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zen-blog/blog/codeqwen1.5.html
zeekay 004979143d fix(branding): move decorator to external /zen-decorate.js for React-proof exec
Root cause of HF + GitHub + section icons not appearing in the docs route:
Next.js React hydration was stripping the inline <script> + <style>
elements I'd injected into <body> on every docs page during DOM
reconciliation. The script never registered its setInterval.

Move the decorator to /zen-decorate.js loaded via <script defer src>
in <head>. React leaves external <script src> elements alone, so the
intervals stay alive. The keyframes CSS is now also small enough to
stay in <body> (React will strip it but the @keyframes are already
registered in the global CSSOM by the time it does).
2026-05-19 20:08:58 -07:00

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<!DOCTYPE html><!--i_dnJM_MIpJSOCQWNJVMq--><html lang="en" class="dark"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/_next/static/chunks/f2332aac77592f9d.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/e83606e8fa9cc796.js"/><script src="/_next/static/chunks/36d6595f0156cd7e.js" async=""></script><script src="/_next/static/chunks/040e9cea20a8d9c7.js" async=""></script><script src="/_next/static/chunks/d4dffb5a0973d49c.js" async=""></script><script src="/_next/static/chunks/turbopack-967f27a9fd33556d.js" async=""></script><script src="/_next/static/chunks/59d0ad1b64f8544e.js" async=""></script><script src="/_next/static/chunks/4d80e004cf4896dd.js" async=""></script><script src="/_next/static/chunks/350ee4303b732916.js" async=""></script><script src="/_next/static/chunks/8de849ca74fc071f.js" async=""></script><script src="/_next/static/chunks/e62b91212ee7f8ff.js" async=""></script><script src="/_next/static/chunks/f19fe44237e54646.js" async=""></script><script src="/_next/static/chunks/cb0a883bafeb6805.js" async=""></script><meta name="theme-color" media="(prefers-color-scheme: dark)" content="#0A0A0A"/><meta name="theme-color" media="(prefers-color-scheme: light)" content="#fff"/><title>Code with CodeQwen1.5 — Zen LM Blog | Zen LM</title><meta name="description" content="GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD"/><meta property="og:title" content="Zen LM - Open Foundation Models"/><meta property="og:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><meta property="og:url" content="https://zenlm.org"/><meta property="og:site_name" content="Zen LM"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:site" content="@zenlmorg"/><meta name="twitter:creator" content="@zenlmorg"/><meta name="twitter:title" content="Zen LM - Open Foundation Models"/><meta name="twitter:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><script src="/_next/static/chunks/a6dad97d9634a72d.js" noModule=""></script><script defer src="/zen-decorate.js"></script></head><body class="antialiased"><div hidden=""><!--$--><!--/$--></div><script>((a,b,c,d,e,f,g,h)=>{let i=document.documentElement,j=["light","dark"];function k(b){var c;(Array.isArray(a)?a:[a]).forEach(a=>{let c="class"===a,d=c&&f?e.map(a=>f[a]||a):e;c?(i.classList.remove(...d),i.classList.add(f&&f[b]?f[b]:b)):i.setAttribute(a,b)}),c=b,h&&j.includes(c)&&(i.style.colorScheme=c)}if(d)k(d);else try{let a=localStorage.getItem(b)||c,d=g&&"system"===a?window.matchMedia("(prefers-color-scheme: dark)").matches?"dark":"light":a;k(d)}catch(a){}})("class","theme","system",null,["light","dark"],null,true,true)</script><!--$--><div data-closed="" role="presentation" hidden="" style="user-select:none;-webkit-user-select:none" class="fixed inset-0 z-50 backdrop-blur-xs bg-fd-overlay data-open:animate-fd-fade-in data-closed:animate-fd-fade-out"></div><div class="bg-fd-secondary/50 p-3 empty:hidden"></div><!--/$--><main class="mx-auto w-full max-w-2xl px-4 py-16"><a class="inline-flex items-center gap-1.5 text-sm text-fd-muted-foreground hover:text-fd-foreground mb-10 transition-colors" href="/blog">← Back to Blog</a><div class="mb-8"><time class="text-xs font-mono text-fd-muted-foreground uppercase tracking-wider">April 15, 2024</time><h1 class="text-3xl font-bold mt-2 mb-3">Code with CodeQwen1.5</h1><p class="text-fd-muted-foreground text-lg mb-4">GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD</p><div class="flex items-center gap-3 pt-4 border-t border-fd-border"><span class="text-sm text-fd-muted-foreground">By <!-- -->Zen LM Team</span><div class="flex gap-1.5 ml-auto"></div></div></div><div class="prose dark:prose-invert max-w-none"><p><a href="https://github.com/QwenLM/CodeQwen1.5" rel="noreferrer noopener" target="_blank">GITHUB</a> <a href="https://huggingface.co/Qwen" rel="noreferrer noopener" target="_blank">HUGGING FACE</a> <a href="https://modelscope.cn/organization/qwen" rel="noreferrer noopener" target="_blank">MODELSCOPE</a> <a href="https://huggingface.co/spaces/Qwen/CodeQwen1.5-7b-Chat-demo" rel="noreferrer noopener" target="_blank">DEMO</a> <a href="https://discord.gg/yPEP2vHTu4" rel="noreferrer noopener" target="_blank">DISCORD</a></p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="introduction"><a data-card="" href="#introduction" class="peer">Introduction</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding these advancements, dominant coding assistants like Github Copilot, built upon proprietary LLMs, pose notable challenges in terms of cost, privacy, security, and potential copyright infringement. Recognizing the imperative for a more transparent and accessible alternative, the open-source community has embarked on a concerted endeavor to develop open codeLLMs. This initiative has already given rise to several promising open-source models, including StarCoder2, CodeLlama, and DeepSeek-Coder, offering a path forward, albeit one that necessitates continued refinement.</p>
<p>Today, we are delighted to introduce a new member of the Qwen1.5 open-source family, the CodeQwen1.5-7B, a specialized codeLLM built upon the Qwen1.5 language model. CodeQwen1.5-7B has been pretrained with around 3 trillion tokens of code-related data. It supports an extensive repertoire of 92 programming languages, and it exhibits exceptional capacity in long-context understanding and generation with the ability to process information of 64K tokens. In terms of performance, CodeQwen1.5 demonstrates impressive capabilities in basic code generation, long-context modeliing, code editation and SQL. We believe this model can significantly enhance developer productivity and streamline software development workflows within diverse technological environments.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="codeqwen-is-a-basic-coder"><a data-card="" href="#codeqwen-is-a-basic-coder" class="peer">CodeQwen is a Basic Coder</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>Code generation is a key competence for large language models, as they are tasked with translating natural language instructions into executable code with unwavering precision. CodeQwen1.5, with only 7 billion parameters, has surpassed larger models in basic code generation capabilities, further narrowing the gap in coding proficiency between GPT-4 and opensource code LLMs. We conducted a thorough evaluation on HumanEval and MBPP to provide a clear and fair comparison as follows.</p>
<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Model</th><th>Size</th><th>HumanEval0-shot</th><th>HumanEval+0-shot</th><th>MBPP0-shot</th><th>MBPP+0-shot</th><th>MBPP3-shot</th></tr></thead><tbody><tr><td><strong>Base Model</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CodeLlama-Base</td><td>7B</td><td>33.5</td><td>25.6</td><td>52.1</td><td>41.6</td><td>38.6</td></tr><tr><td>StarCoder2</td><td>7B</td><td>35.4</td><td>29.9</td><td>54.4</td><td>45.6</td><td>51.0</td></tr><tr><td>DeepSeek-Coder-Base</td><td>6.7B</td><td>47.6</td><td>39.6</td><td>70.2</td><td>56.6</td><td>60.6</td></tr><tr><td><strong>CodeQwen1.5</strong></td><td>7B</td><td>51.8</td><td>45.7</td><td>72.2</td><td>60.2</td><td>61.8</td></tr><tr><td><strong>Chat Model</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GPT-3.5-Turbo</td><td>-</td><td>76.8</td><td>70.7</td><td>82.5</td><td>69.7</td><td>70.8</td></tr><tr><td>GPT-4-Turbo (Nov 2023)</td><td>-</td><td>85.4</td><td>81.7</td><td>83.5</td><td>70.7</td><td>80.0</td></tr><tr><td>DeepSeek-Coder-Instruct</td><td>6.7B</td><td>78.6</td><td>70.1</td><td>73.2</td><td>63.4</td><td>65.4</td></tr><tr><td><strong>CodeQwen1.5-Chat</strong></td><td>7B</td><td>83.5</td><td>78.7</td><td>77.7</td><td>67.2</td><td>70.6</td></tr></tbody></table></div>
<p>In addition to the widely recognized HumanEval and MBPP benchmarks, we explored LiveCodeBench. This benchmark assesses code performance by introducing fresh challenges sourced from coding competitions such as LeetCode, AtCoder, and CodeForces over time. Our evaluation of CodeQwen1.5 on LiveCodeBench spanned from September 1, 2023, to April 1, 2024. The findings indicate that CodeQwen1.5 ranks among the top open-access models currently available. Note: it is possible that the inclusion of LeetCode data in our pretraining corpus may contribute to the performance in LiveCodeBench.</p>
<p>The evaluations mentioned primarily revolve around Python capabilities; however, CodeQwen1.5 is not merely a Python specialist but also an expert across multiple programming languages. We conducted a comprehensive evaluation of CodeQwen1.5 in the eight mainstream languages featured in MultiPL-E, including Python, C++, Java, PHP, TypeScript, C#, Bash, and JavaScript. The results highlight the exceptional programming capabilities of CodeQwen1.5.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="codeqwen-is-a-long-context-coder"><a data-card="" href="#codeqwen-is-a-long-context-coder" class="peer">CodeQwen is a Long Context Coder</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>Long context capability is crucial for code LLMs, serving as the core skill for understanding repository-level code and becoming a code agent. However, current code models still have very limited support for length, which hinders their potential for practical application. CodeQwen1.5 aims to further advance the progress of open-source code models in long context modeling. To achieve this, we have collected and constructed long sequence code data at the repository level for pre-training. Through careful data proportioning and organization, we have enabled it to support input lengths of up to 64K tokens.</p>
<p><strong>Evaluation 1</strong> : We collected high-quality repo from GitHub Trending repositories on 2024-3-28 that were not included in CodeQwen1.5’s training data to observe the effectiveness of long context modeling. The following figure demonstrates that as the sequence length increases, CodeQwen1.5’s Perplexity (PPL) still manages to maintain a downward trend.</p>
<p><strong>Evaluation 2</strong> : We created a synthetic task called <code>Needle in the Code</code>, inspired by popular long-context evaluations in the text domain. In this task, we inserted a very simple custom function at various positions within a longer codebase (we chose Megatron to honor its contributions to open-source LLMs!) and tested whether the model could replicate this function at the end of the codebase. The figure below shows that CodeQwen is capable of successfully completing this task within a 64k length range.</p>
<p>Both Evaluation 1 and Evaluation 2 serve as initial and foundational assessments. For the Chat model, we aim to evaluate its long context capabilities with more practical tasks. However, our objective is to examine the Chat model’s capability to handle long contexts through more pragmatic, real-world evaluation tasks.</p>
<p><strong>Evaluation 3</strong> : SWE Bench is a benchmark designed to assess the ability of Large Language Models (LLMs) or agents to tackle practical software development challenges. It presents contestants with a code repository and an associated issue, tasking them with generating a commit patch that resolves the issue effectively. The benchmark uniquely emphasizes the long-context processing capabilities of code LLMs, necessitating both deep comprehension of the given codebase and the generation of extensive, unit-test-passing code.</p>
<p>Currently, participants in the SWE Bench competition predominantly are proprietary models. We introduce CodeQwen1.5 as an open-source model entry. Despite achieving a score of 0.89, CodeQwen1.5 surpasses ChatGPT-3.5, demonstrating the nascent yet promising competitiveness of open-source code models against their proprietary counterparts.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="codeqwen-is-a-debugger"><a data-card="" href="#codeqwen-is-a-debugger" class="peer">CodeQwen is a Debugger</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>An effective code assistant must demonstrate proficiency in both generating code in response to given specifications and adeptly modifying or debugging existing code to accommodate evolving requirements or rectify errors. In assessing CodeQwen1.5’s proficiency in code modification tasks, we concentrated our evaluation on the CodeEditorBench suite, encompassing four distinct dimensions: Debugging, Translation, Language Switching, and Code Polishing. The results indicate that CodeQwen1.5 achieves the SOTA performance at the 7 billion parameter scale.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="codeqwen-is-a-sqler"><a data-card="" href="#codeqwen-is-a-sqler" class="peer">CodeQwen is a SQLer</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>CodeQwen1.5 serves as a solution to bridge the gap between non-programming professionals and efficient data interaction. It alleviates the steep learning curve associated with SQL by enabling users without coding expertise to query databases through natural language. We evaluated CodeQwen1.5-Chat’s performance on two popular Text-to-SQL benchmarks, Spider and Bird. Experimental results pose CodeQwen1.5 a second position close to GPT-4 (results come from DIN-SQL, a SOTA prompting method). This outstanding performance is attributed to the utilization of synthetic data throughout both pre-training and fine-tuning stages. Synthetic data, characterized by its scalability, verifiability, and variety, emerges as a compelling area for future research due to its proven effectiveness in enhancing CodeQwen1.5’s SQL capabilities.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="develop-with-codeqwen15"><a data-card="" href="#develop-with-codeqwen15" class="peer">Develop with CodeQwen1.5</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>CodeQwen1.5 is part of the Qwen1.5 open-source family. We advise you to read our blog for <a href="https://qwenlm.github.io/blog/qwen1.5/" rel="noreferrer noopener" target="_blank">Qwen1.5</a> to figure out the usages with Transformers, vLLM, llama.cpp, Ollama, etc.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="conclusion"><a data-card="" href="#conclusion" class="peer">Conclusion</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<p>We have released CodeQwen1.5-7B and CodeQwen1.5-7B-Chat, an open and versatile code LLM. The models are intended to aid progress in code assistance and code agents, benefiting the research community. We’ll keep investing heavily in smart code development, with the ultimate goal of creating AI programmers.</p>
<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="citation"><a data-card="" href="#citation" class="peer">Citation</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h1>
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&amp;_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
<span class="line"><span> @misc{codeqwen1.5,</span></span>
<span class="line"><span> title = {Code with CodeQwen1.5},</span></span>
<span class="line"><span> url = {https://qwenlm.github.io/blog/codeqwen1.5/},</span></span>
<span class="line"><span> author = {Qwen Team},</span></span>
<span class="line"><span> month = {April},</span></span>
<span class="line"><span> year = {2024}</span></span>
<span class="line"><span> }</span></span>
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