Think of Qwen3-235B-A22B-Thinking-2507 as a specialized “reasoning engine” built on …
Qwen-MT Translation Guide: Unlock 92-Language AI Translation for Legal, Medical & Real-Time Use Cases
1 months ago 高效码农Qwen-MT in Plain English: A 3,000-Word Guide to 92-Language Translation for Everyday Users What you’ll learn in the next ten minutes How Qwen-MT turns any sentence into 92 languages without losing nuance The exact three-step setup to start translating in under five minutes When to pick “turbo” vs “plus” (and what it costs) Real code you can copy-paste for legal, medical, or social-media content 1. Meet Qwen-MT: the translator that speaks 92 languages Qwen-MT is a machine-translation model built on top of the Qwen3 large-language family. Think of it as a bilingual friend who has read every Wikipedia, contract, and …
Breakthrough in Multi-Token Prediction: How AI Models Now Generate Text 5x Faster
1 months ago 高效码农AI Speed Revolution: How Language Models Can Predict Multiple Words at Once Introduction: The Efficiency Dilemma of Autoregressive Models In the field of artificial intelligence, autoregressive language models like GPT have become core tools for content generation. These models generate text by predicting words one at a time, much like playing “Pictionary” where you can only draw one stroke at a time. However, as models grow larger, this serial generation approach reveals significant drawbacks: Slow generation speed: Each word must wait for the previous one to complete Wasted computational resources: The entire model runs for each single word prediction Long-text …