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14:["$","p",null,{"children":"We have prepared some demos to showcase the new uses that zen-Turbo can bring, covering deep understanding of long novels, repository-level code assistant, and reading multiple papers."}]
15:["$","p",null,{"children":"Example: Understanding Long Novels Next"}]
16:["$","p",null,{"children":"User"}]
17:["$","p",null,{"children":["[Upload the three Chinese novels of ",["$","code",null,{"children":"The Three-Body Problem"}],", which contain 690k tokens.]"]}]
18:["$","p",null,{"children":"Provide a summary of the plots of these three novels in English"}]
19:["$","p",null,{"children":"zen-Turbo"}]
1a:["$","p",null,{"children":"The three novels you mentioned from the “Earth’s Past” series are interconnected and form a trilogy that delves into the complex interactions between Earth and an alien civilization known as the Trisolarans."}]
1b:["$","p",null,{"children":[["$","strong",null,{"children":"First Novel: “Earth’s Past”"}]," In the first novel, the narrative focuses on the development of a top-secret project called “Red Coast” (红岸) initiated by the Chinese military. The protagonist, Ye Wenjie, is a scientist who discovers that the Trisolarans have begun listening to the universe. Her actions, particularly her decision to respond to the Trisolarans, drastically alter the course of human history. She sets up a powerful signal to send to the Trisolarans using the sun as an amplifier. This action marks the beginning of a new era where humanity faces a significant existential threat."]}]
1c:["$","p",null,{"children":[["$","strong",null,{"children":"Second Novel: “Dark Forest”"}]," The second novel, titled “Dark Forest,” delves into the concept of the “Dark Forest Theory.” According to this theory, the universe is a vast battlefield where civilizations must remain silent to avoid detection and subsequent destruction. The Trisolarans and humans engage in a complex strategic game, with the Trisolarans deploying advanced weapons known as “water drops” (水滴). These water drops are capable of destroying entire planets by disrupting their ecosystems. Meanwhile, humans develop their own strategies, including the “Wall-Bearing Plan” (面壁计划), which involves four individuals who devise plans to counter the Trisolaran threat without revealing their intentions to the outside world. The story follows one of these face-bearers, Luo Ji, as he struggles with the ethical implications of his mission."]}]
61:T49f, In “Death’s End,” Luo Ji becomes the ultimate guardian of the Dark Forest Theory. He holds the key to the survival of both human and Trisolaran civilizations. However, his position as the guardian (执剑人) brings him immense personal struggle and psychological turmoil. As Luo Ji’s time as the guardian nears its end, he must make a critical decision about the future of humanity. Meanwhile, the Trisolarans continue their efforts to colonize and assimilate other worlds. The novel explores the consequences of this decision and the broader impact on the universe. It also introduces a new element: the creation of micro-black holes (黑域) by humans, which could potentially isolate them from the rest of the universe. However, the effectiveness of this strategy is questioned when six of these micro-black holes begin to move and accelerate towards Earth. The trilogy as a whole explores themes of survival, technological advancement, and the ethical dilemmas faced by humanity in the face of extraterrestrial threats. Each novel builds upon the previous one, revealing more about the intricate relationship between the two civilizations and the universe they inhabit.1d:["$","p",null,{"children":[["$","strong",null,{"children":"Third Novel: “Death’s End”"}],"$61"]}]
1e:["$","p",null,{"children":"Example: Understanding Repository Next"}]
1f:["$","p",null,{"children":"User"}]
20:["$","p",null,{"children":["[Upload a text file containing all code files from the ",["$","$Lf",null,{"href":"https://github.com/QwenLM/Qwen-Agent","children":"Qwen-Agent"}]," repository, which has a length of 133k tokens.]"]}]
21:["$","p",null,{"children":"Please read the codes carefully and ensure your response is comprehensive. Introduce this repository"}]
22:["$","p",null,{"children":"zen-Turbo"}]
23:["$","p",null,{"children":"Qwen-Agent is a framework designed for developing LLM (Large Language Model) applications. It is based on the instruction-following, tool usage, planning, and memory capabilities of Qwen. Here are some key points and functionalities:"}]
24:["$","ol",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"Agent Class"}]," : Provides a high-level interface for integrating tool calls and LLM interactions. It supports role-playing, automatic planning, and tool invocation."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"DocQA Agent"}]," : Specialized for document question answering, capable of handling long documents and providing context-aware responses."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Function Calling"}]," : Supports function calling, enabling interaction with tools or APIs directly from the model’s responses."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"RAG (Retrieval-Augmented Generation)"}]," : Integrates document retrieval with generation, enhancing the model’s ability to recall and use information from documents."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Tool Integration"}]," : Includes various built-in and custom tools, such as code interpreters, web extractors, and image generators, which can be used to perform tasks like code execution, web scraping, and image manipulation."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Multi-Agent Systems"}]," : Supports the creation of multi-agent systems where multiple agents can interact, simulate conversations, and collaborate on tasks."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Customization"}]," : Highly customizable, allowing developers to tailor the framework to specific needs, such as creating custom agents, defining custom workflows, and integrating additional tools."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Examples and Demos"}]," : Comes with several examples and demos, such as a Chrome browser extension called BrowserQwen, which demonstrates the practical application of Qwen-Agent in a real-world scenario."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Testing and Debugging"}]," : Includes testing and debugging utilities, such as test cases and logging mechanisms, to ensure the reliability and effectiveness of the developed applications."]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"Dependencies"}]," : Requires certain dependencies to be installed for specific functionalities, such as ",["$","code",null,{"children":"qwen-agent[gui]"}]," for GUI support and ",["$","code",null,{"children":"qwen-agent[rag]"}]," for RAG support. This repository is valuable for researchers, developers, and anyone interested in leveraging large language models for complex tasks and applications."]}],"\n"]}]
25:["$","p",null,{"children":"User"}]
26:["$","p",null,{"children":"What are the subclasses of Agent in this repository, and provide their file paths"}]
27:["$","p",null,{"children":"zen-Turbo"}]
28:["$","p",null,{"children":["Here are the subclasses of the ",["$","code",null,{"children":"Agent"}]," class in the Qwen-Agent repository along with their file paths:"]}]
29:["$","ol",null,{"children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"Assistant"}]}],"\n"]}]
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32:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/tir_agent.py"}]]}],"\n"]}]
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34:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/user_agent.py"}]]}],"\n"]}]
35:["$","ol",null,{"start":"15","children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"VisualStorytelling"}]}],"\n"]}]
36:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/visual_storytelling.py"}]]}],"\n"]}]
37:["$","ol",null,{"start":"16","children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"MemoAssistant"}]}],"\n"]}]
38:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/memo_assistant.py"}]]}],"\n"]}]
39:["$","ol",null,{"start":"17","children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"WriteFromScratch"}]}],"\n"]}]
3a:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/writing/write_from_scratch.py"}]]}],"\n"]}]
3b:["$","ol",null,{"start":"18","children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"ExpandWriting"}]}],"\n"]}]
3c:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/writing/expand_writing.py"}]]}],"\n"]}]
3d:["$","ol",null,{"start":"19","children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"OutlineWriting"}]}],"\n"]}]
3e:["$","ul",null,{"children":["\n",["$","li",null,{"children":["File Path: ",["$","code",null,{"children":"qwen_agent/agents/writing/outline_writing.py"}]]}],"\n"]}]
3f:["$","p",null,{"children":["These subclasses extend the base ",["$","code",null,{"children":"Agent"}]," class to provide specialized functionalities tailored for different use cases within the Qwen-Agent framework."]}]
40:["$","p",null,{"children":"Example: Understanding Multiple Papers Next"}]
41:["$","p",null,{"children":"User"}]
42:["$","p",null,{"children":["[Upload 7 papers about long-context LLM, which have a length of 171k tokens. Sources: ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2402.17463","children":"1"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2407.02490","children":"2"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2402.05136","children":"3"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2402.04617","children":"4"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2407.11963","children":"5"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2404.06654","children":"6"}]," ",["$","$Lf",null,{"href":"https://arxiv.org/abs/2404.14469","children":"7"}],"]"]}]
43:["$","p",null,{"children":"classify the above papers into two categories"}]
44:["$","p",null,{"children":"zen-Turbo"}]
45:["$","p",null,{"children":"The papers provided can be classified into two categories based on their focus:"}]
46:["$","p",null,{"children":["$","strong",null,{"children":"Category 1: Long-Context Scaling Techniques"}]}]
47:["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"DCA (Dual Chunk Attention)"}]," : This paper introduces a new training-free framework to allow large language models to handle long-context sequences by breaking down the attention computation into smaller chunks. (File: 1. DCA.pdf)"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"MInference"}]," : This paper proposes a method to accelerate pre-filling for long-context language models by using a dynamic sparse attention mechanism with a context memory module. (File: 2. minference.pdf)"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"InfLLM"}]," : This paper presents a training-free memory-based approach to enable large language models to understand extremely long sequences by incorporating an efficient context memory mechanism. (File: 4. infLLM.pdf)"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"SnapKV"}]," : This paper introduces a fine-tuning-free approach to efficiently minimize the key-value (KV) cache size of large language models during generation while maintaining comparable performance. (File: 7. SnapKV.pdf)"]}],"\n"]}]
48:["$","p",null,{"children":["$","strong",null,{"children":"Category 2: Benchmarking Long-Context Capabilities"}]}]
49:["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","strong",null,{"children":"LV-Eval"}]," : This paper introduces a challenging long-context benchmark designed to evaluate the capabilities of large language models across various lengths and tasks, with a focus on mitigating knowledge leakage and providing a balanced evaluation. (File: 3. lveval.pdf)"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"NeedleBench"}]," : This paper presents a benchmark for evaluating the retrieval and reasoning capabilities of large language models in long-context scenarios, including a needle-in-a-haystack task. (File: 5. needlebench.pdf)"]}],"\n",["$","li",null,{"children":[["$","strong",null,{"children":"RULER"}]," : This paper proposes a synthetic benchmark for evaluating long-context language models with diverse task categories, including retrieval, multi-hop tracing, aggregation, and question answering. (File: 6. ruler.pdf)"]}],"\n"]}]
4a:["$","p",null,{"children":"User"}]
4b:["$","p",null,{"children":"Output a JSON array where each element sequentially contains the paper title and a one-sentence summary"}]
4c:["$","p",null,{"children":"zen-Turbo"}]
4d:["$","$L62",null,{"className":"shiki shiki-themes github-light github-dark","style":{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8","--shiki-light-bg":"#fff","--shiki-dark-bg":"#24292e"},"tabIndex":"0","icon":"<svg viewBox=\"0 0 24 24\"><path d=\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\" fill=\"currentColor\" /></svg>","children":["$","$L63",null,{"children":["$","code",null,{"children":[["$","span",null,{"className":"line"}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" ["}]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" {"}]}],"\n",["$","span",null,{"className":"line","children":[["$","span",null,{"style":{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"},"children":" \"title\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":": "}],["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":"\"Dual Chunk Attention\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":","}]]}],"\n",["$","span",null,{"className":"line","children":[["$","span",null,{"style":{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"},"children":" \"summary\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":": "}],["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":"\"DCA introduces a novel training-free framework to enable large language models to handle "}]]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":" long-context sequences bydecomposing attention computation into smaller chunks.\""}]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" },"}]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" {"}]}],"\n",["$","span",null,{"className":"line","children":[["$","span",null,{"style":{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"},"children":" \"title\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":": "}],["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":"\"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":","}]]}],"\n",["$","span",null,{"className":"line","children":[["$","span",null,{"style":{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"},"children":" \"summary\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":": "}],["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":"\"MInference uses a dynamic sparse attention mechanism with a context memory module to"}]]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":" significantlyreduce the latency of long-context LLM inference.\""}]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" },"}]}],"\n",["$","span",null,{"className":"line","children":["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":" {"}]}],"\n",["$","span",null,{"className":"line","children":[["$","span",null,{"style":{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"},"children":" \"title\""}],["$","span",null,{"style":{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"},"children":": "}],["$","span",null,{"style":{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"},"children":"\"InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory\""}],"$L64"]}],"\n","$L65","\n","$L66","\n","$L67","\n","$L68","\n","$L69","\n","$L6a","\n","$L6b","\n","$L6c","\n","$L6d","\n","$L6e","\n","$L6f","\n","$L70","\n","$L71","\n","$L72","\n","$L73","\n","$L74","\n","$L75","\n","$L76","\n","$L77","\n","$L78","\n","$L79","\n","$L7a","\n","$L7b","\n","$L7c","\n","$L7d"]}]}]}]
4e:["$","h1",null,{"className":"flex scroll-m-28 flex-row items-center gap-2","id":"how-to-use-the-api","children":[["$","a",null,{"data-card":"","href":"#how-to-use-the-api","className":"peer","children":"How to Use the API"}],["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100","aria-hidden":true,"children":[["$","path","1cjeqo",{"d":"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"}],["$","path","19qd67",{"d":"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"}],"$undefined"]}]]}]
4f:["$","p",null,{"children":["The latest zen-Turbo supporting 1M tokens is used in the same way as the standard Qwen API and is compatible with the OpenAI API. Below is a simple Python call example (Note: You need to set the environment variable ",["$","code",null,{"children":"YOUR_API_KEY"}]," to your API Key. For more details, please visit ",["$","$Lf",null,{"href":"https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen","children":"Quick Start of Alibaba Cloud Model Studio [Chinese]"}],"):"]}]
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51:["$","h1",null,{"className":"flex scroll-m-28 flex-row items-center gap-2","id":"model-performance","children":[["$","a",null,{"data-card":"","href":"#model-performance","className":"peer","children":"Model Performance"}],["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100","aria-hidden":true,"children":[["$","path","1cjeqo",{"d":"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"}],["$","path","19qd67",{"d":"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"}],"$undefined"]}]]}]
52:["$","p",null,{"children":"In this section, we evaluate the performance of zen-Turbo through various benchmark tests and improvements in inference speed."}]
53:["$","h2",null,{"className":"flex scroll-m-28 flex-row items-center gap-2","id":"passkey-retrieval","children":[["$","a",null,{"data-card":"","href":"#passkey-retrieval","className":"peer","children":"Passkey Retrieval"}],["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100","aria-hidden":true,"children":[["$","path","1cjeqo",{"d":"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"}],["$","path","19qd67",{"d":"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"}],"$undefined"]}]]}]
54:["$","p",null,{"children":"We first conducted experiments on the 1M-token Passkey Retrieval task. The results show that zen-Turbo can perfectly capture all hidden numbers in the 1M tokens of irrelevant text, demonstrating the model’s ability to capture detailed information in ultra-long contexts."}]
55:["$","h2",null,{"className":"flex scroll-m-28 flex-row items-center gap-2","id":"more-complex-long-text-tasks","children":[["$","a",null,{"data-card":"","href":"#more-complex-long-text-tasks","className":"peer","children":"More Complex Long Text Tasks"}],["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100","aria-hidden":true,"children":[["$","path","1cjeqo",{"d":"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"}],["$","path","19qd67",{"d":"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"}],"$undefined"]}]]}]
56:["$","p",null,{"children":"We select several datasets of long text understanding to test the model, including:"}]
57:["$","ul",null,{"children":["\n",["$","li",null,{"children":[["$","$Lf",null,{"href":"https://github.com/hsiehjackson/RULER","children":"RULER"}],": An extended benchmark based on Needle in a Haystack, tasks include finding multiple “needles” in irrelevant contexts, answering multiple questions, or finding the most or least frequent words in the context. The maximum context length is 128K."]}],"\n",["$","li",null,{"children":[["$","$Lf",null,{"href":"https://github.com/infinigence/LVEval","children":"LV-Eval"}],": A benchmark test requiring simultaneous understanding of numerous evidence fragments. We adjust the evaluation metrics in the original version of LV-Eval to avoid false negatives caused by overly strict matching rules. The maximum context length is 256K."]}],"\n",["$","li",null,{"children":[["$","$Lf",null,{"href":"https://github.com/THUDM/LongAlign","children":"LongbenchChat"}],": A dataset evaluating human preference alignment in the tasks of long context. The maximum context length is 100K."]}],"\n"]}]
58:["$","p",null,{"children":"The results show that zen-Turbo has advantages in various tasks of long context:"}]
59:["$","ul",null,{"children":["\n",["$","li",null,{"children":"In the RULER benchmark test, zen-Turbo scores 93.1, surpassing GPT-4o-mini and even GPT-4, proving its excellent ability to handle long text tasks."}],"\n",["$","li",null,{"children":"In more tasks of long context understanding like LV-Eval and LongBench-Chat, zen-Turbo surpasses GPT-4o-mini in most dimensions and can process tasks with a context of over 128K tokens."}],"\n"]}]
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60:["$","p",null,{"children":"While we are pleased to finally extend the context of zen-Turbo to 1M tokens, we also recognize that the current model does not always perform satisfactorily when solving long sequence tasks in real applications. There are many unresolved challenges, such as the model’s performance being more unstable in long sequence tasks, and the cost of inference making it difficult to use larger models. However, we will actively explore further alignment of human preferences in long sequences, optimize inference efficiency to reduce computation time, and attempt to launch larger and stronger long-context models. We look forward to sharing new progress in developing long-context models with you soon, so stay tuned!"}]
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