Kimi Linear: How This Hybrid Attention Architecture Masters Million-Token Contexts
13 hours ago 高效码农Kimi Linear: Revolutionizing Efficient Attention Architecture for Long Context Processing The Core Challenge in Modern Language Models How can we process million-token contexts while maintaining performance and efficiency? Kimi Linear presents a groundbreaking hybrid attention architecture that successfully addresses this fundamental challenge. As large language models evolve into sophisticated agents capable of complex tool usage and multi-step reasoning, the computational limitations of traditional attention mechanisms have become increasingly apparent. The quadratic time complexity and linearly growing memory requirements of standard softmax attention create significant bottlenecks for real-world applications. Kimi Linear emerges as a comprehensive solution that not only maintains but …
Exploring LFM2-ColBERT-350M: A Practical Guide to Multilingual Retrieval for Everyday Developers
2 days ago 高效码农Have you ever built a search feature for an app where users from different countries type in their native languages, but your documents are all in English? It’s frustrating when the system misses obvious matches because of language barriers. That’s where models like LFM2-ColBERT-350M come in handy. This compact retriever, built on late interaction principles, lets you index documents once in one language and query them effectively in many others—all without slowing down your application. In this post, we’ll walk through what makes this model tick, how it performs across languages, and step-by-step ways to integrate it into your projects. …
SoulX-Podcast: Achieving Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity
2 days ago 高效码农The Core Question This Article Answers How can we build a system that generates natural, long-form, multi-speaker conversational speech while supporting dialect and paralinguistic control? SoulX-Podcast makes breakthrough progress in this area by combining large language models with multi-stage data processing pipelines. Recent advances in text-to-speech synthesis have significantly improved speech quality, but most existing systems struggle with multi-speaker, multi-turn conversation scenarios. SoulX-Podcast emerges as a specialized solution to this challenge. It supports both Mandarin and English, along with several Chinese dialects including Sichuanese, Henanese, and Cantonese, while also controlling paralinguistic features like laughter and sighs—setting a new standard for …
On-Policy Distillation: The Cheap Way to Supercharge Small Language Models
3 days ago 高效码农Teaching Models to Correct Themselves: A Complete Guide to On-Policy Distillation What is the cheapest way to make a small language model as good as a big one at narrow tasks? Let the small model generate its own answers, then let the big model grade every single token in real time. On-policy distillation does exactly this—online, dense, and 5-30× cheaper than RL. Table of Contents Why Post-Training Needs a Third Way Algorithm in One Breath Math Reasoning: 60 % → 70 % with 1/10 the GPU Hours Company Assistant: Add Private Knowledge, Then Get Chat Skills Back for Free Author’s …
Glyph: Scaling Context Windows via Visual-Text Compression
9 days ago 高效码农Core Question This Article Answers: How can large language models (LLMs) process million-token contexts without prohibitive computational and memory costs? In the era of advanced AI, LLMs power everything from document analysis to multi-step reasoning. Yet, as contexts stretch to hundreds of thousands or millions of tokens, the quadratic complexity of attention mechanisms balloons resource demands, making real-world deployment impractical. Glyph offers a fresh solution: by rendering long texts into compact images and leveraging vision-language models (VLMs), it compresses inputs 3-4x while preserving accuracy. This approach not only extends effective context lengths but also accelerates training and inference. Drawing from …
QeRL: Revolutionizing LLM RL Training on a Single H100—Quantization That Sparks Exploration and Crushes Costs
15 days ago 高效码农Picture this: You’re knee-deep in debugging an RL pipeline for a 32B LLM, your H100 GPU’s fans screaming like a jet engine, and yet another out-of-memory error crashes your session. Rollouts drag on for hours, rewards barely budge, and your electricity bill rivals a small country’s GDP. Sound familiar? As an AI dev, I’ve been there—staring at frozen progress bars, wondering if true reasoning in large language models is just a pipe dream. But what if I told you there’s an open-source framework that tames this beast on one H100, slashes training time by up to 2x, and—get this—turns quantization …
LightReasoner: How Tiny Models Supercharge LLM Reasoning & Cut Compute by 90%
17 days ago 高效码农Picture this: You’re knee-deep in a math puzzle, and your Harvard-level AI professor (the big LLM) is brilliant but stumbles at the crucial step. Then a sharp kid next door (a small model) chimes in with, “Hey, try it this way.” Boom—the professor gets it, and the answer clicks. Sounds like a fairy tale? Nope, it’s the magic of LightReasoner in action. This framework boosts your LLM’s math reasoning by up to 28% while slashing 90% of your compute costs. Intrigued? It’s not sci-fi—it’s open-source on GitHub, ready for you to tinker with. TL;DR: What You’ll Walk Away With After …
🚀 Ling-1T: When AI Stops Thinking — The Era of Efficient Reasoning
21 days ago 高效码农“ Keywords: Ling-1T, non-thinking model, efficient reasoning, Evo-CoT, FP8 training, MoE architecture, scalable cognition, AI optimization, Hugging Face, ModelScope 1. The Day AI Stopped “Thinking” For years, the holy grail of AI development has been to make machines think like humans. Every major model—from GPT to Gemini—has been racing to emulate human reasoning, emotion, and even creativity. Then inclusionAI came along with a bold reversal: “ “What if true intelligence doesn’t require thinking at all?” Meet Ling-1T, the world’s first non-thinking model — a trillion-parameter behemoth that doesn’t think, but calculates. It doesn’t wander through a maze of self-generated thoughts. …
UserLM-8B: How This AI User Impersonator Flips the Script on Assistant Testing
21 days ago 高效码农Picture this: You’re a developer knee-deep in debugging a multi-turn chat system. Your AI assistant nails every test—anticipating needs, delivering crisp responses. But swap in real user feedback? Chaos. Users fire off half-baked queries riddled with typos, tangents, and zero context. Suddenly, your “perfect” bot stumbles. Sound familiar? This isn’t dystopian fiction; it’s the gritty reality of LLM evaluation today. As someone who’s tinkered on the AI fringes for years, I’ve lost count of the times I’ve wondered: Are our polished assistants truly ready for our messy, human selves? Enter UserLM-8B from Microsoft Research—a game-changer that’s not another chatbot, but …
From O(n²) to O(L·√L): How DeepSeek-V3.2-Exp Slashes Long-Context Costs Without Hurting Quality
1 months ago 高效码农A 5-minute read for engineers who need 128 K tokens tonight, not next quarter. 1. The Scene: 2 A.M. and the Context-Length Wall Li, a Beijing-based ML engineer, just wanted his 671 B model to read a 100 k-token spec and answer one obscure question. By token 60 k the GPU fans sounded like jet engines; at 90 k the server threw an OOM and the latency graph looked like Everest. Sound familiar? Long-context is the new memory wall—and the bill is paid in both dollars and sleep. The next morning DeepSeek dropped an experimental image on Docker Hub: lmsysorg/sglang:dsv32 …
Revolutionizing AI Accuracy: The Hierarchical Chunking Breakthrough You Need to Know
1 months ago 高效码农The Secret Weapon for Improving AI Answer Quality: How Hierarchical Chunking is Revolutionizing Retrieval-Augmented Generation Systems Have you ever asked an AI a question only to receive fragmented, incomplete answers? Or found that despite having the full information in a document, the AI system only retrieves disconnected pieces? This frustrating experience stems from a fundamental challenge in how AI systems process documents: the quality of document chunking. Today, we’ll explore a groundbreaking solution called hierarchical chunking that’s transforming how AI handles complex documents and delivers coherent, accurate responses. Why Traditional Chunking Methods Fail to Deliver Complete Answers Retrieval-Augmented Generation …
Tongyi DeepResearch: Revolutionizing Deep Information Retrieval with Agentic Language Models
1 months 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
1 months 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
1 months 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
1 months 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
1 months 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
2 months 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
2 months 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
2 months 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
2 months 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 …