Qwen · 历史博客

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HISTORY近 30 天历史柱高表示当天去重热搜数量

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09/17—10/16 有历史数据

01
Qwen3Guard: Real-time Safety for Your Token Stream
Tech Report GitHub Hugging Face ModelScope DISCORD Introduction We are excited to introduce Qwen3Guard, the first safety guardrail model in the Qwen family. Built upon the powerful Qwen3 foundation models and fine-tuned specifically for safQwen Team
02
Qwen-Image-Edit: Image Editing with Higher Quality and Efficiency
QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We are excited to introduce Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text renderQwen Team
03
Qwen-Image: Crafting with Native Text Rendering
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD We are thrilled to release Qwen-Image, a 20B MMDiT image foundation model that achieves significant advances in complex text rendering and precise image editing. To try the latest model, feel freeQwen Team
04
GSPO: Towards Scalable Reinforcement Learning for Language Models
PAPER DISCORD Introduction Reinforcement Learning (RL) has emerged as a pivotal paradigm for scaling language models and enhancing their deep reasoning and problem-solving capabilities. To scale RL, the foremost prerequisite is maintainingQwen Team
05
Qwen-MT: Where Speed Meets Smart Translation
DEMO API DISCORD Introduction Here we introduce the latest update of Qwen-MT (qwen-mt-turbo) via Qwen API. This update builds upon the powerful Qwen3, leveraging trillions multilingual and translation tokens to comprehensively enhance the mQwen Team
06
Qwen3-Coder: Agentic Coding in the World
GITHUB HUGGING FACE MODELSCOPE DISCORD Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A3Qwen Team
07
Time to Speak Some Dialects, Qwen-TTS!
API DISCORD Introduction Here we introduce the latest update of Qwen-TTS (qwen-tts-latest or qwen-tts-2025-05-22) through Qwen API . Trained on a large-scale dataset encompassing over millions of hours of speech, Qwen-TTS achieves human-levQwen Team
08
Qwen VLo: From "Understanding" the World to "Depicting" It
QWEN CHAT DISCORD Introduction The evolution of multimodal large models is continually pushing the boundaries of what we believe technology can achieve. From the initial QwenVL to the latest Qwen2.5 VL, we have made progress in enhancing thQwen Team
09
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen3 Embedding series, a new proprietary model of the Qwen model family. These models are specifically designed for text embedding, retrieval, and reranking tasks, built on the Qwen3 foundaQwen Team
10
Qwen3: Think Deeper, Act Faster
QWEN CHAT GitHub Hugging Face ModelScope Kaggle DEMO DISCORD Introduction Today, we are excited to announce the release of Qwen3, the latest addition to the Qwen family of large language models. Our flagship model, Qwen3-235B-A22B, achievesQwen Team
11
QVQ-Max: Think with Evidence
QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction Last December, we launched QVQ-72B-Preview as an exploratory model, but it had many issues. Today, we are officially releasing the first version of QVQ-Max, our visual reasoningQwen Team
12
Qwen2.5 Omni: See, Hear, Talk, Write, Do It All!
QWEN CHAT HUGGING FACE MODELSCOPE DASHSCOPE GITHUB PAPER DEMO DISCORD We release Qwen2.5-Omni, the new flagship end-to-end multimodal model in the Qwen series. Designed for comprehensive multimodal perception, it seamlessly processes diversQwen Team
13
Qwen2.5-VL-32B: Smarter and Lighter
QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction At the end of January this year, we launched the Qwen2.5-VL series of models, which received widespread attention and positive feedback from the community. Building on the Qwen2.Qwen Team
14
QwQ-32B: Embracing the Power of Reinforcement Learning
QWEN CHAT Hugging Face ModelScope DEMO DISCORD Scaling Reinforcement Learning (RL) has the potential to enhance model performance beyond conventional pretraining and post-training methods. Recent studies have demonstrated that RL can signifQwen Team
15
<think>...</think> QwQ-Max-Preview
QWEN CHAT DISCORD This is a blog created by QwQ-Max-Preview. We hope you enjoy it! Introduction <think> Okay, the user wants me to create a title and introduction for their blog announcing the release of QwQ-Max-Preview. Let me start by undQwen Team
16
Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model
QWEN CHAT API DEMO DISCORD It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in efQwen Team
17
Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens
Tech Report HuggingFace ModelScope Qwen Chat HuggingFace Demo ModelScope Demo DISCORD Introduction Two months after upgrading Qwen2.5-Turbo to support context length up to one million tokens, we are back with the open-source Qwen2.5-1M modeQwen Team
18
Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL!
QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2.5-VL, the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2-VL. To try the latest model, feel free to visit Qwen Chat and chooseQwen Team
19
Global-batch load balance almost free lunch to improve your MoE LLM training
GITHUB HUGGING FACE MODELSCOPE DISCORD Background The Mixture-of-Experts (MoEs) architecture has become a popular model-parameter-scale-up technique. Typically, one MoE layer consists of a router (often parameterized as one single Linear laQwen Team
20
Towards Effective Process Supervision in Mathematical Reasoning
GITHUB HUGGING FACE MODELSCOPE DISCORD Introduction In recent years, Large Language Models (LLMs) have made remarkable advances in mathematical reasoning, yet they can make mistakes, such as miscalculations or logical errors, leading to wroQwen Team
21
QVQ: To See the World with Wisdom
GITHUB HUGGING FACE MODELSCOPE KAGGLE DEMO DISCORD Language and vision intertwine in the human mind, shaping how we perceive and understand the world around us. Our ability to reason is deeply rooted in both linguistic thought and visual meQwen Team
22
QwQ: Reflect Deeply on the Boundaries of the Unknown
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”. What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wadeQwen Team
23
Extending the Context Length to 1M Tokens!
API Documentation (Chinese) HuggingFace Demo ModelScope Demo Introduction After the release of Qwen2.5, we heard the community’s demand for processing longer contexts. In recent months, we have made many optimizations for the model capabiliQwen Team
24
Qwen2.5-Coder Series: Powerful, Diverse, Practical.
GITHUB HUGGING FACE MODELSCOPE KAGGLE DEMO DISCORD Introduction Today, we are excited to open source the “Powerful”, “Diverse”, and “Practical” Qwen2.5-Coder series, dedicated to continuously promoting the development of Open CodeLLMs. PoweQwen Team
25
Qwen2.5: A Party of Foundation Models!
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we havQwen Team
26
Qwen2.5-LLM: Extending the boundary of LLMs
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In this blog, we delve into the details of our latest Qwen2.5 series language models. We have developed a range of decoder-only dense models, with seven of them open-sourced, spanningQwen Team
27
Qwen2.5-Coder: Code More, Learn More!
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In early April, we introduced CodeQwen1.5, which garnered significant attention from the community. Since then, we have been working to enhance the coding model. Today, we are excitedQwen Team
28
Qwen2.5-Math: The world's leading open-sourced mathematical LLMs
GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks. Introduction A month ago, we released theQwen Team
29
Qwen2-VL: To See the World More Clearly
DEMO GITHUB HUGGING FACE MODELSCOPE API DISCORD After a year’s relentless efforts, today we are thrilled to release Qwen2-VL! Qwen2-VL is the latest version of the vision language models based on Qwen2 in the Qwen model familities. ComparedQwen Team
30
Qwen2-Audio: Chat with Your Voice!
DEMO PAPER GITHUB HUGGING FACE MODELSCOPE DISCORD To achieve the objective of building an AGI system, the model should be capable of understanding information from different modalities. Thanks to the rapid development of large language modeQwen Team
31
Introducing Qwen2-Math
GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 This model mainly supports English. We will release bilingual (English and Chinese) math models soon. Introduction Over the past year, we have dedicated significant effort to researching and enhancinQwen Team
32
Hello Qwen2
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction After months of efforts, we are pleased to announce the evolution from Qwen1.5 to Qwen2. This time, we bring to you: Pretrained and instruction-tuned models of 5 sizes, including QwenQwen Team
33
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
We’ve created an agent using Qwen2 models with an 8k context size to understand documents with 1M tokens, surpassing RAG and native long-context models. This agent was also used to generate data for training new long-context Qwen models.Qwen Team
34
Notes on Qwen-Max-0428
API DEMO DISCORD Previously, we opensourced a series of Qwen1.5 model ranging from 0.5 to 110 billion parameters. Now, we release a larger model, Qwen-Max-0428. Qwen-Max-0428 is an instruction-tuned model for chat service. Very recently, itQwen Team
35
Qwen1.5-110B: The First 100B+ Model of the Qwen1.5 Series
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction Recently we have witnessed a burst of large-scale models with over 100 billion parameters in the opensource community. These models have demonstrated remarkable performance in both beQwen Team
36
Code with CodeQwen1.5
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding tQwen Team
37
Qwen1.5-32B: Fitting the Capstone of the Qwen1.5 Language Model Series
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The open-source community has long sought a model that strikes an ideal balance between performance, efficiency, and memory footprint. Despite the emergence of cutting-edge models likQwen Team
38
Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction Since the surge in interest sparked by Mixtral, research on mixture-of-expert (MoE) models has gained significant momentum. Both researchers and practitioners are keenly interested inQwen Team
39
Introducing Qwen1.5
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In recent months, our focus has been on developing a “good” model while optimizing the developer experience. As we progress towards Qwen1.5, the next iteration in our Qwen series, thiQwen Team
40
Introducing Qwen-VL
Along with the rapid development of our large language model Qwen, we leveraged Qwen’s capabilities and unified multimodal pretraining to address the limitations of multimodal models in generalization, and we opensourced multimodal model QwQwen Team
41
Introducing Qwen
4 months after our first release of Qwen-7B, which is the starting point of our opensource journey of large language models (LLM), we now provide an introduction to the Qwen series to give you a whole picture of our work as well as our objeQwen Team
42
OFASys: Enabling Multitask Learning with One Line of Code!
Intro Generalist Models are hot! We all see an opportunity towards a real generalist model by multimodal multitask learning. We previously release an opensourced unified multimodal pretrained model OFA for this goal. However, we actually meQwen Team
43
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
CLIP1 is a phenomenal playmaker in vision and multimodal representation learning. It plays not only as a foundation model but also a bridge between vision and language. It has triggered a series of research in different fields, especially tQwen Team
44
OFA: Towards Building a One-For-All Model
2022 is a year of generalist models! With the bloom of multimodal pretraining, especially the unified model, we have witnessed the opportunity to building a generalist model that is capable of processing tasks of different modalities or mulQwen Team