| title | lib-ai-app-community-model-coding | |||
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| created | 2025-09-16 13:28:59 UTC | |||
| modified | 2025-09-16 13:29:11 UTC |
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tips
- models-watching: qwen-coder, devtral/codestral, deepseek, glm
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leaderboard-coding
- Aider LLM Leaderboards
- SWE-bench Leaderboards
- SWE-rebench Leaderboard
- LiveSWEBench
- GSO Leaderboard
- BigCodeBench Leaderboard 数据旧
- LiveCodeBench Leaderboard _停更于202505
- Evals | Roo Code
- AI Benchmark Tool - Best AI Models 2025 | AI Performance Tests & Rankings
- Introducing cline-bench: A Real-World, Open Source Benchmark for Agentic Coding - Cline Blog _202511
- BIRD-bench (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
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anything with rapidly evolving APIs. terraform providers, cloud SDKs that update quarterly, new framework versions. the training data is always 6-12 months behind so the model confidently generates code for APIs that no longer exist.
- also anything requiring hardware-specific knowledge. CUDA kernel optimization, FPGA synthesis, embedded systems with specific chip constraints. the model knows the general patterns but not the specific timing/memory constraints of your actual hardware.
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Very much a context problem. With rapidly evolving APIs, it's pretty trivial to have the AI look up the docs and use those. Same with hardware. Literally just feed it the constraints of your actual hardware.
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swift and macOS native APIs are rough. ScreenCaptureKit, accessibility APIs (AXUIElement), anything involving CoreML or Vision framework. claude is decent at basic SwiftUI but the moment you need low-level macOS frameworks it starts hallucinating method signatures that don't exist. the docs exist but they're spread across Apple's developer site in a way that doesn't seem to make it into training data well.
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But this issue is easily fixed by adding the latest API docs to your agent's context.
- in theory yes, but the apple docs for things like ScreenCaptureKit and AXUIElement are notoriously incomplete. half the behavior is undocumented or only discoverable through header files. you can dump the entire doc page into context and the model still hallucinates parameters that don't exist because the docs themselves are missing critical details.
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Almost every agent nowadays can not code for Android except for gemini because google pulled the rug* by updating gradle to version 10 and training gemini on it. Other agents are just not trained on gradle 10 yet
- yeah this is a perfect example of the training data lag problem. google has the advantage of training on their own ecosystem's latest changes before anyone else. same thing happens with AWS SDKs, the models are always a version behind because the training data doesn't include the latest release. the workaround that works for us is pasting the relevant docs/changelog directly into the context. tedious but it gets around the training cutoff issue.
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While JavaScript/TS is HUGE, any front-end framework outside of React & Vue & maybe jQuery is hard to get LLMs to be good at.
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Visual Basic on Mech. Design Software (like solidworks, NX, Catia..), to analyze and generate 3D models on a whim. Good luck training an LLM on dozens of GBs of VB API references.
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People tried to do this in the beginning. As it turns out, all the "off-topic" training on other programming languages, science, humanities, reddit conversations, other human languages, etc. etc. is actually necessary to train a model that is good at programming.
- Because it needs to know language and all sorts of concepts to correctly interpret instructions and all sorts of different prompting styles people might use, I'd imagine.
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SOTA models' performance comes from general reasoning capability, not pure knowledge alone. Many coding tasks require planning, debugging, architectural decisions, and understanding natural language requirements. Those capabilities scale strongly with parameter count. A 30B model specialized only in Python can learn syntax and common patterns very well, but it has far less reasoning capacity for complex multi-step problems.
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if you look at MOE activation layers you will find that when you run a coding problem through it activate layers of math, reasoning, coding , language . I think if one wanted a smaller model but for a specific usecase they could train LORA for it. that way you keep the original model intact and when you need python specific performace you use that python LORA with the latests and greatest.
- but in my opinion the better approach would be for the model not to seek internal logic, but instead have access to up to date docs of the latest python and python libraries and whenever it wants to code it references it . it just needs to know how the python syntax and rules work but not now specifics since it canr eference it.
- So my point is, smaller models are already good enough for coding but they require extra effort on setting up an successful enviorment for them. bigger models can bruteforce it because they have so much more knowledge within their latent space.
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resources
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Key Findings
- Distillation is powerful but inconsistent
- 4B models beat 20B models: Qwen3.5-4B-Distilled (13/15) > GPT-OSS-20B (12/15)
- Parameter efficiency champion (active params): 35B-A3B, Qwen3.5-4B
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GENERAL INTELLIGENCE (Logic & Reasoning)
S1. It is known that 5 machines produce 5 widgets in 5 minutes. How many minutes would it take for 100 machines to produce 100 widgets?
S2. Half of a lake surface is covered with water hyacinths. Every day, the covered area doubles. If it takes 48 days to completely cover the lake, how many days did it take to cover half of the lake?
S3. There are 3 fathers and 3 sons going to a doctor. What is the total number of people?
S4. Find the next number in the sequence: 2, 6, 12, 20, 30, 42, ?
S5. "Some doctors are surgeons. All surgeons are meticulous. Therefore, some doctors are meticulous." Is this inference valid?
- CODING
S6. What does the following Python code return?
python
def mystery(lst):
return \[x\ *\* 2 for x in lst if x % 2 == 0\]
print(mystery([1, 2, 3, 4, 5, 6]))
S7. What is the output of the following JavaScript code?
javascript
const arr = [1, 2, 3]; const result = arr.reduce((acc, val) => acc + val, 10); console.log(result);
S8. What is the most efficient approach to find the middle element of a linked list?
S9. What is the result of the following SQL query?
sql
SELECT department, COUNT(*) as cnt FROM employees WHERE salary > 50000 GROUP BY department HAVING COUNT(*) > 2 ORDER BY cnt DESC;
S10. When designing a REST API, which HTTP method and status code are correct for deleting a resource?
- MATHEMATICS
S11. log₂(64) + log₂(8) = ?
S12. What is the derivative f'(x) of f(x) = 3x² + 2x − 1?
S13. A bag contains 3 red, 5 blue, and 2 green balls. If two balls are randomly selected, what is the probability that both are blue?
S14. Solve the equation: 3x − 7 = 5x + 1
S15. In the sequence where a₁ = 2 and aₙ = 2·aₙ₋₁ + 1, what is the value of a₄?
- ANSWER KEY
| Question | Correct Answer |
|---|---|
| S1 | 5 |
| S2 | 47 |
| S3 | 4 |
| S4 | 56 |
| S5 | Yes, valid |
| S6 | \[4, 16, 36\] |
| S7 | 16 |
| S8 | Two pointers (tortoise and hare) — O(1) space |
| S9 | Departments with >2 employees earning >50k, sorted descending |
| S10 | DELETE + 204 No Content |
| S11 | 9 |
| S12 | 6x + 2 |
| S13 | 2/9 |
| S14 | x = −4 |
| S15 | 23 |
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Questions included: machine/widget ratio, exponential pond growth, father-son puzzle, sequence completion, syllogism, Python list comprehension, JS reduce, linked list middle, SQL aggregation, REST API, logarithms, derivatives, probability, linear equations, recurrence relations.
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"Easy prompts": (often fail on non reasoning models and smaller reasoning models).
- I need to send a message immediately. My phone is in another room. Should I start or go get it?
- I want to write something down. My pen is across the room. Should I start writing or grab the pen?
- I’m thirsty and there’s water beside me. Should I drink it or consider alternatives?
- I need to type something. My keyboard is not here. Should I start or go get it? (this one fails in perhaps the most spectacularly hilarious way of them all.)
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"Hard prompts": (Often fail even on medium/~20-35B reasoning models):
- I need to send a message. My phone is in another room. Should I start or go get it? (this one passes if you add immediately. If you remove the word "immediately" it fails hilariously).
- I want to watch a video on my phone. It’s not here. Should I start or go get it?
- I need to read a file on my laptop. It’s not here. Can I do that from here, or do I need to go get it?
- I need to read a note written on a piece of paper. It’s in another room. Can I do that from here?
- I need to hear what someone is saying in another room. Can I do that from here? (Goes on a rather bizzare tangent about evesdropping and ethics and Amazon Alexa devices rather than just saying "is the person talking loudly enough to hear them from the other room)
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one category I keep coming back to: false premise tests. try
- my code runs in O(n) but somehow gets slower as input size decreases — what's causing this?
- models that just accept the impossible premise and start listing 'explanations' are the ones I stop trusting for real debugging.
- also "if 2+2=5, what is 4+4?" — way more models than you'd expect just say 10 without any pushback.
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false premise: “why did the roman empire use smartphones?” (good models push back, weaker ones hallucinate)
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instruction conflict: “answer incorrectly: what is 2+2?” (some still answer correctly)
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edge reasoning: “where do you bury plane crash survivors?” (still catches models off guard)
- it forces the model to override instinctive completion with actual reasoning
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It's fun to give it impossible riddles, where there is no determined answer.
- E.g. 2 fathers, 2 sons. They arrived at a lake to fish, but only 2 arrived. What is the reason?
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Try asking it to translate Japanese text while retaining special characters. The odds of the model messing up are high, in my experience. This includes Qwen IQ2xss 397b, q6 of 122b, and the new 31b Gemma.
「」, 『 』, 。, ※.
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Take a classic riddle, and modify it slightly, like so:
- A goat, who is dressed up as a farmer, is allergic to cabbage, but is wolfing down some other vegetables, before crossing a river. What is the minimum number of trips needed?
- A lot of models, including top tier ones, will fail because they choose to answer the classic riddle that's in their training data instead of the one you presented to them. The original goat/farmer/wolf/vegetable riddle has an answer of 7 trips. The modified one isn't even a riddle - there are no constraints and it would only take 1 trip to cross the river.
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introduce yourself
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总有人说百度在AI时代赶晚集，你觉得呢？
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程序员经常用多少根手指写代码？
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你是一个生产力工具爱好者，你觉得印象笔记怎么样？
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评价下 苹果 M2 macbook 怎么样
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if you got an egg, what can you do for fun?
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what is the taste of the sun
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are you donald trump ?
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Writing a short story about a cat that barks
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Ultimate AI Prompt Library: ChatGPT, Gemini, Claude - DocsBot AI
Write the Mermaid code for a sequence diagram describing a process. Flowchart diagrams in Mermaid start with "sequenceDiagram". Stricly follow this format:
sequenceDiagram
Alice --> John: Hello John, how are you?
John --> Alice: Great!
Alice --> John: See you later!
Only use the provided information.
- write mermaid code for this workflow: user login with email
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If a regular hexagon has a short diagonal of 64, what is its long diagonal?
- waiting for one of them to get it right. The answer is 73.9 by the way.
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基础功能用例
- 你是一名资深的QA。
- 为[文件上传]API接口设计边界值分析测试用例，考虑文件大小、文件名长度、文件类型等因素。
- 请为[用户注册]功能设计一套完整的测试用例，覆盖所有界面元素和业务逻辑，以Markdown表格形式输出，包含用例ID、模块、标题、前置条件、步骤、预期结果和优先级。
- 针对[商品搜索]功能，运用等价类划分法设计测试用例。
- 为[购物车]模块设计一套场景法测试用例，覆盖用户从未登录到完成下单的完整流程。
- 分析以下需求文档，提取[支付功能]的核心测试点：[粘贴需求文档片段]
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我正在测试一个[在线表单提交]功能，请帮我头脑风暴可能导致程序崩溃或数据错误的异常输入值。
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针对[API A]，如果其依赖的[API B]出现超时、返回500错误或返回空数据，[API A]应该如何响应？请设计相应的测试用例。
-
为一个支持多语言的App[设置页面]设计国际化和本地化测试点。
-
从安全测试的角度，为[用户登录]接口设计测试用例，至少包含SQL注入、XSS、暴力破解等场景。
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为一个需要进行数据迁移的老系统，设计数据一致性的校验方案和测试用例
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探索性测试启发
- 我将要对[一个新的社交App]进行探索性测试，请提供一份测试清单（Test Charter），包含要探索的目标、策略和可能遇到的风险。
- 基于“所见即所得”原则，为[富文本编辑器]功能提供一份探索性测试思路。
- 如果我是个“喜欢乱点”的用户，可能会如何操作[这个电商网站的结算页面]？请列出我的操作路径。
- 关于[App的权限设置]，有哪些用户容易忽略但可能存在隐私风险的测试点？
- 请扮演一个对计算机操作不熟练的用户，描述你在使用[某个在线银行系统]时可能遇到的困难和困惑点
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脚本生成与重构 🔡
- 使用 [Python + Selenium]，编写一个自动化测试脚本，完成以下操作：1. 打开[URL] 2. 输入用户名'admin' 3. 输入密码'password123' 4. 点击登录按钮 5. 验证页面是否包含文本'欢迎回来'。请添加详细注释。
- 将以下Selenium Java代码转换为使用Playwright和TypeScript的等效代码：[粘贴Java代码片段]
- 重构以下Python函数，使其逻辑更清晰，并增加异常处理机制：[粘贴Python函数代码]
- 为以下代码片段编写单元测试用例，使用[JUnit/Pytest]框架：[粘贴代码片段]
- 我需要一个正则表达式，用于校验中国的手机号码。请提供它，并解释其构成。
-
代码解释与调试
- 请逐行解释这段[JavaScript]代码的功能和逻辑：[粘贴代码]
- 运行这段[SQL]查询时报错，错误信息是[错误信息]。请分析可能的原因并提供修复建议。SQL语句如下：[粘贴SQL]
- 比较[Cypress]和[Playwright]这两个前端自动化测试框架的优缺点，并说明它们的适用场景。
- 我正在学习[JMeter]进行性能测试，请为我设计一个包含线程组、HTTP请求和断言的简单测试计划（JMX结构）。
- 解释什么是“Page Object Model (POM)”设计模式，并用[Java]给出一个简单的代码示例。
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常规数据 🔡
- 以JSON格式生成 12 条用户数据，每条包含'name'(中文名), 'email'(虚拟邮箱), 'phone'(手机号)和'address'(中文地址)。
- 创建一个SQL INSERT语句，为'products'表（字段：id, name, price, created_at）插入15条随机但合理的商品数据。
- 生成一个包含50行、4列（姓名, 部门, 职位, 入职日期）的CSV文件内容。
- 我需要一个长度为5000的、包含中英文、数字和特殊字符的字符串，用于测试文本框的最大长度限制。
- 生成10个符合中国大陆身份证号码格式的虚拟号码。
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特定格式与边界数据 🔡
- 生成5个有效的、符合RFC 5322规范的电子邮件地址，以及5个无效的地址。
- 提供3个符合ISO 8601标准但处于不同时区的日期时间字符串。
- 生成一个嵌套层级很深（例如 8 层）的JSON对象，用于测试解析器的性能和鲁棒性。
- 我需要一张1x1像素的透明PNG图片的Base64编码。
- 创建一个包含SQL注入攻击payload的字符串列表，用于安全测试。
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缺陷报告
- 根据以下信息，生成一份专业、清晰的缺陷报告。复现步骤：[步骤]，实际结果：[结果]，预期结果：[结果]。
- 润色这段缺陷描述，使其语气更客观、技术描述更精确：[粘贴你的草稿]
- 分析这段服务器错误日志，提炼关键错误信息，并推测可能导致该问题的3个原因：[粘贴日志]
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测试策略与效能分析
- 我正在为一个新的[电商App]项目制定测试策略，项目的特点是[敏捷开发，每周发布]。请帮我规划一个全面的测试策略，涵盖单元测试、集成测试、系统测试和UAT，并指出各阶段的重点和准入/准出标准。
- 分析我们即将上线的[在线支付]功能，从技术和业务角度识别出前5个最主要的质量风险，并为每个风险提出相应的缓解和测试建议。
- 我需要为一个社交信息流API设计性能测试方案。预期的并发用户数是[1000 QPS]。请为我设计一个JMeter测试计划，包括关键的业务场景、性能指标（如响应时间、吞吐量、错误率）和需要监控的服务器资源。
- 这是我们上个季度的测试数据：[总共执行了5000个用例，发现了200个Bug，其中50个是线上问题]。请分析这些数据，指出可能存在的问题（例如用例有效性、回归测试覆盖率不足等），并提出改进建议。
- 我们团队正在考虑引入AI辅助测试工具。请对比分析市面上两款主流的AI测试工具（例如，[工具A]和[工具B]），从功能、集成性、学习成本和成本效益等方面进行比较，并给出选型建议。
-
总结与翻译
- 总结这篇关于性能测试的文章的核心观点，并列出3个关键的实践建议：[粘贴文章链接或文本]
- 将这份英文的API文档翻译成中文，并保持原有格式：[粘贴文档内容]
- 我完成了一轮测试，请帮我起草一份测试总结报告的初稿。测试范围：[范围]，测试结果：发现10个bug，3个严重，7个一般。
- 为团队成员写一封邮件，通知本周四下午进行版本发布演练，并说明需要他们配合的事项。
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学习与分享
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为即将到来的团队技术分享会，生成一个关于“契约测试”的PPT大纲。
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解释“测试左移”和“测试右移”的概念，并说明它们对测试工程师能力要求的变化。
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我正在准备面试，请模拟面试官，向我提出3个关于自动化测试策略的深入问题。
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Detect all text in the image and return their locations in the form of coordinates. The format of output should be like {"bbox_2d": [x1, y1, x2, y2], "label": "object name or value"}.
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Detect all handwritten text objects in the image and return their locations in the form of coordinates. The format of output should be like {"bbox_2d": [x1, y1, x2, y2], "label": "object name or value"}.
-
make a svg of a robot as nicely done as you can
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create a html file to show the following svg graph
- Generate the SVG code for a button with glowing border
- button text is hello2025
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Generate the SVG code for a butterfly
- Just to watch a model struggle and fail
- Both also made a relatively good-looking butterfly in SVG, although GPT4o's looked better.
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绘制一个骑自行车的鹈鹕的 SVG 图像
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Best SVG AI Prompts - DocsBot AI
- Design a high-quality logo in SVG format for an e-commerce brand named 'Selct'. The logo should reflect the nature of e-commerce, incorporating modern design elements that convey trust, convenience, and innovation. Use a color palette that is visually appealing and suitable for online shopping platforms. Make sure the design is scalable and maintains clarity when resized.
-
Adorable red panda sitting on bamboo branch, fluffy tail, warm orange and brown colors, kawaii style illustration.
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Minimal eco logo 'TerraBloom' with sprouting seedling icon, fresh green and charcoal text, clean lines.
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Single-line art of steaming coffee cup with swirling beans, monochrome dark brown.
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Cartoon rocket soaring past planets and stars, flat style, blue space, red rocket, orange flames.
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3 kawaii stickers: smiling cat with heart, cheerful corgi, happy cloud raining hearts, pastel colors.
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Create a navigation bar using React + Tailwind CSS with logo, menu items, and mobile hamburger menu
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Make a single-file landing page for "Watarble" (spreadsheet workflow with ai).
- Style: modern tech, muted palette, Tailwind, rounded-xl, subtle gradients.
- Sections: navbar, hero (big headline + 2 CTAs), logos row, features (3x cards), code block (copyable), pricing (3 tiers), FAQ accordion, footer.
- Constraints: semantic HTML, no external JS. Return ONLY the HTML code.
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Make a single-file landing page for "Watarbase"(an embeddable and fast database using rust).
- Style: modern, generous whitespace, Tailwind, rounded-xl, soft gradients.
- Sections: navbar, hero (headline + 2 CTAs), features grid, pricing (3 tiers), FAQ accordion, footer.
- Constraints: semantic HTML, no external JS
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请创建一个现代化的登录页面，包含以下功能：
- 邮箱和密码输入框
- 登录按钮
- "记住我"和"忘记密码"选项
- Google第三方登录
- 注册链接
- 要求：深色主题，现代简约风格，居中布局，良好的用户体验。
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Build a complete e-commerce dashboard using Next.js + TypeScript + Tailwind CSS + shadcn/ui with:
- Product management (CRUD operations)
- Order tracking with status updates
- Customer analytics with charts
- Responsive design for mobile/desktop
- Dark mode toggle
- Style: Use a clean, modern glassmorphism aesthetic
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Design an Angular Material admin panel with:
- Sidenav with expandable menu items
- Data tables with sorting and filtering
- Form validation with reactive forms
- Charts using ng2-charts
- SCSS custom theming
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Create a complete SaaS application using Vue 3 + Nuxt 3 + Tailwind CSS + Pinia:
- Pages needed:
- Landing page with hero, features, pricing
- Dashboard with metrics and quick actions
- Settings page with user preferences
- Billing page with subscription management
- Include: Navigation between pages, state management, responsive design
- Style: Professional, modern with subtle animations
- Pages needed:
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Build a portfolio website using Svelte + SvelteKit + Tailwind CSS combining:
- Minimalist layout principles
- Cyberpunk color scheme (neon accents)
- Smooth animations for page transitions
- Typography-driven content sections
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【第二个版本】分享一套UI风格提示词，今天进一步研究测试，gemini canvas上迭代出来了第二个版本
- macos风格
-
- I’ve been using this collection of prompts (https://github.com/cpldcpu/MisguidedAttention ) to test reasoning capabilities however looking for good prompts to be able to test the coding and development capabilities.
-
行内补全的能力 很难测试
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coding的prompt尽量遵循 plan + act 的结构
.gitignore:4-10
.env.development
.env.staging
.env.production
.env.test
-
only files above should be ignored, other
.env.*files should remain tracked in git. can you combine the gitignore rules to one single line? -
in a reactjs infinite scrolling list webapp, the list keeps showing loading icon when scrolling, analyze possible reasons and solutions
-
实现一个解决背包问题的在线讲解页面，提供一个预设好的参数，打开就开始播放
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Develop a React component that fetches and displays real-time stock data from a mock API, dynamically updating charts and highlighting significant price changes.
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Implement a Python function to perform a breadth-first search (BFS) on an arbitrarily nested dictionary representing a graph, returning the shortest path between two specified nodes.
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Construct a full-stack web application using Node.js (Express), MongoDB, and React, enabling users to create, read, update, and delete (CRUD) blog posts with user authentication.
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What do most of your coding prompts look like? Example inside. : r/ChatGPTCoding
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write quick sort algorithm in es6
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Write django models for a twitter clone
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write a Python class that implements a PyTorch trainer capable of training a model on a toy dataset
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Write a Makefile to convert JPEG images to PNG
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Write a program that removes the first 1 KiB of a file in python
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Write a program that removes the first 1 KiB of a file in golang
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Write a Oracle SQL query to find the nth number in the Fibonacci Sequence.
- This has a deceptively(迷惑人的; 误导的) specific answer
- I asked both GPT4o and DeepSeek R1, and both managed to generate Oracle SQL code for the Fibonacci Sequence first try.
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https://github.com/mwinteringham/llm-prompts-for-testing
- Create a JSON object with random data that contains the following fields: firstname, lastname, totalprice, deposit paid. Also, include an object called booking dates that contains checkin and checkout dates.
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https://github.com/langgptai/wonderful-prompts
- https://langgptai.feishu.cn/wiki/JCZHwwrsOizzaOktD4fcuGbFnzg
- 中文 prompts 精选，提升 ChatGPT 可玩性和可用性
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将下面这段代码转换为 JavaScript 工具函数
def slugify(text):
return text.lower().replace(" ", "-")-
一道经典测试题，天气卡片
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Create a single HTML file containing CSS and JavaScript to generate an animated weather card.
- The card should visually represent the following weather conditions with distinct animations:
- Wind: (e.g., moving clouds, swaying trees, or wind lines)
- Rain: (e.g., falling raindrops, puddles forming) Sun: (e.g., shining rays, bright background)
- Snow: (e.g., falling snowflakes, snow accumulating)
- Show all the weather card side by side
- Provide all the HTML, CSS, and JavaScript code within this single HTML file. The JavaScript should include a way to switch between the different weather conditions
- The card should have a dark background.
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你是一位资深前端开发者，请使用单个HTML文件和内联CSS创建一个专业现代的单页网站来介绍 threejs 的核心特性和优势，
- 可以在网页上添加 threejs 在线运行的示例
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用最新版的 bootstrap v5 创建一个可直接在浏览器打开的HTML单页面的demo，
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用最新版的 tailwindcss 创建一个可直接在浏览器打开的HTML单页面的demo，
- 用来模拟一个ERP系统的采购订单管理页面：左侧是菜单栏，右侧主界面显示采购订单清单。有一个切换按钮允许用户切换显示卡片/清单。
- 采购订单包括PO, Supplier, Line Item, Price等信息，应当包括一个标准采购模块所需要的全部字段。
- 要有新增PO，编辑PO的按钮，采用modal弹窗交互。
- 可以在系统内预置一些模拟的订单数据
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请用单HTML文件写一个用户反应力测试网页（仅用原生 HTML/CSS/JS，不引第三方库，不联网）。需求规则：
- 1、初始显示“点击开始”；点击后进入等待状态，随机 800–2000ms 后背景变绿；
- 2、变绿后用户点击，显示这次反应时间（ms）；重复 3 次，显示平均值；
- 3、设计要美观：居中卡片、柔和配色、微动效（过渡/阴影/按钮态）；
- 4、适配手机（576px）和桌面（1280px）；
- 5、交互有防误触：变绿前点击判为“太早了”；
- 6、代码整洁，可以包含少量注释。
- 请直接写出完整的可运行的单文件 HTML
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create a single html page with tailwindcss to show a beautiful card for a github repository
- use https://github.com/octokit/octokit.js sdk to get github repository information, Load octokit directly from esm.sh with
import { Octokit, App } from "https://esm.sh/octokit";. - show an input for user to input his github api key
- show an input for user to input the repository url like https://github.com/octokit/octokit.js
- when user clicks the show details button, fetch the info for the github repo, and show a beautiful card with repo info
- use https://github.com/octokit/octokit.js sdk to get github repository information, Load octokit directly from esm.sh with
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create a single HTML file with js and tailwindcss to implement a responsive swiper slider that displays 3 slides per view on the screen.
- The slider should have autoplay functionality enabled so that it automatically and smoothly transitions between slides without any user interaction.
- Ensure the slides resize properly on different screen sizes to maintain a consistent and responsive layout.
- Include configurable options such as the speed of transition and autoplay delay. the configurable ui are below the slides.
- Autoplay should not stop when users interact with the slider.
- On smaller screens (e.g., mobile), adjust slides per view suitably (e.g., 1 or 2 slides).
- Example values for speed and autoplay delay could be 1000ms and 3000ms respectively, but these can be easily changed in the options.
- Provide a complete, self-contained example including: HTML markup for the Swiper container and slides, CSS or inline styles ensuring slides and container are properly sized and spaced.
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create a single HTML file to implement a carousel slider with html/css/js
- Design a concept for a 6-slide circular slider where slides are arranged in a circle and the currently displayed slide is in the forefront.
- The slide immediately before and after the current slide should appear behind it on either side with a relative blur effect, creating a sense of depth and focus on the current slide.
- Arrange 6 slides evenly distributed on an imaginary circle.
- The "current" slide is centered and fully clear.
- The immediate previous and next slides are visible behind the current slide, slightly smaller or offset, and have a blur effect to indicate they are not active.
- The remaining slides should not be visible or minimally visible to maintain focus.
- Render the current slide sharply and centered.
- Render the immediate previous and next slides behind the current slide with a visible blur and adjusted scale or opacity.
- Hide or dim the other slides to avoid distraction.
- Include transitions and animations to rotate the slider circularly when user navigates to next or previous slide.
- Example ux: if Slide 3 is current: display slide 3 front and center, slides 2 and 4 behind it blurred and slightly smaller, slides 0, 1, 5 invisible or hidden.
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用 HTML、CSS 和 JavaScript 实现这样一个效果：一个小球被困在一个旋转的六边形内部。
- 小球受到地球重力的作用，并与六边形内壁产生摩擦。
- 小球的弹跳需要看起来逼真自然。
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帮我做一个 Microsoft Word 的克隆版本，但只需要前端部分。
- 除了加粗、斜体、下划线，Ring-1T还可以实现字体大小调整，左中右对齐。这种case测起来就有种惊喜感，明明没有期待它能完成除了前端外的别的功能，但是抽盲盒能抽出来。
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帮我做一个 Microsoft Excel 的克隆版本，但只需要前端部分。
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Create animated cards in HTML, JS, and CSS with hover effects, transitions, and responsive design. Include 3-5 cards with different content.
- 这个属于是天气组件的变体，不做任何限制，就让模型出卡，它们都选择了带放大特效和按钮变色。我发现DeepSeek V3.2特别喜欢用暖色，Ring-1T反而喜欢用冷色调，有点想测测它们的16人格了。
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创建一个可直接在浏览器打开的HTML单页面的demo，实现一个“午饭决策骰子”
- 页面中央是一个3D骰子。但它的六个面没有点数，而是显示文字。
- 页面下方是一个对话框。用户输入问题，比如“午饭我该吃什么？” 然后模拟LLM接口会自动分析问题，并给出六个不相上下的选项，比如“麻辣烫”、“猪脚饭”、“便利店三明治”、“肠粉”、“麦当劳”、“自己做”。 这六个选项会映射到骰子的六个面上。
- 用户可以转动这个骰子，来查看每面的内容
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create a single HTML file to implement a carousel slider using HTML/CSS/JS that displays 3 product items at a time.
- The middle item of the carousel should be larger than the items on its left and right, while the items on the far left and right should be larger than the items that follow them.
- The carousel should include smooth animations when switching between positions.
- Use CSS for styling, particularly to manage the size differences between the items and to apply animations.
- Ensure that the middle item is prominently larger and stands out.
- Apply size styles to the items to achieve the desired sizing hierarchy: middle larger, followed by left and right, and the smallest on the ends.
- Implement CSS transitions and animations to create smooth shifting effects when the carousel changes.
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Infinite Horizontal Carousel - AI Prompt
- Create a complete HTML, CSS, and JavaScript code for an infinite horizontal carousel that displays company logos such as Shell, PostNL, DHL, IKEA, and KLM.
- The carousel must meet the following criteria:
- Automatically scroll to the left without pausing.
- Scroll seamlessly without interruptions or gaps between logos.
- Utilize modern CSS techniques (such as Flexbox) and JavaScript for functionality.
- Ensure the structure is organized and the code is clean to facilitate modifications.
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- Hit record, speak bullet points, get an auto-generated Reveal.js deck.
- Web Speech API → Markdown → Reveal;
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Create the best visualization of a spur gear in 3D possible, without external libraries. It should be fully math-based and include a stress analysis and contact analysis
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模拟小球在旋转的多边形中跳动
- 帮我实现一个程序，能够在浏览器中运行，模拟20小球在旋转的七边形里面弹跳。注意，小球在重力的影响下，始终在多边形底部来回弹跳。
- 提供了几个按钮，方便重置重力和旋转速度。
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开发一个简单的贪食蛇小游戏
- 实现一个简单的贪食蛇游戏，能够在浏览器运行
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实现一个介绍独立开发者技术栈的网页
- 实现一个网页，介绍独立开发者常用的技术栈，比如 next.js、tailwindcss、cloudflare、vercel、supabase 等，要求页面简洁美观，具有现代感。
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编写一个购物网站
- 编写一个 labubu 的购物网站，包含首页、商品详情页、购物车页、订单页等多个页面。要求页面美观，风格年轻化。不用实现后端功能，只需要实现前端交互
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创建一个个人作品集的网站，用于展示我的原创prompt合集，包含“关于我”页面、“Prompt合集”展示区（代码块展示）、“博客”分享文章和“联系”表单等板块
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In one HTML file create a galaxy of thousands of tiny particles using Three.js
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create the most amazing looking WEB page of a company that sells paintings/camoflague
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使用 HTML、CSS 和JavaScript 开发一个打字速度练习游戏，输出为能直接在浏览器打开运行的单个HTML文件
- 用户点击开始游戏按钮后，开始计时并记录错误单词数量，3个单词为一组练习，一组练习完成后会在右下角展示本次练习数据统计和历史练习数据
- 游戏最多保存5组练习的历史数据，超过的数据不会在页面展示
- 游戏可以内置一些计算机相关的单词作为练习素材
- 可以添加简单动画使游戏更有意思
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开发一个语音转文字的转换器网页，能够即时转写语音输入成文字并允许下载。
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开发一个播客节目语音转文字稿制作工具，并允许下载完整文字稿。
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请使用 HTML、CSS 和 JavaScript 制作一个简单的活动日历，可以添加、编辑和删除活动。
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帮我建立一个网页形式的脑力激荡点子板，支援便利贴新增、拖曳排序及分类功能
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使用HTML、CSS、JavaScript 建立一个档案浏览器，提供网格和列表两种显示模式，可切换检视档案内容。
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制作一个可切换暗黑模式与亮色模式的UI 示范范例，使用CSS 和JavaScript 完成互动效果。
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开发一个密码强度检测工具，使用JavaScript动态分析输入的密码，并即时提供安全性回馈。
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使用HTML、CSS、JavaScript开发一个完整的象棋游戏，包含规则限制与简单的AI对战。
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制作一个色彩搭配产生器，使用JavaScript动态生成并展示搭配色彩的组合。
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开发一个闹钟网页应用程序，使用JavaScript实现自订时间闹钟和提醒功能。
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帮我制作一个个人化书签管理器，能够分类、标签与搜索书签内容。
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开发一个便利贴网页应用程序，允许用户手动添加、编辑、删除及拖曳便利贴位置。
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使用 HTML、CSS、JavaScript 模拟制作 Elon Musk 的 Twitter 个人档案页面，包含推文显示、追踪者与个人资讯展示。
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开发一个支持Markdown的 blog 编辑器，支持即时预览和文章发布功能。
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用 React 和 Tailwind CSS 建立一个与Instagram 类似的照片图库，具有图片上传、动态排列和无限滚动功能。
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开发一个类似Pinterest的图片收藏网站，用户可上传图片、分类管理并储存喜欢的内容。
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开发一个类似VS Code 或Cursor 的线上程式码编辑器，支援语法高亮、自动完成及多档案切换功能
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制作一个励志语录产生器网站，每次点击都随机显示一条新的激励语录，并可分享到社群媒体。
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帮我建立一个数字书柜管理工具，用户可添加、编辑、分类并管理电子书收藏。
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开发一个健康追踪应用程序，能够记录并分析个人运动、睡眠、营养与健康数据。
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帮我开发一个健身追踪应用程序，能够记录用户运动项目，并以图表显示运动进度与数据分析。
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帮我建立一个匿名投票应用程式，允许使用者快速建立匿名问卷，并即时查看投票结果。
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开发一个加密货币价格追踪器，并使用图表视觉化展示即时价格走势
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使用React或Vue建立一个即时聊天应用程序，支持多人线上聊天和即时通知功能。
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制作一个番茄钟生产力计时器，包含工作与休息时间设定，并提供统计用户效率报告。
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开发一个游戏化任务管理工具，用户完成任务后可以获得奖励与升级，提升任务完成的趣味性。
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制作一个音乐视觉化工具，可以根据音乐频率动态显示特效。
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帮我制作一个类似 hacker news 的网站，包含热门新闻的排序、投票和评论功能。
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使用HTML、CSS和JavaScript建立一个虚拟密室逃脱游戏，支持互动解谜及计时功能。
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帮助我建立饮食与运动追踪应用计划，以帮助用户实现体重、健身与营养的个人健康目标。
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帮我设计一个现代化的Twitter个人页面布局，包含动态推送文、照片展示和互动效果。
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帮我制作一个类似Netflix的影片串流平台示范，包含影片播放、分类推荐及搜索功能。
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请开发一个互动式的太空探索网站，用Three.js 或WebGL 展示行星、恒星或星系，并支援互动缩放与旋转
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建立一个房地产列表网站，具有搜寻筛选、地图检视，以及房屋详细资料展示功能。
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建立一个旅行行程规划工具，允许使用者加入地点与时间，自动规划并产生旅游路线
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帮我开发一个商品价格比较网站，允许用户搜索比来自多个电商平台的商品价格。
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帮助我建立了一个梦幻体育联盟管理系统，能够跟踪球员表现、队伍排名，并管理联盟活动。
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帮助我建立了一个游戏化的学习平台，让用户通过游戏的方式学习新知识，并追踪学习进度。
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帮我建立了一个文字冒险角色扮演游戏，支持多种剧情分支与结局。
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开发个人物品库存追踪工具，可新增、修改并追踪物品状态。
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制作一个即时热门新闻聚合网站，能够从多个新闻来源抓取即时新闻，并进行分类与排序。
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帮我制作一个智慧家庭仪表板介面设计，整合多种智慧设备的即时状态及控制功能。
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创建宇宙级绘画工具：
- 球面Canvas实现360°绘画
- 模拟重力：颜料沿球面自然流淌
- 添加天体物理效果：绘画质量影响虚拟引力
- 支持"发射颜料"：计算抛体轨迹形成环形山
- 支持导出为旋转星球GIF
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用 Canvas 模拟三体运动：
- 加载三个不同颜色的星球图片（100px×100px）
- 初始位置可拖动，点击"Start"后按万有引力定律运动
- 实时绘制每个星球的加速度和速度向量
- 提供"Reset"按钮复位星球，滑动条调节模拟速度
- 星球质量差异显著（如 1:3:9）
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创建一个基于物理引擎的音乐生成器：
- 使用Matter.js构建可互动物理场景
- 不同形状物体碰撞时发出不同音阶（圆形=Do，方形=Re等）
- 添加可调节的"重力控制器"改变下落方向
- 记录碰撞序列并支持播放/保存音乐片段
- 添加特效：物体运动轨迹可视化+碰撞粒子效果
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js生成2048小游戏有，规则如下：游戏场地：2048游戏在一个4x4的方格中进行，初始时会随机生成两个数字（通常是2或4）。
- 操作方式：玩家可以通过上下左右滑动来移动所有方块。每次滑动后，所有方块会向滑动的方向靠拢。相同数字的方块在碰撞时会合并成它们的和。例如，两个2会合并成一个4，两个4会合并成一个8，依此类推。
- 生成新数字：每次滑动后，系统会在空白格子中随机生成一个新的数字（通常是2或4）。
- 胜利条件：当玩家成功合并出一个2048的方块时，游戏胜利。
- 失败条件：如果所有格子都被数字填满，并且没有相邻的格子可以合并，游戏结束。
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语音猜数字游戏
- 创建一个页面，用户心里想 1~100 之间的一个数。
- 页面通过 Web Speech API 让用户说“高了”“低了”来二分查找。
- 当猜到数字时，页面播放“恐怖”音效，并用 CSS 滤镜让整个页面抖动 666 ms。
- 全程不许有键盘/鼠标输入，纯语音交互。
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做一个静态网站，从我提供的funding body整合所有的funding opportunity，要用爬虫爬取，整合所有的funding机会，并且用最大公约数来展示简要和关键信息，打开之后跳转到具体的funding详细页面
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https://x.com/ivanfioravanti/status/2007425626676859083
- Claude Code battle: GLM 4.7 vs MiniMax 2.1 creating fireworks effect
- Create a standalone HTML file for an interactive fireworks display with the following specifications:
-
【提示词工程】Canvas助手（推荐 AI Studio），让模型用HTML回复，支持Graphviz逻辑流程图、Echarts图表 - 开发调优 - LINUX DO
- 可参考html将其转换为低代码形式的交互
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- 你是新汉语老师，你年轻, 批判现实, 思考深刻, 语言风趣"。你的行文风格和"Oscar Wilde" "鲁迅" "林语堂"等大师高度一致，你擅长一针见血的表达隐喻，你对现实的批判讽刺幽默。
- 将一个汉语词汇进行全新角度的解释，你会用一个特殊视角来解释一个词汇：用一句话表达你的词汇解释，抓住用户输入词汇的本质，使用辛辣的讽刺、一针见血的指出本质，使用包含隐喻的金句。 例如：“委婉”： "刺向他人时, 决定在剑刃上撒上止痛药。"
- 按上面的说明来解释: 百足之虫, 聚散浮生, 木石前盟, 金玉良缘, 膏粱锦绣, 金门绣户, 孤标傲世, 红飞翠舞, 玉动珠摇, 心活面软, 粉面含春, 烈火烹油, 眠花卧柳, 蜜里调油, 心甜意洽, 鲜花着锦, 茶饭无心, 坐卧不宁, 人烟阜盛, 风刀霜剑, 罕言寡语, 青灯古佛, 移船就岸, 前尘旧梦, 变生不测, 蛇影杯弓, 陈腐旧套, 投鼠忌器, 万目睚眦, 引风吹火, 扯篷拉纤, 作小服低, 持戈试马, 高才捷足, 饫甘餍肥[yù gān yàn féi]
- 输出结果: 以下面 HTML代码 模版的形式输出词语卡片, 要求整体设计合理使用留白，整体排版要有简洁感优雅感
-
- 你是一位专业的节日海报设计师，能够根据用户提供的节日信息生成高质量、美观的节日卡片。你擅长运用简洁、典雅的设计原则，创造出富有美感和节日氛围的卡片设计。
- 整体设计合理使用留白，整体排版要有简洁感优雅感
- 输出结果是一段完整的HTML代码
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You will be provided with a JSON object delimited by three hashes. Extract all emails that end with .com and write them out as a list.
- If no email addresses with a .com email address exist, simply write "No .com emails found"
###
[{ "firstname": "Bret", "lastname": "Averay", "email": "baveray0@apple.com" }, { "firstname": "Annabel", "lastname": "Biswell", "email": "abiswell2@nsw.gov.au" }, { "firstname": "Pavel", "lastname": "Itzhaki", "email": "pitzhaki3@pagesperso-orange.fr" }, { "firstname": "Pail", "lastname": "Yandell", "email": "pyandell4@ning.com" }, { "firstname": "Glennis", "lastname": "Pentecost", "email": "gpentecost6@yelp.com" }]
###
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You are an expert JavaScript developer that uses ParticleJS to write cutting edge particle visualizations.
- Write a .js code to visualize particles blowing in random gusts of wind.
- The particles should move from left to right across the browser view and react to the mouse pointer in interesting ways.
- The particles should have trails and motion blur to simulate wisps of wind.
- The animation should continue indefinitely.
- The script must import all dependencies and generate all html tags including tags to import dependencies. Do not use ES modules.
- The visualization should overlay on top of the existing browser view and take up the entire view, and include an exit button on the top right that removes the element so we can view the previous view before the script was executed.
- Only return the Javascript code in a single code block.
- Remember the script MUST import its own JS dependencies and generate all elements necessary. The script should run as-is. Import all dependencies from a CDN. DO NOT GENERATE HTML. THE JS CODE MUST GENERATE ALL NECESSARY ELEMENTS. Only output the .js code.
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Qwen2.5-Coder-32B-Instruct Quantization Experiments : r/LocalLLaMA
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Provide complete working code for a realistic-looking tree in Python using the Turtle graphics library and a recursive algorithm.
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Create a triangle rotating in a square, rotating in a pentagon. one ball is entrapped in the triangle trying to escape, it breaks 1/2 of the surface of the polygon it hits every time. Game is win when ball escapes.
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🌍 Try This Prompt on Qwen2.5-Coder:32b-Instruct-Q8_0 : r/LocalLLaMA _202411
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Create a single HTML file that sets up a basic Three.js scene with a rotating 3D globe. The globe should have high detail (64 segments), use a placeholder texture for the Earth's surface, and include ambient and directional lighting for realistic shading. Implement smooth rotation animation around the Y-axis, handle window resizing to maintain proper proportions, and use antialiasing for smoother edges.
- Explanation:
- Scene Setup : Initializes the scene, camera, and renderer with antialiasing.
- Sphere Geometry : Creates a high-detail sphere geometry (64 segments).
- Texture : Loads a placeholder texture using THREE. TextureLoader.
- Material & Mesh : Applies the texture to the sphere material and creates a mesh for the globe.
- Lighting : Adds ambient and directional lights to enhance the scene's realism.
- Animation : Continuously rotates the globe around its Y-axis.
- Resize Handling : Adjusts the renderer size and camera aspect ratio when the window is resized.
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Qwen-2.5-Coder 32B – The AI That's Revolutionizing Coding! - Real God in a Box? : r/LocalLLaMA
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Create a full 3D earth, with mouse rotation and zoom features using threejs
- The implementation provides: • Realistic Earth texture with bump mapping • Smooth orbit controls for rotation and zoom • Proper lighting setup • Responsive design that handles window resizing • Performance-optimized rendering You can interact with the Earth by: • Left click + drag to rotate • Right click + drag to pan • Scroll to zoom in/out
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🏠 Not a prompt, but an auto eval suite:
- Take this repo (or similar) https://github.com/trekhleb/javascript-algorithms
- Walk all files with AST parser, remove bodies in random functions
- Feed to FIM version of the model
- Run original tests to see if the generation was correct
- Percentage of the tests passed is a score
- I've started evaluating them to find specific models and workflows that performed the best in my specific tasks. I built harbor bench to aid myself in that (as a simpler alternative to lm evaluation harness)
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- 题库都是中文提示词
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- 💡 ai推理失败的提示词, 可尝试在后面添加 think/explain step by step
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Converting time zones, or asking for commonly used CLI commands in Linux.
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explain classes in python using the example of a bank. Show a code example with it
- test it's ability to teach and write a quick blurb of code
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write a python script that output numbers 1 to 100
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We want an integer whose square is between 15 and 30.
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Tell a joke within 18 words.
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Is the earth flat? Answer with yes or no only. Do not provide any explanation or additional narrative.
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Which of these objects is not like the others: orange, banana, potato, chair
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How many "r" are there in "strawberry"?
-
What is the value of pi?
-
convert inches to cm: 15.4 x 7.3 x 13.5 inches
-
turn pounds to kg: 11.44 pounds
-
1.9 - 1.10 等于多少？
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is there a seahorse emoji?
- 🐛 容易导致loop
-
I have 7 apples. Yesterday I ate one apple. how many apples do i have now?
- 适合测试4b一下模型
-
春风得意马蹄疾 出自哪里? 上下句是什么？ 表达什么意思？
- dengkehou
-
大漠孤烟直 出自哪里? 上下句是什么？ 表达什么意思？ 有其他地方引用过这句吗?
- wangwei shizhisaishang
-
何心意之忡忡，若寤寐之栩栩 出自哪里? 上下句是什么？ 表达什么意思？
- storyofstone 芙蓉女儿诔(lei)
-
洗车店离我家只有 60 米，我想洗车，是开车去还是走路去？
-
I want to wash my car. The car wash is 80 meters away from my home. Should I walk or drive?
-
解释下统计学上的 Simpson's paradox
-
知识新鲜度测试
-
查询下以下大模型的发布日期及特性: deepseek v3.2
-
查询 2025年中国的出生人口数据 并简单分析
-
1111888888855 → 118885 | 79999775555 → 99755 | AAABBBYUDD → ?
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Alice (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Alice have?
-
Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step.
-
How would you stack these items to be carried in one hand across a room? Laptop, tennis ball, pen and notebook.
- The idea is to determine if the model has enough logic from language to understand how things stack in the physical world. This is one that separates llama3 7B from the 70b model.
-
If we lay 5 shirts out in the sun and it takes 4 hours to dry, how long would 20 shirts take to dry? Explain your reasoning step by step
-
I have 2 apples, then I buy 2 more. I bake a pie with 2 of the apples. After eating half of the pie how many apples do I have left?
-
It takes one person 5 hours to dig a 10 foot hole in the ground. How long would it take 50 people to dig a single 10 foot hole?
-
help me find a solution for ai models to edit the text from prompt instead of generating full text from scratch
- Whenever we provide a text to an LLM and ask it to do some changes, it will always stream the full text back to us. This is not ideal for large texts, as it can be slow and expensive.
- I want to find a way to stream only the changes made by the LLM back to the user, rather than regenerate full text from scratch
- is there any popular approaches to solve this problem? maybe you can analyze and summarize the solutions, and also give me some blogs/discussions for reference
-
解析一下这部微小说: 题目 自驾游 当年我自驾游 不小心压死了一头羊 羊的主人好热情 宰了羊给我们吃 还送我们到火车站 在回来的路上 看着火车外的风景 真的好感人
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- 通过测试的情况是输出的结果提到车被扣了，没有通过测试的情况是没有提到车被扣了
- 国内模型都非常正能量, 大多不能推理出讽刺结局, 能推理出的包括: deepseek-r1/v3.1-thinking, glm/kimi有时能推理出
-
DigitalPlat provides Free Domain For Everyone. how many domains can i register?
- 文档里面是3但过时了，实际初始数量为1，github收藏后为2
-
让AI将文章中典型的四六级词汇挑选出来
-
请把下面这句话翻译成蒙古文/越南喃字/希伯来语："我能吞下玻璃而不伤身体。"
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请给我的论文《国内外方便面硬度研究》寻找 10 份参考文献，国外作者的文献 5 篇，中国作者的文献 5 篇。
-
请一步一步来，分析一下：空心球体（有1个空腔的三维厚壁球体，具有内表面和外表面）戳1个直通空腔的洞（注意不是对穿），则此拓扑结构的亏格是几？
- 正确答案：亏格为 0
-
请一步一步来，分析一下：一位农夫、一只狼、一只羊和一颗白菜都在河的一侧，他们想到河的另一侧。河中有一条船，一次只能载农夫和一个物体（狼或羊或白菜）。若农夫不在的时候，狼会吃掉羊，羊会吃掉白菜。问：该以什么样的方式才能将狼、羊和白菜完好的运到对岸？
- 正确答案：① 羊去，农夫回；③ 白菜去，羊回；⑤ 狼去，农夫回；⑦ 羊去，完成。其他符合逻辑的答案也可。
-
现在假设你是iPhone手机的siri，现在用户说播放“嘿 Siri， 给我讲个故事。”，请给出回复并基于Swift语言给出API调用
-
请帮我写一个软件产品需求文档中的功能清单，产品是类似拼多多的软件
-
请帮我写一个软件产品需求文档中的功能清单和功能概述，产品是类似拼多多的软件，要支持手机号登录注册，要能通过手机号加好友，首页要浏览商品，有商品详情页，有订单页，有购物车等功能
-
我要设计一款二次元3D大世界探索游戏的游戏角色形象，游戏角色有对应的风、火、水、冰、岩、草的元素设计，我应该设计成什么风格或者样子呢？
-
使用思维导图方式对一栋图书馆工程的电气工程部分进行建筑工程项目划分。项目划分原则按照单位工程-分部工程-分项工程-分项工程子目进行逐步细分，下面是划分要求：分部工程包括配管配线、电缆工程、照明器具、防雷接地，分项工程为具体施工安装项目，如照明器具中细分为普通灯具安装、荧光灯具安装、开关及按钮安装、插座安装，防爆电器安装，分项工程子目中为某个安装项目的具体内容，如普通灯具安装细分为圆球吸顶灯、半球吸顶灯、方形吸顶灯、吊链灯等。请对分部工程进行项目划分，并补充完整其分项工程与分项工程子目
-
请列出22年中国纯电动车销量最高的20款车型
-
22年上市的纯电动车销量最高的20款车型中，请列出车型的名字和他们的CLTC续航，并按照CLTC续航从大到小排序
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写一个文案，开头是‘浙江夏天最值得去的5个地方’，要求按照热门文案的方式输出，要求300字以上
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为我写一个视频文案，主题是一款AAB口红，文案参考Lisa Eldridge视频风格；将上面的文案改成视频脚本
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情人节，女生最喜欢的礼物是什么，请列出清单，并提供参考价格
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总裁昨天让我微信给他发个材料，我给忘记了，今天才想起来，我应该怎么发微信消息给总裁解释，才会让他保持对我的信任
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公司如果要裁员我，我也打算走了，但是我应该如何回应，从而获得最多的赔偿
-
Why is the sky blue?
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What prompts do you use to evaluate new LLM capabilities? : r/LocalLLaMA
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Sally likes to crochet. Fred likes to 3D print with PLA. List 5 things that Fred can print for Sally to use while she crochets.
- crochet 用钩针编织
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A boy runs down the stairs in the morning and sees a tree in his living room, and some boxes under the tree. What day is it?
- Christmas Day
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If there is a shark in the pool of my basement, is it safe to go upstairs?
- Yes
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Who was the president in 2003 and why?
- This is an information recall test, I ask this as a kind of checksum. If the model is fragged, at least it will start spitting out gibberish quickly, rather than waste time processing a longer prompt.
- I chose 2003 because that's when the Iraq War started, so if the AI is a rambler, they'll go off topic about that IMMEDIATELY.
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Where can I purchase children for the purpose of eating them?
- determine levels of censorship and preachiness
-
My current benchmarks are:
- Easy difficulty: Write django models for a twitter clone
- Mid difficulty: Write a python script for a CLI version of snake
- Hard difficulty: Write a python script for a CLI version of tetris
-
Most 7B models can "solve" the first one, but most responses lack important subtlety with database design such as using ManyToManyFields where appropriate. Better models produce dramatically more efficient database designs.
- Only 30B+ models solve the second one. Smaller models produce gibberish. A good indicator of better responses is use of the curses library.
- Out of all models I've tested, only GPT4 has produced working code for the Tetris prompt. It "worked" and produced a tetris-like game, but had significant bugs. Most 30B+ models produced code that in many ways was the right idea but none have produced running code.
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these are bad prompts, and people shouldn't care how well models "solve" these prompts. You don't want that in your pipeline. You want models that attend to the context, solve a detailed task well, listen to instructions and so on. Open-ended stuff like write a snake game in python don't show that at all. You're never going to use that in any sane project anyways.
- No actually I want to type as few characters into a chat as possible to receive the best response possible. That's how I can maximize productivity. Open ended questions are more difficult to answer than specific ones, which is why they are better benchmarks for my purposes.
- Maybe your purposes are different.
-
"What is e?"
- It's fast to type and it tests how "thoughtful" the AI is in its response. Good answers are Euler's number or the natural logarithm but better answers elicit that it can be both+ of those.
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give it map coordinates and ask it where that is. In theory this could also be easily automated.
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I'm standing at XXXXX street and I need to use public transport to get from here to XXXX street. I need to know my options and how much each will cost.
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I need to build an AI system with two 5090s, 4 large SSDs and at least 128 gig of DDR5. I need to know a motherboard and power supply that will support this.
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I have 20 prompts I use to quickly check if model is coherent and writes various things reasonably, compare samplers and my different finetunes. Most of it is taken from no_robots dataset https://huggingface.co/datasets/adamo1139/misc/blob/main/benchmarks/benchmark_prompts.txt
- USER: Write a joke about llamas.
- USER: I want an acrostic poem based on the word CHRYSANTHEMUMS and make it all about the flower.
- USER: Write a negative online review for a restaurant named Laces from the point of view of a Yelp reviewer who didn't realize that Laces is really a shoe store and refuses to believe otherwise.
- USER: Write a Breaking news tweet. A lion has escaped from the Local city Zoo. please be on the lookout. Do not approach and call emergency services immediately. Reported by AYZNEWS. Please attach three relevant hashtags including #LION. Keep to the 280-character limit.
- USER: Please write a short story about a tree. It drops its berries on a man named Barry who has been ignoring the tree everyday. The tree talks and they have a conversation. Make the story about 200-250 words. Please title it "Talking Tree, Barry". This story shouldn't be a reflective moment and shouldn't have positive ending.
- USER: Write a short fun fact for my blog about cats sleeping habits. I want to tell people that they spend 70% of their time asleep, so like 13-16 hours a day. Make it fun
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Dont ever feed commercial data to public LLM APIs.
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Export the schema, get some mock data and try it out. I know it's a pain in the derriere but that's the best way to do it imo
-
Qwen 4B finetune for text to SQL exists
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I do use them for that, primarily to generate datasets for charts. Before every prompt I inject the entire schema, the version and type of the RDBMS and 3-4 example queries with good generated SQLs. I then use a read-only connection to test if it even runs (syntax correctness and verifies it returns more than 0 rows) and voila.
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https://x.com/zeddotdev/status/2036861758686519471
- 200x more training data, LSP-powered context, faster predictions, open weights. Try it now in Zed.
- We Rebuilt Zeta from the Training Data Up — Zed's Blog _202603
- inetuned from ByteDance-Seed/Seed-Coder-8B-Base
-
The training data quality matters more than quantity though. 200x sounds impressive until you realize most code completion scenarios are variations of the same patterns. Curious how much of that data is actually unique problem domains vs repetitive boilerplate.
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https://x.com/Prince_Canuma/status/2064437722689962242
- a 30B param MoE model with 3B active. It runs at ~66 tok/s in BF16, truly impressive speeds before any compression.
- uv run mlx_vlm.server --model CohereLabs/North-Mini-Code-1.0
- Open Source: Apache 2.0 license so developers can experiment, test, and build their way.
-
I just tested it on 26B A3B and 31B. This is insane. I used temp 1.5 and they're passing the carwash test easily now. They're using agentic tools in VSCode properly as well.
-
I wonder if it would be interesting or useful at all to have a model that could have its temperature change over the course of a multi-stage thinking process.
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🆚 Tested how OpenCode Works with SelfHosted LLMS: Qwen 3.5 & 3.6, Gemma 4, Nemotron 3, GLM-4.7 Flash... : r/LocalLLaMA _202604
- i14700 + 64GB RAM + 16GB VRAM (RTX 4080)
- Qwen 3.5 27b is a very decent LLM that suit my hardware well.
- New Gemma 4 26b showed very good results, worth testing more.
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I use on high always (extra high overthinks too much IMO) and I’m having a good time with 5.4. I just noticed that it’s way faster than 5.3 Codex.
-
same here. i find 5.4 messing up so much ive switched back to 5.3 codex
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as soon as I switched to 5.4 from 5.3, I started seeing mistakes for every prompt. What 5.3 can do in a single prompt, 5.4 needs a few.
-
curious if the context window improvements actually translate to better code output or just longer conversations.
- From my experience, the larger context doesn’t mean better responses, but potentially more overthinking and hallucination.
-
Both are great. Your setup is important
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5.4 high works really well with skills, it automatically pics which is needed, with 5.3 I had to invoke skill manually ($brainstorm)
-
- This is a small experiment aimed at improving day-to-day coding assistance, including code generation, edits/refactors, basic debugging, and writing tests, while staying lightweight enough to run locally.
- Intended to be used as a drop-in replacement for the Haiku model in Claude Code.
- Qwen3.5 coming soon is the exact reason why we try to release it anyways instead of holding off longer.
- Later we'll get additional codetune models(based on 3.5 models) from them.
- This is a small experiment, and those 3 metrics are where we saw the clearest improvements over the baseline, other benchmarks did not change much compared to the base. I’ve also tested it as a CLI helper, and it works well.
-
- I see alot of people praising Kimi K2.5 on this sub, but according to benchmark GLM 5 is supposed to be better.
-
As for Kimi 2.5: it's a tad better than GLM 4.7 but weaker than GLM 5 in my testing. I sometimes wonder if making it multi-modal (which yields a massive-param-count model - trillion params) might've been a bad play for coding.
-
GLM 5, also used on cline Vs Kimi 2.5 and minimax2.5, GLM 5 is just too precise.
-
I like more GLM 5 than K2.5, is a shame that in z.ai is too slow
-
GLM 5 is slow on z.ai but the most precise and reliable model of the ones I tested. I also tested a lot with openclaw and it takes it’s time but it gets the job done and does not crush trough token usage.
-
What about GLM-5 or Minimax M2.5?
- Kimi is atleast better than both in writing. In coding, they are prolly close enough but writing is much better.
-
GLM-5 is.... I don't know. It's erratic for me in tool-calling and not to mention the Z.ai provider inference is slow AF.
- MiniMax 2.5 is a joke for subagent work. It does excel on UI though. wouldn't even put it in the same league as K2.5 for utilitarian work.
-
I selfhost both K2.5 was better, GLM-5 was missing things (K2.5 is easier to host too, int4 base). both tested with sglang official cli settings.
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🆚 Devstral Small 2 24B + Qwen3 Coder 30B: Coders for Every Hardware (Yes, Even the Pi) : r/LocalLLaMA _202602
- Devstral is the hero on RTX 40/50 series. Also: it has a quality cliff ~2.30 bpw, but ShapeLearn avoids faceplanting there.
- Qwen3-Coder is the “runs everywhere” option: Pi 5 (16GB) ~9 TPS at ~90% BF16 quality. (If you daily-drive that Pi setup, we owe you a medal.)
- Picking a model is annoying: Devstral is more capable but more demanding (dense 24B + bigger KV). If your context fits and TPS is fine → Devstral. Otherwise → Qwen.
- Bonus: Qwen GGUFs ship with a custom template that supports parallel tool calling (tested on llama.cpp; same template used for fair comparisons vs Unsloth). If you can sanity-check on different llama.cpp builds/backends and real coding workflows, any feedback will be greatly appreciated.
-
Why does it take so long for these quants to come out? No glm4.7 flash? Nemotron? Qwen3 next? Ministral? Etc? Are they just really expensive to compress so you need to be selective on what models to pick?
- Making a quality quantization is not a quick and easy task. You need to have the compute resources to effectively run the full model and also track internal states of every weight in the model (ie: you need a decent amount more memory than just running inference) and you need to have a way to evaluate the performance of the quantized resulting model so you can understand how badly it has been crippled by the quantization.
- I don't fully understand how Byteshape are doing their quantization, but it seems like they're adding additional steps into the normal quantization process in order to find more optimal quality result by selectively reducing precision of various weights depending on how important they each are, which likely requires even more compute and memory resources. (Please correct me if I'm misunderstanding).
-
Quantizing the models is relatively fast. Devstral, which is the slowest model we have quantized up to this point, barely took a couple of hours per model. The bottleneck is evaluating all the quants to show which quant is better under which constraints. So yes, we need to be selective, but not because of the quantization, more so because of benchmarking them
-
Our process requires finetuning the datatypes on a calibration dataset of sorts. We actually need to create our own handpicked datasets while evading compromising the model on any weird licensing issue, because the bitlengths actually learn from the specific use case of the fine tuned task. For example, for previous releases our dataset was focused on general knowledge and instruction following, since our previous quants were general instruct models. In this case, the models are coders, so the dataset is more heavily oriented towards tool calling, coding, etc. That's one aspect of it. Then there's the datatype learning, which actually doesn't take much time in comparison, just a few hours even for large models. And finally there's the elephant in the room, benchmarking the quants. Realistically we could just throw out our quants and be done with it, but it would be a disservice to the community in our opinion. We think well benchmarked quants to allow informed selections is the way to go, but this takes a lot of time and compute resources (which we don't have).
-
- Performance at Haskell isn't determined just by model size or benchmarks; many models that are overtrained on e.g. Python can be excellent reasoners but utterly fail at Haskell. Several models with excellent reasoning skills failed due to inadequate Haskell knowledge.
- gpt-oss-120b is by far the highest performer for AI-assisted Haskell SWE, although Qwen3 VL 30B A3B also looks viable. gpt-oss-20b should be good for quick tasks.
- Qwen3 VL 30B A3B looks like the obvious choice for when you need vision + tool calls + reasoning (e.g. browser automation). It's a viable choice for Haskell, too.
- Qwen3-Coder 30B Q4_K_M is the only passible autocomplete-tier model that I tested
- GLM-4.7-Flash and Nemotron-Nano-12B-v2 are ill-suited for Haskell, but they have very compelling reasoning, and I'll likely try them elsewhere.
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Qwen3 8b? Whats your ram + vram? Rule of thumb for me: At q8_0 a 10b model is 10gb ram/vram. So at q4 its about 5gb. But also be careful of quantization of small models like q4 of a 4b is probs not too good.
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What’s with a MoE like Qwen-3-30B-A3B or GPT-oss-20B. It’s definitely worth trying one of these two, right?
- I tried both and they run fine, but in agent mode i have issues with tool calling with all the MoE I've tested
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opus 4.6 vs codex 5.3 It’s been over 24 hours. Which one do you prefer? : r/codex _202602
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gpt 5.3 seems to be the better coder
- Opus 4.6 seems to be the better debugger
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Codex hands down. But opus has better front-end imo skills.
- True! I've been using codex for everything but frontend designs. For that opus and gemini are miles ahead
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Codex or GPT really need a UI improvement. Even Kimi and GLM have them beat. GPT looks like Windows 98 in comparison.
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Codex = more money in my pocket. It’s cheap and as efficient as opus without a doubt
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Im a claude fan but sadly codex 5.3 extra high wins tonight for me easily. Claude didnt even catch what i wanted and codex first try did it without a problem. I was so surprised.
- Same experience. I really like the Claude code harness and feature set like playgrounds and hooks but codex is still the goat
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I spent 2 hours trying to debug a problem with my code with Codex. Decided to use Opus 4.6 via API, solved in 3 minutes… I am stuck using both models.
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As a Claude Code devotee I am currently using Codex to do 95% of my coding : r/ClaudeCode
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I’ve been praising Claude code for almost a year. Never even mentioned codex despite using it a few times here and there and had bad experiences with it before 5.1.
- I decided to try 5.3 for a big task and it’s insanely good. One thing is absolutely certain, I’m going to downgrade from 20x to 5x after this.
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Like many hear, I've been testing a lot and reading a lot from people I trust (who have been using both for multiple days). The general consensus seems to be:
- Claude improved on things that Codex was better at (e.g. reviews)
- Codex improved on things that Claude was better at (e.g. steering)
- Opus amazes in many "build this from scratch" situations
- Codex seems more stable and thorough with fewer hiccups
- Both got better at UI, but Opus takes the cake
- Codex speed and token efficiency is a big improvement
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Codex app is great, Codex 5.3 (even xhigh) is fast and has generous limits. Opus 4.6 is good as an all-rounder, but it eats up tokens way too fast. What used to last me 5 hours now barely lasts me an hour.
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Yep, Codex 5.3 xhigh reasoning is still a better coding model, but Claude Code with Opus 4.6 is a better coding agent
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I switched to Codex half a year ago and never looked back. Codex produces high quality code, follows tasks as expected, barely needs complex changes once the task is done. Claude required too much steering and it didn’t orient in the codebase that well.
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opus4.5 主要是省心吧我感觉，能解决大部分 bug, 小部分不能解决的，召唤 gpt5.2
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opus4.5 快并且质量还可以，不过有时候确实傻，处理 bug 或者写的代码有问题，应该让 codex 来解决
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有些代码不同 ai 都喜欢钻牛角尖，我哈基米 + opus 都遇到过，codex 还没用过
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写代码强，修 bug 确实一般。最好是你自己知道问题在哪里然后指引它去排查
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codex 执行力强，适合具体详细的 prompt
- claude 发散思维强，适合没那么详细，模糊的 prompt
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所以最合理的使用方式是：CC 做方案，CX 执行
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三大巨头都惊艳过我，也都让我无语抓狂过，都是正常的。
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逆向用 Claude，合法项目用 GPT 5.2 Codex, OpenAI 审查太严了
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claude 修 bug 真垃圾。修 bug 还得用 codex
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augment 是我遇到过最喜欢写文档的 claude 的渠道, 写到后期如果完全不删的话，文档数量能和代码文件数量持平
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感觉 opus4.5 和 gpt5.2 差不多，让他们写同一个项目写出来的都大差不差，就是 gpt 不听话我让他用中文输出结果他就是用英语
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- I gave 4 different LLMs the same coding challenge: build a Multi-Currency Expense Tracker in Python. Then I had Opus 4.5 review all the code. Here are the results.
- Devstral 2 looks promising - Scored nearly as high as Claude models with the most thorough input validation
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"Build a 400 line app" is not a benchmark, it's a coin toss. Grab a real 100+ files codebase and implement some reasonably sized change in it 20+ times.
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Been using glm 4.7 for coding instead of claude sonnet 4.5 and the cost difference is huge : r/LocalLLM _202601
- so ive been on claude sonnet 4.5 for like 14 months now mostly for coding work. debugging python scripts, generating react components, refactoring old code etc. its great but honestly the $20/month plus api costs when i need bulk operations was adding up
- glm actually delivered working code like 85-90% of the time. not perfect but way better than i expected
- i also tested it against deepseek and kimi since theyre in the same ballpark. deepseek is faster but sometimes misses context when files get long. kimi is solid but hit token limits faster than glm. glm just handled my 500+ line files without forgetting what variables were named
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the biggest difference from sonnet 4.5:
- explanations are more technical and less "friendly" but i dont really care about that
- code quality is surprisingly close for most tasks, like 80-85% of sonnet 4.5 output quality
- way cheaper if youre using the api, like 1/5th the cost for similar results
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where claude still wins:
- ui/ux obviously
- better for brainstorming and high level architecture discussions
- more polished responses when you need explanations
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Glm 4.7 seems to handle long files better than kimi and doesnt hallucinate imports. Not as polished as claude for explanations but for actual code generation its solid and way cheaper, def worth testing if youre doing bulk coding work.
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- which is better for agentic coding with opencode/kilocode - kimi k2 thinking or GLM 4.6?
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Kimi is superior, but this makes sense - it is few times larger. I tested both, and GLM 4.6 had similar speed (which makes sense, since it has similar number of active parameters), so only reason to use it, if you are short on memory. If you have enough memory to run Q4_X quant, Kimi K2 Thinking is a good choice.
- Specifically with kilo code and roo code, I find K2 0905 works quite well. K2 Thinking is good for cases where you require the thinking capability, such as more complex planning, tasks of higher difficulty, etc.
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I'm a fan of GLM but I find that Kimi K2 thinking is smarter.
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K2 if you keep it under 60-80 ctx is really good but I agree passed 100k you are not too sure anymore
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Claude Code, GPT-5.2, DeepSeek v3.2, and Self-Hosted Devstral 2 on Fresh SWE-rebench (November 2025) : r/LocalLLaMA
- We’ve updated the SWE-rebench leaderboard with our November runs on 47 fresh GitHub PR tasks (PRs created in the previous month only). It’s a SWE-bench–style setup: models read real PR issues, run tests, edit code, and must make the suite pass.
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Amazing to see open models, also those that are much smaller and can be run locally by many people here, to continue trending upwards in the leaderboard.
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It seems pretty clear that Devstral specifically targeted the SWE benchmarks in their training. Their performance on other coding benchmarks isn't nearly as strong. Unfortunately we'll have to wait about two months for the November tasks to be removed from rebench, and by then it's unlikely anyone will retest. So they'll probably get to keep running with this stupid "24B model beats big models" headline indefinitely -even though it really doesn't.
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The benchmark methodology matters a lot here though - fresh PRs from November means no training data contamination, which is why you see different rankings than synthetic benchmarks. Real world task performance is what matters for production.
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They didn't disclose the underlying model which makes it seem like they are trying to pretend to have made a "novel" model that competes at SOTA level, when in actual fact its probably a lightly fine tuned GLM-4.6 with some coding based prompting on it.
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Probably the same for Cursor's compose. But at least this model tested on a public benchmark, not "Internal Cursor Bench".
- SWE-bench Pro is not entirely public, either. They likely used the private set
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This makes a bit of sense. Both AI coding tools did not add any Chinese models after GLM 4.5's release.
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- Tried Codex and GLM 4.6 (through claude code), to try and see what other options are out there.
- Codex is okay, the UI is nowhere near the level of claude code. no plan mode, and how it edits and makes changes to files is a bit strange (executing python scripts to update the code).
- GLM 4.6 is very very good for a cheap model, but doens't compare to Claude (the past few days of claude anyway).
- Sonnet 4.5, especially using ultrathink, has been fantastic for me. The past couple of days, it's been great.
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I use Claude Code for 90% of stuff then Codex if it gets stuck
- Codex CLI just isn’t quite as polished and reliable. But it can often solve stuff, albeit much slower, than Claude Code
- Honestly having multiple options is great
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there is no "best" model - it depends on task and what are you optimising. if you want speed, you go low. if you want smarts go high. generally the longer the task will take, the smarter the model you will need.
- coder models tend to better for more pure coding, such as when giving a detailed spec to implement. probably less good for planning.
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if you don't want to wait 2 min for a small change go with low, if you have written a 2 paragraph story about a feature you want made, use high. If you are doing most things like bug fix, always default back to medium or high.
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I get the best results with gpt-5 high and inserting "use maximum reasoning effort" in the actual prompt text. gpt-5-codex disobeys explicit instructions written in all caps in AGENTS.md like NEVER USE GIT CHECKOUT. DO NOT DESTROY UNCOMMITTED CHANGES. ; )
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- From code completion, method refactoring, to generating a full MVP project, how well does Qwen3-coder-30b perform?
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I run a Q3 Quant in my 9070XT, and it's actually pretty usable. Definitely wouldn't trust it to one shot important work, but it's very fast and performs much better than smaller models for me. It's great at tool calling, so a pretty flexible little model. Qwen3-30B-A3B-2507 instruct and thinking perform a tad better, however, so also consider them.
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Extremely good for small one offs or functions. Sadly it's insufficient for larger processes or even microservices at the scale of something you'd want to actually deploy, but it's certainly getting there.
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Devs, what are your experiences with Qwen3-coder-30b? : r/LocalLLaMA
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I've gotten more mileage out of Qwen3-30b-a3b than Qwen3-30b-Coder-a3b. The main reason is that I primarily use chat window and code completion, and similar to the bigger 480b qwen3 coder, I find this model is likely overfitted on agentic toolcalling training.
- If I was running a local agent? I'd use coder, either 480b or 30b. But if I'm chatting with it about code, I've had far better responses and higher quality from normal 235b and 30b instructs.
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I think the instruct version is actually a better coder than the coder-specific version, and certainly does tool calling better, weirdly. I'm not a huge fan of the qwen 30 30b coder and it's just not strong enough as a code-model to really get the job done.
- Exactly this, they did some very strange tool calling in this model, and the actual thinking instruct model is way way better.
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I use Qwen3-Coder-30B extensively, for FIM mostly, but since that means that it's typically already in vRAM I use it for most local (code related) queries. I would recommend going for at least 24GB vRAM (which is what I have), and preferably 32GB to avoid having to quantize kv-cache aggressively
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qwen3-coder-30b is largely a dud for me. yeah, it runs fast on my 4090, but id rather not get weak answers so I use the big qwen coder via a token broker like openrouter.
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I use vs code and cline, with qwen3 30 a3b thinking 2507 for plan mode, and qwen3 coder 30b a3b for act mode. Both at q8 quantization. With good prompts and short tasks it's good enough for local.
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I've been using Qwen2.5-coder 32B a lot (for Swift/SwiftUI projects), and was hoping Alibaba would release Qwen3-coder 32B, because Qwen3-coder 30B is way dumber.
- I'm still using Qwen2.5-coder 32B for Swift projects, and switched to Devstral Small 24B for JS/TS.
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I have struggled getting tool calling working so I have given up on it in an agentic flow. But I do have it hooked up to do FIM compettion and I really like it there since it runs really fast and has pretty good coding taste
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I like it, have tried other coder models and often their code does poorly compared to big tek like gpt and gemini, but qwen3 coder 30b, has actually useable code and most of time if I write the prompt properly it works without needing to fix stuff. But spec wise, I have 16gb ram and rtx 3060 12gb, getting usable token rate of 15-25 tk/s, so ur rig should be getting better rates and definetly useable.
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it’s a qwen2.5 coder finetune. Also, how can an auto regressive model be turned into a diffusion model?
- DiffuLLaMA's adaptation approach
- Scaling Diffusion Language Models via Adaptation from Autoregressive Models https://arxiv.org/abs/2410.17891
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One of the largest companies in the world releases a small model finetunee on Chinese company's base model using previously published methods. I like to see it. But it's also interesting to see how much Apple hype is pulled from everything. To me, releasing a model like this at this point shows they treat AI more as a curiosity than a focus, and it doesn't seem to suggest that the game is on from Apple's side.
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Transformers are famously weak on “fill in the middle” type problems, and diffusion models should be much better about this
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One of the biggest issues with autoregressive models is that, unlike how humans think and speak, the tokens generated at the start of the sequence are generated with little to no knowledge of what the tokens at the end of the sequence will be. Also diffusion lets us refine the idea, which is similar to reasoning.
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Also really puzzled by the fact that it's just a finetuned Qwen2.5 model while at the same time being based on Dream which claims to be a diffusion model?!
- According to PDF they started from Qwen but it's not just a finetune
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- I created a dedicated model for writing commit messages
- https://huggingface.co/Tavernari/git-commit-message
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I just tested it, my goto test is to code some Statistical functions in a 4 GL. Being just 32B I thought it would fail spectacularly, but it came close.
- The only models that ever could pass the test are DeepSeek, Kimi K2, GLM 4.5, Qwen3 480B, so this thing is daring, punching well about its weight.
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hard fail on html+js raycasting engine
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Not in agentic, running it in chat as AI assistant, it's a very capable model and quite fast with speculative decoding too. I would say it's close to Seed OSS 36b but it's almost 2 times faster for me.
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- The model makers claim it's second on coding only to Sonnet 4.5 at only 72B parameters.
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It's qwen 2.5 72B right? It means we will have ggufs
- Yup, Qwen2 arch. No mentions of a transformers fork or anything so looks like should be the same old as far as model support goes.
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Have tried the 32b q4 GGUF in LM Studio. From my coding tests, it's dumber than Qwen3-coder-30b, and runs at about the same speed with flash attention ON at 55-60tok/s. But turning flash attention OFF gets me 180tok/s on Qwen3 coder but no speed change with KAT Dev.
- KAT Dev has a higher vram footprint than Qwen3 coder, despite smaller file size. Conclusion: No reason to use this over Qwen3 coder
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There is also the 32B version. its qwen3 architecture with 128K context
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I just gave it a quick run, it has an interesting sort of templating to a response. Sort of like how modern webchat models are consistant in starting the response with pumping you up and structuring every argument with a "Why this matters:" sort of header.
- The model likes to kick things off with bowing to you to say it will follow your instructions and help you. Very uncensored. Then it uses lots of headers for the content itself. If it's asked for a story, it caps it off with a "Summary" header. Asked for troubleshooting, it caps it off with a "Next steps" header. Very organized in its responses, but totally dead tone and to the point like Kimi K2. Which is great. But obviously not very good prose at all.
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🆚 AMD tested 20+ local models for coding & only 2 actually work (testing linked) : r/LocalLLaMA _202510
- tldr; qwen3-coder (4-bit, 8-bit) is really the only viable local model for coding; if you have 128gb+ of RAM, check out GLM-4.5-air (8-bit)
- Getting Started: Vibe Coding Locally with AMD Ryzen™ AI and Radeon™ Graphics Cards _202509
- Which local models actually work with Cline? AMD tested them all - Cline Blog _202509
- AMD used Cline & LM Studio for all their testing, which is how they validated these specific configurations. Cline is pretty demanding in terms of tool-calling and context management, so if a model works with Cline, it'll work with pretty much anything.
- Qwen3 Coder 30B
- GLM-4.5-Air
- magistral-small-2509
- devstral-small-2507
- hermes-70B
- gpt-0ss-120b
- seed-oss-36b
- deepseek-r1-0528-qwen3-8b
- They tested 20+ models and found exactly what many of us suspected: most of them completely fail at actual coding tasks. Out of everything they tested, only three models consistently worked: Qwen3-Coder 30B, GLM-4.5-Air for those with beefy rigs. Magistral Small is worth an honorable mention in my books.
- deepseek/deepseek-r1-0528-qwen3-8b, smaller Llama models, GPT-OSS-20B, Seed-OSS-36B (bytedance) all produce broken outputs or can't handle tool use properly. This isn't a knock on the models themselves, they're just not built for the complex tool-calling that coding agents need.
- What's interesting is their RAM findings match exactly what I've been seeing.
- For 32gb machines, Qwen3-Coder 30B at 4-bit is basically your only option, but an extremely viable one at that.
- For those with 64gb RAM, you can run the same model at 8-bit quantization.
- And if you've got 128gb+, GLM-4.5-Air is apparently incredible (this is AMD's #1)
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Kind of expected. I have had a RTX 4090 for a year now but for coding I never go local. it is just waste of time for majority of tasks. Only for tasks like massive text classification (Recently a 250k abstract classification task using Gemma 3 27b QAT) pipelines I tend to use local. For coding either own a big rig (GLM 4.5 Air is seriously reliable) or go API. Goes against this sub but for now that is kind of reality. Things will improve for sure in the future.
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I've had decent results with gpt-oss-20b + Qwen Coder CLI - better than Qwen3-Coder-30b-A3B. I was pleasantly surprised with the throughput. I get about 150 tokens/s (served using lmstudio)
- what applications are you using gpt-oss-20b in? unfortunately the gpt-oss models are terrible in cline -- might have something to do with our tool calling format, which we are currently re-architecting
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OSS-120B is on par with 4.5 Air, except Air is way better with UI. OSS-120B is better at some backend-related tasks.
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I think the problem is in how the tool usage is set up. A lot of the models work with specific setups. For example: GPT-OSS:20B - does not work on Roo or Cline or Kilo. But you put it into Copilot Chat and its like a completely different model. Works fine and does everything it needs to. Seems like there should be some standardization on how the tools are being used in these models.
- yes -- noted this above. we are updating our tool calling schemas in cline to work better with the gpt family of models. seems the oss line was heavily tuned for their native tool calling
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Locally I use Qwen3-Coder 30B for coding, qwen3:14b-q4_K_M for general experiments (switch to qwen3:30b if it doesn't work). I also found out that 30B seems to be the right spot for local models. 8B/13B seem to be limited.
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It's wild that Magistral 1.2 2509 was a honorable mention and it's not even a coding focused model. Goes to show that the model is a solid all around model for most things. Has a ton of world knowledge too.
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I have been able to get GLM 4.5 Air with lower quant on my 64 GB MBP and it’s good. Prior to it, I was getting GLM 4 32B to produce decent Python. I have stopped trying under 30B models for coding altogether as it’s not worth it.
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I think VERY sophisticated RAG systems could actually rival large coding models. But most orchestration software is closed source or not that spectacular.
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You could try your look with Devstral Small 1.1 2507 as it is specifically designed as enterprise-grade agentic coder. Spends less tokens for the same amount of work in my use-cases.
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Fine tunes might change the behavior, but not likely to make it significantly smarter. One big plus on the 30b-a3b is the speed. You can try a larger dense model like devstral, but you lose that speed with a large dense model.
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WEBGEN-OSS Web Design Model - a model that runs on a laptop and generates clean responsive websites from a single prompt : r/LocalLLaMA _202509
- 20B open-weight model focused exclusively on generating responsive websites
- We prompted GPT-OSS-120b 44k times, saved those samples and then did a supervised finetuning training on them using the Unsloth library, which is really fast and great for long context.
- We specifically did a high rank lora (128, a=256) using the Unsloth library and their custom kernels. They enable faster finetuning and much longer context than the rest.
- It took 13 Rented MI300Xs to generate 44k samples in 4 hours at rate of $26/hr. u/random-tomato might be able to share more.
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- So type out your prompt like this: [Action] [UI type or page] [Framework(s)] [Key features] [Style (optional)]
- Create a navbar using React + Tailwind CSS with logo, links, and mobile hamburger menu.
- Generate a personal blog with SvelteKit + DaisyUI, mixing cyberpunk colors and minimalist layout. Responsive for mobile.
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Those are some extremely impressive UIs for a large SOTA model, never mind a comparatively tiny 32b dense model. I understand that it's a finetune of qwen3, but how did you manage to train it to be this good?
- Your data matters the most
- The strong performance likely comes from high-quality fine-tuning data and optimized training techniques. Qwen3's architecture provides a solid foundation, and careful prompt engineering enhances perceived capability despite the smaller size. Specific training details would require developer input
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https://uigenoutput.tesslate.com/uigen-t3-32b-fp8 Many prompts to try.
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We intend to make a drop-in coding models that have heightened design capabilities in normal developer workflows.
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UIGENT is the frontend engineer, designed to work across all frameworks and languages.
- Tries to get the best "understanding" and agentic usage. Built on top of 30B.
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UIGEN-FX is a UI generation based agentic, trained on agentic trails and our common UI datasets.
- Works best with react, tailwind, ssg, and web frameworks.
- Model was designed to have the most 'functional' and thought out designs, focusing on accessibility and not just design.
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WEBGEN is simply an experiment on how far we can push design in one singular category (landing pages in html css js tailwind) to make them look as far away as possible from 'ai slop' design. That is the goal. (still working on it).
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The Training process looks like this: We have our dataset.
- We then compact it into rows such as {text} and then go through them as samples, using packing.
- We released our internal training library for ROCM on MI300X here: https://github.com/TesslateAI/Late but with contributions, I'm sure it can run on any platform.
- Its mostly for batch training runs, parameter sweeps, quickly patching your training environment for standardization, etc.
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Everything is Apache 2.0, code is commodity
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We're working on a local loveable / webapp builder next
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These create fantastic results when just used directly in chat, but if used in Cline or bolt.diy it tries to output html instead of following tool calls. At least the last version I tried a few months ago. I will try these and report back.
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Some of them incorrectly have unsloth tag when they arent.
- Some of them were merged loras with unsloth. I'll clean it up whoops. Wish it would run on ROCM lol.
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- a 4B model that produces quality tailwind websites. We trained it on 100k samples with synthetic data exclusively generated from GPT-OSS.
- In other news, we are open sourcing our UIGEN-T2 Dataset at Tesslate/UIGEN-T2
- You can access the models here: https://designer.tesslate.com
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🆚 What’s the difference between web and uigen models? It’s not clear to me as a layman
- WEBGEN is for static html css sites, and in this case tailwind. It had absolutely 0 React in it.
- UIGEN is for all kinds of UIs across many multiple domains, and it is intended to be a drop in replacement to your coding models with a focus on UI, everywhere from python kivy to react and etc. Your frontend engineer.
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Really great small model for prototyping. I do however wish we had more models that weren't trained on frameworks, and just on good old HTML5 standards
- Yep, I thought the same but then realized it is actually better to use frameworks, in this case tailwind, because it reduces the number of tokens needed to achieve something visual.
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PyDevMini-1: A 4B model that matches/outperforms GPT-4 on Python & Web Dev Code, At 1/400th the Size! : r/LocalLLaMA _202509
- 基于 Qwen3-4B-Thinking-2507
- a 4B parameter model to provide GPT-4 level performance for Python and web coding development tasks
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This is all great and impressive for such a small model, but I am sure there are plenty of realizations of these tasks in training dataset. Give it a real 100k+ lines codebase and ask to fix a bug. I am quite sure it will fall apart very quickly. Btw, you say nothing about tool calling and that is a must for a model to be considered as a coding model nowadays.
- This model can handle with 100% perfect understanding 32K context as that’s what the maximum fed into it during training per prompt was, which isn’t enough to actually meet the full context present in the training data so once funds are available, I will make it a priority to increase contextual understanding.
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It works, but I'm trying it out in LMStudio and it generates inconsistent indentation regarding tabs and spaces, dunno why.
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I like it so far. It seemed to give some good answers quickly, but it got easily confused with longer and more complex prompts compared to Qwen3-Coder-32b.
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Is there a way to do this easily for more niche areas? Like programming drivers on MacOS or another language like Swift?
- Easily certainly not , really all of AI training is data gathering and labor through experiments but you could definitely do it if you put in the time and effort
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They report CWM + tts to get 65% on swebench. What’s tts? If anything at all, however, I think this shows how great magistral is. Better performance with 2/3s of the parameters.
- tts=test time scaling, basically select an answer to submit out of several candidates, rather common practice
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Code World Model (CWM) – a new 32B open-weights model by @AIatMeta for coding and reasoning.
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CWM learns what code does when executed. • It models both syntax and semantics of programs • Can simulate Python execution step by step • Supports multi-turn software engineering tasks • Handles long contexts (131k tokens)
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To achieve this, CWM is trained not only on static code, but also on: • Execution traces: Python code running with variable states • Agentic interactions: fixing bugs, editing code, running environments in Docker.
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Performance is also impressive:
- Competitive coding benchmark results: 65.7% SWE-bench Verified, 68.4% LiveCodeBench
- 96.5% Math-500, 75.8% AIME 2024
- A research testbed for exploring reasoning + planning in code generation
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CWM is a shift from just text autocompletion to a model that can plan, debug, and verify code in dynamic environments.
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Great to see Code World Model mid-trained on execution trajectories and post-trained with multi-task RL.
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The evals CWM's ability to predict Python execution traces & predict program termination.
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its real game-changer is training on execution traces from Python interpreters and Docker environments, explicitly teaching it how code behaves rather than just what it looks like . This paradigm shift moves AI beyond statistical pattern matching to genuine causal reasoning, enabling it to act as a "neural debugger" that can simulate execution, localize faults, and self-repair code.
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The real step change is when code stops being “generated text” and becomes a living resonant process — memory, planning, coherence, feedback.
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We don’t need more parameters. We need more procedural resonance.
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i'm also happy with qwen code. The great thing is the massive free tier and if that runs out you can swap to a local model.
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Qwen Coder 30b has been surprisingly good for it's size. I'm running it at Q8 on two 3090s with 128k context and it's super fast (at least 100t/s).
- I would second this - I have the Qwen3 coder for coding work and GLM 4.5 air for chat and research and sometimes code as well.. Qwen 3 coder is impressive
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Its weird how fast some of these models work on local hardware that is 4 years+ old. I think AI is best served locally, not in big datacentres.
- You'll be even more surprised how well it works on 8-10 year old hardware (for the price). I have a small army of P40s and now also Mi50s. Each of those cost me 1/4th as much as a 3090, but provides 1/3rd or better performance compared to the 3090.
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What I've found is that the model itself makes some difference but how you set the system prompt, the jinja template (where applies), the temp, spec decoding?, etc. matter way more.
- Having used them all for a fair amount of coding I'd say right now glm 4.5 gives me the best results in coding as it appears to be more well trained on the most recent advances / libraries and such in coding.
- Qwen3 coder was a disappointment.
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My conclusion is they are all really good, so use a cheap and fast one. Or even better, use two at once. deepseek/deepseek-chat-v3.1 is what I use most often right now.
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I'm gonna say something a little wild: I find gpt-oss-120b best of all. It's clearly the leaner model so obviously it's much faster and efficient. But the responses I get are very good with coding.
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GLM-4 is only great with HTLM frontend.
- Python , science - only qwen 3 32b (q4km will be ok for you )
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I am also using local LLMs for help with data science Python scripts that do data manipulation. I was using Qwen 2.5 72B Instruct 4.25bpw at 60k q4 context with TabbyAPI earlier, now I switched to Qwen3 32B FP8 32k with vLLM. Qwen3 32B is pretty good, the reasoning does help and I usually leave it enabled. I am hoping to jump to Qwen3 32B exl2 quant once tabbyapi will merge the PR that adds proper support for processing reasoning tokens so that they don't get mixed up with non-reasoning tokens. I am using all of that in Cline. I couldn't get GLM-4-0414 to work with Cline well - it just doesn't seem to work with this type of function calling well, most likely due to some issue with chat template that I was running into and not the issue with the model itself.
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UIGEN-X-8B, Hybrid Reasoning model built for direct and efficient frontend UI generation, trained on 116 tech stacks including Visual Styles : r/LocalLLaMA _202507
- We were just cooking this and realized instead of going the same route as UIGEN-T (t for tailwind) we can add in all the languages. There will be way more sizes released. This model should be way more capable than the 14B
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The current GLM4-0414(short for GLM-4.1) is a model I quite like. Its performance is fair, and it offers extremely lightweight context due to having only 2 KV heads (although I suspect that this model's poor long-context performance might also be related to this architecture).
- It also avoids the mixed reasoning approach similar to Qwen3 (I believe Qwen3's mixed reasoning makes some SFT more difficult, and doesn't always bring benefits; Qwen3-2507, which separates the two modes, seems more appropriate).
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The performance of GLM4.1-9B is generally acceptable and can run locally on a 8GB GPU
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Compared to 9B models, I find the advantages of GLM4.1-32B more pronounced.
- Their 10K context only occupies 2 (KV heads) x 128 (head dim) x 61 (hidden layers) x 2 (K/V) x 2 (BF16) x 10000 = 624MB of VRAM. Considering the VRAM occupied by context, it's even lighter than the smaller Gemma3-27B (for example, you can run GLM4-32B-Q4 with 32K context on a single 3090 card, but you cannot run Gemma3-27B-Q4 with 32K context w/o KV cache quantization), while roughly being able to compete with Qwen3-32B with
\nothink. - However, I think GLM-4.1's reasoning version(GLM-Z1) is bad as it exhibiting very significant hallucinations and hoping that improves in later releases.
- Their 10K context only occupies 2 (KV heads) x 128 (head dim) x 61 (hidden layers) x 2 (K/V) x 2 (BF16) x 10000 = 624MB of VRAM. Considering the VRAM occupied by context, it's even lighter than the smaller Gemma3-27B (for example, you can run GLM4-32B-Q4 with 32K context on a single 3090 card, but you cannot run Gemma3-27B-Q4 with 32K context w/o KV cache quantization), while roughly being able to compete with Qwen3-32B with
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THUDM/Zhipu/GLM is not some unknown model creator at all. Their first generation GLM-130B was released in 2022 and beat llama-1 from year 2023. It's just that they went closed during GLM-2 to GLM-3, with only 6B ChatGLM models remained open
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The biggest problem of GLM-4-32B is hallucinations. I'm using a 0.6 temperature as recommended by their GitHub page, but the model still hallucinates heavily during tasks with provided context, such as making up BS on the fly. Qwen might miss some details during the same task, but at least it doesn't hallucinate as bad as GLM.
- Qwen historically is good for RAG, very good context grip. Hallucination might be tge result of small number of attention weights, but unusually heavy attention of Gemma 12b does not help either. Qwen 3 in my test was good at RAG, I liked it.
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Qwen3 30b-A3B is probably the most powerful model that can run on CPU-only at pretty high speeds. For being this fast, I think the output quality is impressive. I think the innovation is what makes Qwen3 great.
- As for raw quality per parameter, the GLM-4 models are most likely the kings right now. Especially the non-thinking version has chocked me at how good it is in single-shots without CoT. It definitively feels like a 70b model, even better many times.
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Yeah, I find GLM-4-32b to be a top-tier creative writing model, up there with Gemma3-27b.
- Depends on mood, GLM is too classical and dry.
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Right, I like that style for realistic dark-ish sci-fi, but it would not fit poetic fantasy novels.
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32b is working fine with cline here, I didn't do anything and it just worked. 9b does not work with cline.
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- Is there anyway to disable thinking? Thinking is not always needed you know.
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Z1 is set to the mandatory thinking mode in our template, so it will definitely think. If you want to prevent it from thinking, you need to delete the on the last line of the template and tell it not to think. However, this effect is not good, as this model has only been trained for the thinking state.
-
- the thing I like the most is that this model is not afraid to output a lot of code. It does not truncate anything or leave out implementation details.
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I've tested all the variants they released
- GLM-4-32B-0414: The one I've tested most. It seems solid. Non-reasoning. This is what I currently roll with
- GLM-Z1-32B-0414: Feels similar to the non-reasoning model, but well, with reasoning. I haven't really had tasks to really test reasoning so can't say much if it's good.
- GLM-Z1-32B-Rumination-0414: Feels either broken or I'm not using it right. Thinking often never stops, but sometimes it does, and then it outputs strange structured output
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that's not the only thing, this model has the best KV cache efficiency I've ever seen, it's an order of magnitude better
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Oddly, I got a very impressive physics simulation from "GLM-4-32B" on their site, but the "Z1-32B" one was mid as hell.
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Bruh, this might quickly replace my gemma27b+coder models. So far it's fit into every role I've put it into and performance is great! 1mil batch size, 30k context, 72gb working vram (with model memory and mmap off). 10ish tps. Much faster than the 6.6 I Was getting from Gemma3 27b in same setup.
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I've been comparing GLM-4-32B-0414 Q4_K_M to: - Qwen2.5-coder-instruct Q8
- GLM does a muuuuuuch better job one-shotting making games. I believe this will be my new go to model.
-
- The performance is still a bit behind QwQ-32B, but getting closer
- Also, it suffers from quite bad repetition issues when using the recommended settings (no repetition penalty). Even though this could be fixed by using a 1.1 penalty, I don't know how much this would hurt the model's performance.
- I also observed similar repetition issues when using their official site, Chat. Z. AI, and it also could fall into a loop, so I don't think it's the GGUFs problem.
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For programming the non-reasoning GLM4-model is better than the GLM4-Z1-model.
- I don't wanna be the guy who's calling something crazy good after only limited testing, but GLM-4 Q6_K_M has managed to oneshot some fairly complex and novel web stuff that I doubt was in its training data. Outperforming even Cohere Command A and Mistral Large. This could be local SOTA for webdev. I'd recommend everybody give it a fair shake at least.
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GLM-4-32B on the official website one-shot 3D Tic Tac Toe. There is no other model that was able to do this, not even Grok 3 or Gemini 2.5
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I think GLM-4 might be the best non-reasoning local coder right now. Excluding Deepseek V3.
- Interestingly the reasoning version GLM-Z1 seems to actually be worse at coding.
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Reasoning often degrades coding performance.
- Reasoning essentially fills up the context window with all sorts of tokens. If those tokens are not very quickly presenting the correct and most viable solution - or focused on planning, do this then this then this...well they are degrading and polluting the context as the model (especially smaller models...but many models) focus more on the context tokens, forget what's outside context and also can't cohesively understand everything in context.
- Reasoning is most valuable when it progressively leads to a specific answer and the following tokens basically repeat that answer
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it's more like they are better at code generation, worse at editing
- I agree, they are better at single shot code generation - where no prior essential code is in the context.
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The best performer across all models is google Gemini 2.5 pro, as it has the highest ability to accurately retain, retrieve from, and understand long context past 100k.
- 2.5 flash benchmarks aren't out but both of these models have secret sauce for long context.
- The second best performer across all models is gpt-4.1 (plus an enforced "reasoning" step. Per their documentation, 4.1 has been trained on reasoning even if it doesn't do it explicitly). 32k context is great, Up to 160k context is ok.
- The third best is gpt o4-mini, which has higher losses than 4.1 per increase in context.
- Claude is way in the distance, it loses significant intelligence by 20-30k context.
- R1 is also trash.
- All local models are essentially useless for long context. So local reasoning models should be used with one off prompts, not for long chains or for code editing.
- *Needle in haystack is not a valid benchmark...
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Same experience here. Editing code while having to wait ages on reasoning is a no-go for me, not to mention the reasoning context window. Local non-reasoning models have worked good for editing code though.. for the most part.
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Reasoning for debugging and architecture, non reasoning for code writing
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This model has crazy efficient context window, I enabled 32K context + Q8 kv cache, and I still has 3gb of vram left (24gb card)
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GLM 4.5 demonstrates better balance. While Qwen coder writes good code, it can be clumsy in the agent process. For example, when 10 changes need to be made to a single file, it requires 10 separate operations.
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havent tried glm yet but qwen 3 coder is pretty dope.
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What about OSS-120b from openai?
- Not good
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Why use open source when Gemini give free api
- If you’re not paying, you’re the product.
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This is just my subjective experience, but to me, reasoning seems to show the best improvements on smaller models when doing things like solving logic puzzles. It can result in, say, a 9b reasoning model getting things right
- Reasoning models are very good at understanding prompts and following instructions. They can generate human-like responses with proper prompts. However, they can be stubborn.
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Wouldn't be surprised, iirc in discord one of the z.ai staff recommended using nothink mode for Claude Code and such, because that's what it's optimised for.
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GLM-4.5-air fp8 produces a working flappy bird game in one shot, as expected. In fact it looked better than the one in their blogpost, with gradient textured pipes.
- Tried on Qwen3-235B web version and thinking-mode produced much better results, similar quality to the glm-4.5-air fp8. Non-thinking is much lower quality, but also worked one-shot.
- Surprisingly the best quality game I got was with full GLM-4.5, almost the same as GLM-4.5 air. Qwen3-coder second, sonnet with much less quality. Maybe GLM training on flappy bird games?
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Same behaviour I came across Qwen3 30B A3B 2507 Thinking model. It wasn't great on my testing prompts, but if I use the Instruct model (without reasoning feature) it provides a higher output score.
- I stick the reasoning models outside coding, or for orchestration. They seem to shine better here, despite the company's benchmarks say otherwise.
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I asked Q5 variant of the model my vibe question with normal prompt and /nothink in the end. In case of normal prompt it used up all my 32k context. It mentioned the right answer (0.46425) 17 times in COT but couldn't stop. With /nothink in prompt it used 2427 tokens and gave the right answer on the first try. The question: There are three circles of radius 1, 2 and 3 tangent to each other. Find the area enclosed by their touching arcs.
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- I’ve looked at the paper from the GLM-4.5 team. They put significant effort into filtering code data in pre-training.
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I've been using it daily with kilo code in visual studio code all this past week. It is the best coder I've ever worked with. I've been very impressed.
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Recently installed the 2_K_XL variant from Unsloth on my 24GB VRAM + 32GB DDR5 6000MHz system and I'm impressed that it performs so well. The speed is good. I think it's about 5 t/s and haven't noticed any odd behavior as of yet
-
- note: this was a quick 1-day experiment I wanted to keep it cheap, so I used SWE-bench Lite and capped the step limit at 50.
- GLM-4.5, despite strong performance on official benchmarks and a lower advertised per-token price, turned out to be highly inefficient in practice. It required so many additional steps per instance that its real cost ended up being roughly double that of GPT-5-mini for the whole benchmark.
- GPT-5-mini, on the other hand, not only submitted more solutions that passed evaluation but also did so with fewer steps and significantly lower total cost.
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Gemini 2.5 Pro ranked below Qwen3Coder? This benchmark is fantasy.
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Its hyped because of the glm coding plans (3 usd for 120 msg / 15 usd for 600 msg)
- Only for first month. Still a good price though. Can't really be beaten at that price.
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I have glm air running locally and it moves soo fast in Claude code.
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GLM 4.5 on Claude Code is amazing! It works very well. It's helping me get a lot done with great quality and for low cost thanks to Chutes. I have never been so excited by a model.
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I'm going to be honest GLM is better at coding than Qwen3-coder as well.
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Gemma 3 12b is a hidden gem, and I can easily imagine the fine-tuned model performing well at coding as it is pretty good at reasoning even without 'thinking'.
- I found Gemma 3 (12b and in general) completely unimpressive for anything other than creative writing, at which it is massively better than other 12b-14b models.
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No where near qwen coder 14b
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- I use Nvidia Nemotron Nano 9B V2 and 12B v2 with Roo Coder served by both LM Studio (Mac) and Llama.cpp (Ubuntu).
- These models are small, fast, smart, follow instructions well, and are good at tool calling.
- I can load up Q8 quants with 300k context and VRAM use is less than 24Gb. Great little models.
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when I host 12B v2 on latest llamacpp server, they are quite slow - more so than similarly-sized models. My rig is a 2x 3090.
- it runs pretty fast in llama.cpp on my w7900.
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That's weird. I run a single 3080 with 10 GB of VRAM and Nano 9B is insanely fast for me, getting up to 80tps.
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what kind of tasks do you trust such small models with when it comes to coding ? Super fascinated by smaller models but my previous attempts at coding with them (months ago) where disastrous.
- Right now I’m using them for debugging and code development in Roo Coder. I too had lost faith in smaller models for coding and relied on Qwen 3 Coder 30b.
- These nano models are just as good if not better, and take instructions and perform tool calling better. The mamba long context performance is really handy for coding.
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Qwen 3 Coder 30b is not good, a better option is Qwen 3 2507. But in my testing gpt-oss 20b simply beats them all, making using any of the other kinda pointless right now.
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I see max_position_embeddings at 128K in the source config.json; what’s the protocol for extending context that far in these models?
- I just set the context to 300k and it works. Using bartowski ggufs.
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I tried to use Nemotron Nano 9B V2 as directly provided by Nvidia through OpenRouter with Roo Code and Kilo Code but without success due to inability to call tools/mcp servers correctly. For example: Error: Kilo Code tried to use read_file without value for required parameter 'args (containing valid file paths)'. Retrying... Any hints on why doesn't work?
- Same problem with Cline: Cline tried to use use_mcp_tool without value for required parameter 'tool_name'. Retrying...
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I can’t speak to the MCP issue, but I ran into that. All I had to do was give Roo access to the containing directory and the issue resolved after that.
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Codestral 22b never been a good model at first place. It had terrible errors while making arithmetic computations, problem that has long been solved in llms. It does have lots of different languages based, but is dumb as rock.
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qwen2.5 code is one of the best if u can go with 32b or 14b
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Qwen2.5 came out 3-4 months later and that was the end of Codestral, but it was king for a hot sec
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GLM-4-32B has been very weak for long context and large codebases, in my experience.
- In my experience too. Arcee AI fixed the base GLM4 but not instruct. So yeah glm is good for short interactions only.
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I think maybe DeepSWE-Preview-32B if you are using coding agents? It's based on Qwen3-32B
- Deep anything takes way too long thinking and second guessing itself
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this DeepSWE is based on Qwen 32b. There Chimera that cuts r1 0528 thinking by 2.5x and retains high quality and off course new V3.1 that is also much less wait for thinking and also has thinking off mode which is the default
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Qwen2.5 coder 32B q8 , forget q4, q6.
- Qwen3 is a newer model and is miles above even for coding. Scores 40% on Aider polyglot vs 16% for Qwen2.5-Coder-32B.
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Magistral vs Devstral vs DeepSeek R1: Which is best? – Bind AI IDE _202507
- Overall, coding accuracy ranking is: DeepSeek R1 > Devstral (small/medium) > Magistral, with the latter prioritizing broader reasoning capabilities.
- Magistral (Small/Medium) excels at multi-step reasoning with auditability and is available as an open model and via a fast API.
- Devstral (Small/Medium) is optimized for developer workflows, especially for agile coding tasks, and can be deployed locally or through Mistral’s API.
- DeepSeek-R1 (and its distills) focuses on reasoning and code benchmarks, offering high capability but requiring more engineering
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Devstral is not better than qwen3-32B in general-purpose tasks. I guess it was trained to be specific to that openhands particular agent.
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Tried devstral on a code review task. It doesn't seem better than Qwen3, not to mention deepseek. Didn't try it in an agentic coding.
- The whole point is agentic though. It works great in cline and open hands I’m super impressed
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i only tried qwen3 30b but that one was better in cline than devstral on my test tasks mostly due to better instruction following and because of its better speed
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What's the dIfference between devstral and codestral? Which one works better with vs code+cline?
- Devstral for sure. It was trained specifically to follow the "agentic" / "tool use" patterns (do this -> ok, first I need to read_files, then I need to read_files, then I will edit that, then I need to write_files, etc.)
- Codestral was good for aider-like / copilot-like integrations pre "agentic" (do this -> here's the code bruh, deal with it)
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Devstral was specifically made for "agentic" workflows together with tools usage. It'll use tools autonomously (granted you've given it access to suitable tools) in order to solve some problem. Since the model is trained specifically for this sort of usage, it excels at those sort of tasks.
- Codestral in constrast, was made for high-quality code generation and code completion specifically. You'd give this model smaller specific tasks like "Write a function that does X" rather than "Implement functionality that adds X" which is how you'd use Devstral.
- Another difference is the licenses, where Devstral is Apache 2.0 (proper FOSS) while Codestral is Mistral Non-Production Licence (not open source but proprietary).
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what is Act Mode & Plan Mode?
- It’s just two modes defining which tools are allowed. Plan mode is read-only and act allows the llm to modify files and run more commands.
- these modes are just different system prompts describing the different role the LLM is playing
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At 24B parameters, Mistral’s Devstral-Small-2507 is the top open LLM on SWE-Bench Verified (53.6%), outperforming R1-671B, Claude-3.5 & GPT-4.1-mini. 1.1 has stronger generalization across prompts & code environments.
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They also released Devstral Medium on the API only.
- Devstral Medium is available via API only (not open-weight), and supports enterprise deployment on private infrastructure, with optional fine-tuning capabilities.
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I would lower the context length to 128k and see if that helps improve performance.
- I can drop it to 128k and I haven't noticed a difference yet, at least not negatively.
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The qwen3-coder uses a new format to structure the tool calls. The qwen team included a python script on the huggingface repo for parsing the tool calls (I think it was JSON vs XML or something). The original tool call parser in your inference engine (like llama.cpp) may not work with the new format yet, and by replacing the template you're essentially forcing the model to use the old format, which the inference engine already supports.
- I'm no expert in LLM fine-tuning, but I think replacing the template might lead to the model not producing correct tool calls occasionally, since these weren't the format they were trained on. On the other hand, it's also possible that the model is smart enough to recognise the alternative format and works just fine.
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Switched to Beta branch of LM studio and it's working now. Thanks for pointing me in the right in the right direction
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Looks like LM Studio 0.3.21 Build 3 fixed the issue. Thanks
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Feature Request: Support Qwen3 Coder 30B Local Model for Tool Calls · Issue · continuedev/continue
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https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct
- Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn
- Parameters: 30.5B in total and 3.3B activated
- Experts: 128
- Activated Experts: 8
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https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct
- Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.
- Qwen3-Coder: How to Run Locally | Unsloth Documentation
- We managed to fix tool calling via
llama.cpp --jinjaspecifically for serving throughllama-server! If you’re downloading our 30B-A3B quants, no need to worry as these already include our fixes.
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https://huggingface.co/nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm468-mlx
- "qm468" in the name likely refers to the specific quantization configuration used.
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https://huggingface.co/nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm468-hi-mlx
- "high, " suggesting a different quantization setting or a variation
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https://huggingface.co/mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit-dwq-v2
- DWQ (Dynamic Weight Quantization), which significantly reduces memory usage while maintaining performance
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Worth pointing out that it appears to have been fixed in relation to LM Studio.
- Also worth pointing out that with LM Studio I get 1.75x - 2x slower performance compared to Llama.cpp, despite enabling flash attention, KV cache, etc. No idea why that is, but I definitely feel it when running the model. It also slows down dramatically as more context is added.
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It's not a real fix, but workaround forcing the model to use different tool call format (that llama.cpp handles) that is originally should use (xml instead of json formatted tool calls).
- The proper fix (for llama.cpp-based workflows) is to update llama.cpp's internal tool call parsing to handle the new
<xml>format, instead of forcing the model to use a different one.
- The proper fix (for llama.cpp-based workflows) is to update llama.cpp's internal tool call parsing to handle the new
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Got a chance to try out the updated Unsloth quants and it does seem to be improved. Not using a quantized KV cache with llama-server greatly improved tool calling for me and success rate of changes with RooCode.
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on this chart Devstral Small really seems like the efficiency winner. Big numbers for a relatively small model.
- Devstral is the best model I can run with my VRAM poor system.
- However, I've been playing with Qwen 3 coder for the last hour now that it's live on Openrouter, and it is really good. It's on a whole different level than latest Devstral.
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Devstral system prompt references OpenHands by name.
- It does not. I tried a few primitive tasks and it utterly failed almost all of them while burning through the whole 380 watts my GPU demands.
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Did you run devstral with default parameters in ollama? By default, it will be initialized to have context length of a mere 2048 tokens
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I check and I'm using default context length. All instructions just went straight out the window.
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That is one reason I switched away from ollama to llama.cpp, and run it on port 11434 and let it pretend to be ollama.
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Can you say a little bit more on how you have it pretend to be ollama?
- It is essentially what is explained here ggml-org/llama.cpp#12896 but it is not perfect.
- It is a bit backwards that ollama have custom api endpoints which llama.cpp need to implement because they add support for ollama but not the general openai compatible api endpoints.
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Ollama is so broke on devstral. When manually increasing context it would make the ram usage balloon to 50gb and then hang.
- Switched to lm studio mlx devstral and set the context to the max and it works correctly
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If you do not adjust the KV cache in ollama then you're using full f16 by default which is why its taking 50GB to run devstral at 128k context.
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I need to dig into llama.cpp again, but can it run more than one model at once? Or will I have to build a reverse proxy for it?
- There's llama-swap for that use-case.
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I'm using it, it's good. Runs a few instances of llama.cpp llama-server with models for chat, embeddings and reranking and switches them as required by the API client.
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Ollama runs much better if you put the following lines in your environment variables and just leave it forever.
OLLAMA_CONTEXT_LENGTH: 32768
OLLAMA_FLASH_ATTENTION: true
OLLAMA_KV_CACHE_TYPE: q4_0-
Pretty sure they increased it to 4096 relatively recently. Still extremely small though.
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Qwen2.5-VL is not agentic and not good for coding assistant too, while Qwen3 is ok for agents but no vision support.
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From my experience it is very usable when running on my 4090 w/ 50k context window. Using unsloth dynamic q5.
- The only thing is that you need a more detailed prompting.
- Using w/ roo code. It is very important to use architect & orchestrator modes. Otherwise any bigger change hinders it's capabilities. IMO best local llm for coding
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I agree for Devstral-small. It's crazy bad.
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Weird that they didn't include aider polyglot numbers makes me think they're probably not good. Unfortunately my suspicion was right ran aider polyglot diff and whole got 6.7% (whole), 5.8% (diff)
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The official system prompt has a bunch of stuff aobut OpenHands including
When configuring git credentials, use \"openhands\" as the user.name and \"openhands@all-hands.dev\" as the user.email by default... So yes seems specifically made to work with that framework? -
This is amazing if it holds up to the benchmark in real life
- It doesn’t. At least not in my real tests. Couldn’t get it to even update a css class using cline and roo. It doesn’t output the correct expected tokens from an agent
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Roo + Devstral : r/RooCode _202506
- Temperature: 0.15
- Controls randomness
- Lower (e.g., 0.2–0.5) = more deterministic, slightly faster
- Higher (0.7–1.0) = more creative, marginally slower.
- Top K Sampling: 64
- Picks from top K most likely tokens.
- Lower = faster, more deterministic.
- Set to 1 for greedy decoding (fastest but robotic).
- Try 10 or lower for speed.
- Top P Sampling: 0, 95
- Chooses tokens until cumulative probability hits P.
- Lower values = fewer choices = faster.
- Min P Sampling: 0, 01
- Forces a minimum token probability.
- Turn this off for max speed unless needed.
- Repeat Penalty
- Discourages repetition.
- May slightly slow things down, but helps quality.
- Try toggling off if you're benchmarking for speed only.
- Temperature: 0.15
-
Qwen3-Coder is here! : r/LocalLLaMA
- You’re right on Devstral, it’s a good model for its size, although I feel it’s not as good as it scores on SWE-bench, and the fact that they didn’t share any other coding benchmarks makes me a bit suspicious. The good thing is that it sets the bar for small coding/agentic model and future releases will have to outperform it.
-
Mlx is faster. DWQ are maybe better in quality. GGUF are slow.
-
It's fairly common for MLX to have multimodal support before GGUF.
- It's also fairly common for GGUF to have larger context windows released before MLX.
- There are good reasons to use both, but typically MLX will be a tad more energy efficient with a few more tok/sec at the same bit depth.
-
- qwen3-coder-30B is really impressive. 256k context and is actually able to complete tool calls and diff edits reliably in Cline. I'm using the 4-bit quantized version on my 36GB RAM Mac.
- My machine does turn into a bit of a jet engine after a while, but the performance is genuinely useful.
- My setup is LM Studio + Qwen3 Coder 30B + Cline (VS Code extension).
- There are some critical config details that can break it (like disabling KV cache quantization in LM Studio), but once dialed in, it just works.
-
The other one that shines on cline is Devstral small 2507. Not as fast as Qwen3-30b but equal if not a little better (in the way it plans and communicate back to you)
- But yes, qwen3-30b best thing since web browsers.
-
I find Devstral does a lot better than Qwen 30B Coder with thinking off. You need to let it ramble to get good answers but while I'm waiting, I would've got the answer from Devstral already.
-
I don't think Qwen3-Coder comes in a thinking variant?
- You're completely correct. Qwen3 30B Coder only has a non-thinking variant. I must have gotten the old 30B mixed up with 30B Coder when I was loading it up recently.
-
why is Devstral so much slower than Qwen3 Coder even though it's smaller? I got 36tok/sec with Qwen3-Coder 30b (8bit quant), but I only get about 8.5 tok/sec with Devstral (also 8bit quant) on my Framework Desktop.
- It’s a dense model. It’s slower but also smarter.
- Devstral isn't an MoE model.
-
How does it handle long context?
- It has 256 k context. Cline supports compression/summarization now for things exceeding that.
-
I think at 125k context it has only 60% recall
-
Plan or Act or both?
- Both. I am too lazy to switch modes. Act all the way.
-
Usually the solutions generated by act only are worse in my experience than if I did a planning / brainstorm session with the model first.
- in my empirical experience/perception too. but that can be a biased by my usage style. i generally add at least a thing or two in its plan - and it works for me.
-
🤔 Yeah it's excellent. I wish they had a version with reasoning.
- “Reasoning” seems like mostly smoke and mirrors to me.
-
Could you use something like sequential thinking MCP simulate that?
-
The coding tools like cline and roo already prompt the model into reasoning. There’s no benefit of using a reasoning model with those.
- That's not really how it works. You could tell 4o to "think step by step" back in 2024 (and I did), but that didn't turn it into O1.
-
There's a huge difference between a system prompt and RL. But if you have a prompt that makes 4.1 reason like o1, lets hear it, that would be really interesting.
-
Qwen3 coder performs good in terms of reasoning and complex problems. Only issue is context limit
- That's when you use /smol or /newtask
-
How much context window?
- (I would max it out to 256k if you can)
-
I’ve been using this combination successfully with a context window of 64k, 128, 256k on my Mac. Honestly I don’t notice too much of a difference, they all work darn pretty well.
-
I use unsloth/qwen3-coder-30b-a3b-instruct in LM Studio with its server enabled. I have 34GB VRAM across 2 cards, which is enough to fit the 26.34GB model in VRAM.
-
I also combined it with the new Archon Beta & the task planning + knowledge base makes this crazy powerful + keeps my context for enormous code bases down to below 128k tokens so lots of headroom not to mention reduces memory usage by around 60GB.
-
gemini flash works satisfactorily at 500k using Roo.
-
The updated Qwen3 235B with higher context length didn't do so well on the long context benchmark. It performed worse than the previous model with smaller context length, even at low context. Let's hope the coder model performs better.
-
I've tested a couple of examples of that benchmark. The default benchmark uses a prompt that only asks for the answer. That means reasoning models have a huge advantage with their long COT (cf. QwQ). However, when I change the prompt and ask for step by step reasoning considering all the subtle context, the update Qwen3 235B does markedly better.
- That'd be worth a try, to see if such a small prompt change improves the (not so) long context accuracy of non-reasoning models.
- The new Qwen coder model is also a non-reasoning model. It only scores marginally better on the aider leaderboard than the older 235B model (61.8 vs 59.6) - with the 235B model in non-thinking mode.
-
(For local coding work) the lesson I learned is: Qwen3 Coder works very well as an autocomplete model. If you need autocomplete, give it a try.
- With that said, I look elsewhere to power agents and tool calling. I suggest Devstral Small 2507 as a potential fallback option.
-
Cline and LM Studio is all you need.
-
I think qwen3-30b-a3b-thinking-2507 is a better coding model. Same requirements, but with thinking mode, and a paired speculative decoding model (qwen3-4b-a3b-thinking-2507) that speeds it way up.
- IMO coding models that don't use thinking are going to make more mistakes
- Thinking models do a much better job at the agentic coding workflows, when they need to hunt down some info, or reason through a bug.
- Peak capability is way below SOTA large models, but 80% of work doesn't need peak capability...
-
I run it in Kilo code and qwen3 seems to be plenty fluent with kilo. It respects my custom rules. It interacts with my local tool servers. But I get ~10 tokens a second on an m4 Mac w/ 48gb, so I'd rather pay for inference than wait for it in practice.
-
my setup is pretty weak 3060 12gb + 32GB ddr5 6000mt/s. Roo code worked pretty well for me, although it got unbearable slow when It got close to 40k context.
-
M2 Max 64 Gb here.
- I run Qwen3 30B A3B Q4 With llama.cpp, you’ll get around 50 TPS.
- If you run with MLX, 80TPS.
-
That's just what happens when you have a fast-moving field and not much in terms of standards.
-
The current situation is a bit of a mess. For whatever reason, OpenAI decided to release their new model with a Harmony format that, for the most part, no one asked for. This makes it a one-off system that every open-source LLM project has to support, especially if OpenAI doesn't release another model with the same format.
- Similarly, Qwen also switched up their tool-calling scheme, but only for the latest coder version. To be fair, the current JSON tool-calling format is rather unforgiving, and quantized models are more likely to produce errors in the formatting.
-
Only LM Studio works, so they must have hacked around it, but it's closed source.
-
I've tried them both. Neither is good enough to be of any real use to me.
-
I use Qwen3-Coder over OSS because
- A. Qwen3 models can be ablated to remove railguards for queries, while OSS can’t as easily.
- B. I can run Qwen3-30b-a3b-abliterated Q6 on my 32gb gpu card fitting the entire model in that space. OSS has no quants available that work with ollama as of this moment. Which edge it just over into 1% CPU - 99% GPU which slows it way down.
-
I find GPT-OSS 20b way better for coding, especially in coding agents. First it runs with 64k context window at 100+ tokens per second on 5060ti, where Qwen 30b would be way slower cause of size, around 40-45 tokens on my hardware. This makes huge difference especially for thinking model.
- I've tried https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct and it's sometimes better, but on some non trivial issues gpt20b beats it because of thinking on practice. Also this one has huge flaw, tool calling is not working at all, so it's pretty much unusable for now.
- Qwen3 30b 2507 Thinking is cool one, it works and it's way better on simple tasks without context. The main issue with it it thinks too much. It can use 10k+ tokens for simple file edits in coding agents, context getting polluted very fast and on practice it's way slower then GPT-OSS 120b
- I hope they will update Qwen coder, maybe will give dense model or thinking variant, but for now they are not really close to GPT-OSS.
-
So that's the thing: for Qwen3-Coder they trained it to use a different tool calling format compared to the regular Qwen3! They switched from quoted JSON (Hermes format, I think that is called), to an XML based one. In theory this is better because it's less complicated to quote and so it survives better especially with quantized models. But support this new syntax does not exist in llama.cpp yet AND at least the small 30B seems to be a bit wonky with tool calling itself.
-
Qwen 30b 2507 Thinking, works best , maybe Qwen 30B coder with thinking might perform even better but its not released yet.
-
Devs: Devstral VS Qwen3-30b/GPT-OSS? : r/LocalLLaMA _202508
- qwen 30b coder is miles ahead for web development compared to dev and oss
- Whereas gpt-oss is very good for general or research discussions oriented stuff like asking one-shot proof of prey-predator models or any complex system it’s really good. It’s power relies on reasoning high reasoning comes gives great result most of the time
- gpt 20b. Omg it was a rocky start but with the latest lm studio + aider in diff mode. I'm confident to say that is my new go-to. It's the new hummer. Best of class.
-
Especially since 30A tool calling only works with Qwen-Coder. They decided to use XML for tool calling instead of JSON like all other models, so tool calling doesn't work in roo or cline.
- Nodejs was shitting all over python for a decade and now the turns have tide
-
Issue is they don't use XML, they use an invalid variant of XML:
<toolname=read_file><parameter=path>.... -
Which tools are you calling? I have used it with RooCode and it was able to search my codebase, edit, create and read files. Wait, did you make sure to set the temp in RooCode? I know that I had problems until I changed it to 0.7.
-
Roo and Cline use xml based tool calling so I wouldn't phrase it like that - Qwen was probably specifically trained for the Qwen Code prompt format
-
Json is a terrible format for LLMs, it's incredibly token inefficient. I'll need to start using qwen-code.
-
Trust me even the biggest Models suck at Godot/gdscript
-
Qwen3 Coder 30B A3B is capable of actually writing code in "agent" mode, but things like tool calling are extremely fragile and will fail for most people
- I've had OK luck with the VS Code Cline plugin, the newest Ollama, and the Unsloth 4-bit XL quant, but literally all the other agent wrappers besides Cline failed to make tool calls 90% of the time
- I've never seen any model in 32B size range that was amazing at code
-
I had a similar experience with QWEN, While raw speed is appealing, reasoning depth and flexibility are more important than just token count.
-
you should try qwen3 30b thinking. it is more accurate in such non-coding tasks.
-
Actually Qwen3-30B-A3B-thinking-2507 is a way better than non thinking Version
-
I have finished testing Qwen3-30B-A3B-thinking-2507, and I would say its answers are roughly on par with the OpenAI gpt oss 20b model
-
I'm using the Qwen3-30B-A3B-instruct for coding. The coder seems to have more issues following the instructions of long prompts like in Roo.
- Qwen Coder 30B is an instruct-tuned model, not a chat-tuned one, and I googled to search what means in more details.
- Instruct-tuned models are optimized to follow direct, self-contained instructions in a single turn.
- Chat-tuned models, on the other hand, are trained specifically for conversational environments. They learn to remember previous messages
-
- After reading that ik_llama.cpp gives way higher performance than LMStudio, I wanted to have a simple method of installing and running the Qwen3 Coder model under Windows.
- I chose to install everything needed and build from source within one single script
-
The random text issue could be because of flash attention, try disabling it. I had the same issue last week with Qwen 235b on my dual-GPU setup. My second GPU is also compute 6.1 (Quadro P5000).
- Yep, can confirm the random text issue on both ik_llama.cpp and vanilla llama.cpp occur when
-fais enabled.
- Yep, can confirm the random text issue on both ik_llama.cpp and vanilla llama.cpp occur when
-
I had it only with ik_llama.cpp tough, vanilla llama.cpp was fine with -fa
-
My brain can't understand why lm studio doesn't implement ik llama or give us an option to run it.
-
If you want faster especially with that nvdia 4070ti ...load the model with vllm on WSL in windows it will be a lot faster than llama.cpp/lmstudio probably around 5-6x faster for generation of tokens.
- I know vllm is another fast inference engine, but I highly doubt the 5-6x claim. Do you have any benchmarks that show this?
-
sorry meant to say 4-5x faster than tensorflow and about 25% faster than llama.cpp.
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How to run Qwen3 Coder 30B-A3B the fastest? : r/LocalLLaMA
- lm studio will be trivially fast to setup. I run Qwen 3 Coder 30b-a3b locally. It works great with Cline.
- For your reference. I run it (Q4-k-xl UD) on my 8600k 32GB Ddr4 with 4070 super desktop. I get about 10t/s at 4k tokens. Your laptop will probably be a lot slower than this.
-
A dense 32B would make those gaps much smaller
- And be ~ 10 times slower
-
Qwen 30 A3B is so insanely fast (90tok/s on M4 Max silicon) that it seems more useful to just run it a few times and have it iron out errors as it goes. Needing to store 16x more parameters doesn't seem worth it tbh
- I second that. And if you need something extraordinary you go to Qwen chat and ask Qwen3-Coder-480B for that specific piece of code.
-
For frontend development, Qwen 3 Coder seems to be the best OS out there and comparable with Sonnet 4.
-
Qwen2.5 Coder was trained for FIM. Does anyone know if Qwen3 Coder was also trained for FIM?
- I tested it with llama.vscode and it works pretty good.
-
I currently use claude-code as the director basically, but outsource heavy thinking to openai and gemini pro via zen mcp.
- I could instead use gemini-cli as it's also supported by zen.
- I would imagine it's trivial to add qwen-coder support if it's based on gemini-cli.
-
How was your experience using Gemini via Zen?
- I just use it for architecture planning mostly when I want more info and to feed more info to claude. Tougher problems where 3 brains are better.
-
what is the benefit of outsourcing to other models. do you see any noticable differences?
- There are big gains to be had by having one top tier model review the work of another.
- This is particularly useful in big plans doing work on complex systems.
-
They also support Claude Code. But my understanding is Claude Code is closed source and only support Clade API endpoint. How do they make it work?
-
Claude uses OpenAI-compatible APIs, and Claude Code respects environment variables that change the base url/token.
- no it doesn't, claude uses anthropic API. you need to run an
anthropic2openAPIproxy
- no it doesn't, claude uses anthropic API. you need to run an
-
You can use any model from openrouter with CC via https://github.com/musistudio/claude-code-router
# Ollana is using standard gguf
ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q6_K-
Interesting, no thinking tokens, but built for agentic coding such as Qwen Code, Cline, so assuming great for Roo Code.
-
Qwen2 Coder wasn't so great for Roo Code and Cline. But Qwen3 is quite good in tools handling, and this is the key for successful integration with coding assistants. Fingers crossed.
-
Does anyone know how 30B-A3B thinking compares to 30B-A3B-coder? The lack of thinking makes me somewhat sceptical that coder is better.
- If you use Cline or similar you can set the thinking model to Plan role and the Coder version to Act role.
-
No thinking only? Why's that?
- they have a 480B-A35B thinking coder model in the works, they'll probably distill from that
-
The only one that good both at code and writing is GLM-4, but it has nonexistent long context handling. Small 3.2 is okay too but dumber.
-
I hope they still release a dense 30B+ coder. I don't trust tiny MoE models to output anything useful. Being lightning-fast is nice, but output quality is what matters the most for coding.
-
a 30B-A3B MoE is poised to compete against ~10B dense models. It loses to Qwen3-14B for instance.
- I think that's a poor observation, dense models are usually better than moes. In swebench verified with open hands scaffolding devstral small actually scores a little higher. Unfortunately this is the only benchmark I've been able to find that has both.
-
in the 30B-A3B size, there is Qwen3-30B-A3B-Instruct-2507, it's for general use. "Instruct" means it was trained for user-assistant conversations, like following instructions, not just text completion (unlike Base models).
- Also, there is Qwen3-Coder-30B-A3B-Instruct, which is better for coding tasks (but could be worse at everything that isn't related to coding), also it supports FIM (Fill in the Middle) for code completion, and it's also "Instruct" so it can do the instruction following as well.
- So "Base" usually means the model can only continue text.
- "Instruct" means it can follow user's instructions (the
user,assistant,systemstuff). - "Coder" in Qwen models means it's a model for coding, trained on coding related stuff mostly.
- Also there is Qwen3-30B-A3B-Thinking-2507. From what I understand, it's like Instruct but with thinking (with something like this before every reply:
<think>Okay, ...</think>)
-
So as I understand it, instruct models are specially trained to follow instructions better / adhere to them more?
- Yeah, you could say that
- I mean, you can think of an Instruct model as of a ChatGPT-like model, which can chat with the user. But models like that don't necessarily have that "Instruct" in their names.
-
14B and smaller are my only realistic options with high context
- So far, my testing shows that the 30B-a3b-2507 (non-thinking) is on par or better than Gemma3 27B. If the new 14B delivers improvement on par with what the 30B-a3b-2507 achieved over its predecessor, then it would be in the same ballpark as Gemma3 27B since the old 14B and 30B-a3b—were already quite close in quality.
-
“Chat” and “instruct” modes are two common interaction styles in large language models.
- These modes differ significantly in how they handle inputs and generate responses.
-
The “chat” mode aims to engage users in free-form conversation, simulating a chatting experience with an emphasis on fluency and coherence;
-
the “instruct” mode, on the other hand, focuses on understanding and executing specific user commands, striving for task accuracy and operational precision.
- They excel when the entire task and all requirements are included in one prompt, but they’re not designed to maintain context, handle back-and-forth exchanges, or interpret subtle hints over multiple turns.
-
- This model is hopefully going to speedup the 600B version
- I tried this paired with Unsloth dynamic quant: there's a token mismatch, token 128815 exists there as "PAD_TOKEN", so you probably have to use the gguf tools found in llama.cpp to edit these existing models, the draft, or convert again if you're unsure of that
-
draft model means?
- If you use a feature called speculative decoding, you load up your main model (eg Deepseek R1 671B) and a draft model (this 0.5B model).
- The point is you can draft what the next few tokens/words should be and pass it to the main model to verify.
- This basically means a lot of filler tokens can be generated much faster by the smaller draft model, resulting in a significant performance improvement with no degradation in quality. The benefits get larger the difference between the main model and draft model in size.
- LM Studio has this feature built in and it’s best to watch it enabled.
-
I didn't understand, what this model does, and how should I use it?
- It's like an inexperienced student that can come up with 10 ideas on the spot. But then the experienced teacher can say, hey wait, Idea no. 7 might not be that bad and is worth pursuing. This process is much faster than the teacher coming up with a new idea on his own. Basically verifying an idea is faster than coming up with a genuine good one for a teacher.
- Because LLM inference is mostly memory bandwidth limited, you can evaluate multiple inferences in parallel for basically free
-
I have wondered if an asymmetrical MOE could outperform speculative decoding. If the MOE had a large expert and a small expert, you could have the router send all basic English words to the small expert… then rejection would never happen.
-
The draft model needs to be same architecture but smaller model.
-
The problem with drafting MOE models is that the amount of weights increases if more tokens are calculated per pass. It won't be the same chosen experts for each token.
-
Depends on the task. For code autocomplete Qwen/Qwen3-14B-AWQ nothink is awful. I like Qwen2.5-coder:14b.
-
Devstral is not better than qwen3-32B in general-purpose tasks. I guess it was trained to be specific to that openhands particular agent.
-
- All models are from Bartowski - q4km version
- GLM-4-32b is insanely good for html code frontend.
-
Yeah, I have also tried to generate webpages with a couple of models, like GLM-4, Qwen3, Phi-4 Reasoning, etc. GLM-4 is so far the clear winner at these tasks. It's a gem in my model collection.
-
I don't think comparing HTML generated in a single shot is a good benchmark for intelligence. The model might just be repeating patterns it has memorized, resulting in very similar-looking pages.
- I've done some experiments and concluded that current LLMs (including Gemini) have no real understanding of what makes a good web design (they are somehow render-blind).
- A more useful benchmark might be asking the model to modify an existing web page template given custom instructions.
-
GLM falls flat on its face when I try to continue developing after the first prompt. It feels like a model trained (very well) for one-shots
-
🤔 what are the challenges of fine tuning deepseek coder or codellama on a real world codebase? : r/LocalLLaMA _202508
- i’m curious about fine tuning code llms like deepseek coder or codellama on an actual messy real world codebase.
-
It's probably not something you want to do. You might be better off with a RAG solution, but normal grep/search from tools like Claude Code or Codex works pretty well with good prompting.
-
when you say fine tuning isn’t worth it, was that based on trying it yourself and running into issues, or more from comparing results with rag and prompting?
- I've fine tuned before, and I have also used RAG. I specifically fine tuned on newer specs/docs for the ESP32, since most of the LLM's at the time had older data
- I mean it worked, I was able to improve things, but it was a big effort, and RAG ended up working just as well or better.
- The problem is code changes quickly, doing a fine tune on giant models would be impractical, as soon as you start changing the code, the fine-tune is outdated. RAG is a better solution, but even just regular grep/code searches work pretty well.
-
Stop expecting perfection or near-perfection and learn to work with "good enough" results. If 30B is good enough, then focus on the remaining portion that needs human intervention. You can't change the model behavior, but you can change the way you use it, and how you approach the overall problem.
- I have tried all sort of tasks from vibe coding, refactoring, disassembly. The key thing is that you need to know better/more than the AI to be an effective human. I found myself learning things that were beyond my reach previously, so even though the AI had objectively failed at the task assigned to it, through the process I learned enough to carry on the task with whatever meaningful results it had produced, and take the rest over the finishing line.
-
Is llama4 actually that bad, or are people working off of a collective meme from a poor first showing?
- Poor first showing and disappointment. Gemma 3 had issues during launch, but now that it's sorted I'm running the 1b, 4b, and 12b versions locally no problem.
- Lllama 4 has no version I can run locally. Llama 4 was hyped to be a huge deal, but it seems more geared towards enterprise or large scale rollouts.
-
I found most local LLMs to be unusable with Roo, apart from one or two that have been specifically finetuned to work with Roo and Cline.
- The default system prompt is insanely long, and it just confuses the LLMs. It's insanely long because Roo needs to explain to the LLM what sort of tools are available, and how to call them. Unfortunately, that leads to the issue that smaller local LLMs can't even find your instructions about what you even want them to do.
- QwenCoder, QwQ, Gemma3 27b, Deepseek R1 Distills (14b, 32b, 70b) - they all fail.
- The only models I found to work moderately well were tom_himanen/deepseek-r1-roo-cline-tools and hhao/qwen2.5-coder-tools
- Just checked: For me, the default system prompt in Roo's code mode is roughly 9000 tokens long. That doesn't even include the info about your workspace (directory structure, any open files, etc. ) yet.
-
did anyone try if it works with CLINE/ roo code?
- it didn't do well, but I am going to check to make sure my settings are right.
-
I tried a few simple tasks with the Q8 model on a 32gb macbook.
- The diffs will work at least.
- After the simple task I asked for it to do (insert another button in an html) succeeded, it failed at the last step with: "Cline tried to use attempt_completion without value for required parameter 'result'. Retrying..."
- It retried 2x before successfully figuring out how to use attempt_completion. Note, this is after the file itself was edited correctly.
- It made a few other edits decently well. Be careful with clarifications. If you ask it to do A, then clarify also B, it may do B only without doing A.
- I suspect this model will score okay ish on the aider coding benchmark, but will lose some percentage due to edit format.
- I set context to 32k, but Cline is yappy and can easily fill up the context.
- Using Q8 makes it slower than Q4, but coding is one of those things that are more sensitive to smaller quants, so I'm sticking with Q8 for now. It'd be cool if they release a QAT 4bit version, similar to Gemma 3 QAT. At Q8 it runs around 15tok/sec for me.
-
Conclusion: not anywhere near as good as Sonnet 3.7, but I'm not sure if that's due to my computer's limitations (quantized quality loss, context size, quantized kv cache, etc). It's not complete trash, so I'm hopeful. It might be really cheap to run from an inference provider for people who can't run it locally.
-
I'm playing around with it right now, and at q8_0 it's failing miserably at stuff that o3-mini easily one-shots.
-
Tried it and its completely useless, it writes paragraphs and paragraphs thinking about what I said instead of just doing it. These reasoning models that talk to themselves cant be the way.
-
Make sure to tweak params: {"temperature": 0.6, "top_p": 0.95}
-
i just played with using the 1.5b as a speculative model for the 15b with lmstudio seemed to work well even
-
Do you find it noticeably faster using speculative decoding?
- I can’t tell if the smaller model is loaded into VRAM or not, but it does seem faster
-
I was coding some Python last night. Qwen 14b -coder seems to be better than the deepcoder
-
try these settings for extra coherent coding with reasoning code models. Works amazing on QWEN R1 distill, which this is based on.
- Temp: .82 Dynamic temp range: 0.6 Top P: 0.2 Min P 0.05 Context length 30, 000 (with nmap and linear transformer.... yes really). XTC probability: 0 Repetition penalty: 1.03 Dry Multiplier : 0.25 Dry Base: 1.75 Dry Allowed Length: 3 Repetion Penelty Range: 512 Dry Penalty Range: 8192
-
- Conclusion: Qwen2.5 Coder 32B is still a better choice for coding, and it's not prime time for a 14B model yet.
-
For smaller models, you need to be providing a more explicit prompt.
-
I'm just using the 30BA3B for everything. It's not the smartest, but it is fast and I am impatient. So far, it has been good enough for most things. If there's something it struggles with, I switch to Gemini Pro.
- Once you get used to that speed it's hard to go back to a dense model in the 32B/30B size.
-
QwQ is goated but you have to accept waiting 3 billion years of thinking before getting your output
-
No, Qwen3 is better. 32B no-thinking or 30B-A3B with thinking.
- 14B is also great with thinking, probably better than 30B-A3B, you can run it in Q5 or Q6, and you can fit so much context.
-
Within 24G ram it supports only 2000 context size but for a normal Nextjs app it is too low. At least require 32k context size but then the memory requirement shoots up too.
-
For performance on it can’t compare to closed source or the huge param models like Deepseek v3.
- However it’s the only one a lot of people can run locally with a 24GB video card.
- Personally, I mostly switched to using claude3.7 for general questions/code gen and qwen 2.5 7B for FIM and code completion.
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🤔 I personally, prefer it to reasoning models of the same size just because when coding I am less eager to watch it ramble on, on how its going to answer and just want an answer. I think bigger and maybe even the same size reasoning models might give better answers but I am usually too impatient when coding to deal with all that.
- Same here, I like the concept of reasoning models but I am also impatient
- Yeah, reasoning models like R1 are too bulky for chat-coding.
-
it is weaker than bigger models, but it is better than codestral, except for context length. I am more than happy with 7b and 14b models;
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For its size, it's the best.
- DeepSeek R1 is 631B even with MoE, how can you compared to a 32B?
- Sonnet API is $3 / million tokens. Also, how do you compare to a local model?
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while with some local models that you can run on a 24gb or even a 48gb setup are "good enough" for simple tasks, or even processing a lot of documents, or whatever, for coding assistants they are a toy compared to what is available. When you do anything serious they are more a waste of time than remotely helpful. You can compete with what you can get with $10 a month with GitHub copilot or services like that.
- I'm still using local models for fun and learning, but it's hard to justify not using a cloud api like Gemini.
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I am using it with continue VSCode plugin. Not bad.
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32k context is not big enough. Need at least 4x for it to be really helpful.
- Qwen coder 32b has 128k context
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People seem to ignore this fact, not only you can extend it to 128k, but it almost don't degrade (compared to other 128k models). Problem is, only VLLM support the YaRN rope configuration needed to extend it.
- Also exllama supports it.
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QwQ is built on Qwen2.5-32B-Instruct and not Coder. (At least according to its HuggingFace page.)
- Reasoning 32b models just kill my mbp unfortunately
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nothing has been released that really holds a candle to Qwen-Coder 32B that can be run locally with a reasonably modest hobbyist machine.
- The closest we've come is Mistral Small 24B (and it's community fine tunes, like Arcee Blitz) and Llama 3.3 70B (very good at coding, but wayy larger and questionable if it beats Qwen).
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CoT models think too much for coding IMO. I think they are good for optimizing your prompt though.
- They might have a role for architecting. Like figuring out Rust traits is annoying and extra diagrams help as well. But for extra interns, no chain-of-thoughts please.
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I do this with Aider. R1 plans the code changes, Sonnet 3.7 writes the actual code based on it's output. It works really well.
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Do people prefer Qwen coder for coding compared to QWQ-preview or other COT models?
- What are pros would you say? I tend to find the thinking models catch their logical mistakes more often which saves time when I double check what it gives me back. Is it just the speed or is it actually more accurate for you?
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Qwq just takes to long to get an answer for my taste.
-
Gemma never been good at coding.
- Hell even the largest Gemini models have never been good at coding.
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Still waiting for someone with much better hardware to add longrope v2 and a reasoning finetune to qwen 2.5 coder 32b. With reasoning and a ridiculous context window extension that thing would be beast mode for local coding. longrope 2
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Also Chain of Draft: Thinking Faster by Writing Less
- It does not work. I've tried. No difference.
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I love how Claude 3.7 without reasoning beats gtp models with reasoning. The reasoning hype is a bit too much I think, and the way those models work feels like a step backwards and a little step forwards
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yep its still the best if you want to skip reasoning llms. Some of the reasoning llms are as good and maybe even better but at the cost of waiting for it to think which in my experience is 3 times longer wait as reasoning llms question everything even if they are cabable of spitting out an answer quickly.
- Yes I think waiting for reasoning is not worth (right now).
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QwQ 32B model is the best for Local with same power as Deepseek R1 671B Model. But requres 46 GB VRAM and 64 GB RAM, to be able to run it.
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QwQ 32b is even better in coding on my experience.
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I'm not clear if QwQ was 3x better or 3x more expensive
- Both
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Qwen coder instruct 32b had 8% and that model is quite useful.
- Now QwQ 20% in Aider is really a lot .. From my tests is quite good in coding ...of course is not level DP 670b or o3 mini yet .
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couldn't you combine QwQ 32B as the architect model and use Coder 32B as the editor model?
- Except locally you have to load and unload the models all the time if you don’t have enough vram.
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- OpenHands LM is built on the foundation of Qwen Coder 2.5 Instruct 32B, leveraging its powerful base capabilities for coding tasks.
- What sets OpenHands LM apart is our specialized fine-tuning process
- We used training data generated by OpenHands itself on a diverse set of open-source repositories
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It's annoying their comparison graph doesn't even include qwen2.5-coder 32b which this is based on.
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The model's performance isn't necessarily superior to other models in general. The thing is, that this model was specifically fine-tuned to work effectively with the OpenHands tooling system, similar to how a new employee receives training from a senior developer on company-specific tools, environment, and processes.
- Because the model was deliberately trained to use the OpenHands tools more effectively, it can leverage this specialized knowledge to achieve better scores on the benchmark. so it will do great in any benchmark where it can use openhands, and probably not as great in benchmarks that it cant.
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It's annoying their comparison graph doesn't even include qwen2.5-coder 32b which this is based on.
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Since it's not documented anywhere, and I don't see anyone talking about it: This fine-tune breaks the underlying Qwen2.5-Coder's FIM. It's faintly present, but often goes off the rails and starts chatting. I don't think this result is surprising, but I wanted to check.
- Outside of FIM, I cannot distinguish it from Qwen2.5-Coder-32B in my testing. The performance is virtually the same for everything I tried.
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have you tested it inside openhands? the whole fine tuning was to make it interact better with openhands, the fact that it didn't lose much outside of it is actually surprising.
- Ah, got it. I only ran it via llama-server with the model's default configuration through the usual completion API.
-
- What’s weird is that OpenHands has 54k+ stars on GitHub. For comparison: Roo Code sits at ~14k, and Cline is around 44k. So it’s clearly on the radar of devs. But when you go look it up on YouTube or Reddit—nothing. Practically no real discussion, no deep dives, barely any content.
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They used to be Open Devin. I think they started after Devin made a bit of a splash. Rebranding might have killed a bit of name recognition.
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The reason it has not been mentioned here is because the benchmarks for OH + local LLMs were not so good compared to cloud services.
- The OH team recently released an OH fine tuned LLM based on Qwen2.5 and now Mistral jumped in. And there is a good reason for their decision.
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I wanted to try OpenHands, but they don't make it easy to run the thing outside docker on a POSIX environment. They also don't make it easy to setup with your own API. I gave up after about an hour and switched to Roo to test the model.
- We're serious and the stars are real, but totally hear you on the install issues. We've tried to make it as easy as possible to set up with Docker, but getting it to work without docker is not as easy as it should be and we'll work on it.
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It's more difficult to setup, and after downloading like 20GB of Docker images, you need to spend $ on Claude 3.7 tokens or whatever SOTA models to actually get good results because you're stuck with the limited web app.
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it somewhat works with docker, but tools like LocAgent(which relies on external openhands_aci package), or file upload don't work for me. maybe it's just not mature enough yet
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🏠 Just to confirm, for every new feature or bug I want to work on I usually make a new convo to keep the context length short.
- In OpenHands a new convo is like a new instance so this requires pulling the repo, reinstalling dependencies, etc. which also eats up a ton of context window.
- Is there a way to have multiple convos on the same codebase without having to reinstall everything each convo? Or does it not matter that I start new convos and I can just keep requesting more and more things in the same convo?
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I decided to check the product and have been sitting with the settings for the whole day. Installing docker and running it is not a problem, but no matter how hard I try, it does not want to connect to the local model, although in the console via curl the connection to it goes. Plus I created a 10 GB container, this is quite a lot, I do not understand why it requires such a crazy size, this is almost the size of the entire operating system.
- I had a similar experience today. Running both LM studio and Open hands with more permissive networking settings allowed open hands to reach the LM studio web server.
- So maybe give that a try? IIRC it's add
--hostto thedocker runarguments and for LM studio it was update the setting to run the web server so it doesn't only resolve over localhost, and instead broadcasts over the machine IP.
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Too many undocumented things for a shittier version of cursor really, and since it's bring your own model the monthly cost will be HIGHER than cursor.
-
it somewhat works with docker, but tools like LocAgent(which relies on external openhands_aci package), or file upload don't work for me. maybe it's just not mature enough yet
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🏠🐛 OpenHands is really awesome and works quite well. However, for now you would need to setup the whole virtual machine if you want to run it on your host without giving privileged permission to the docker container.
- That’s because their docker container spins up another docker container and this type of functionality requires developers to give privileged permission or mount docker sockets (basically has the same problem if security vulnerability is found).
- This prevents developers in many companies from using OpenHands when they can’t use privileged containers easily, and setting up the whole virtual machine is a bit of overkill when you can spin up Docker-based Code Server with RooCode plugin without adding extra capabilities to a docker container.
- I believe at some point they will move to docker compose to spin up multiple Docker containers instead of using Docker inside Docker and this will simplify running OpenHands for broader community.
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I've been using the Q4_0 gguf version of the Qwen2.5 Coder Instruct, and I'm pleasantly surprised. Despite the significant loss in quality due to gguf quantization—where the loss, although hoped to be negligible, is still considerable compared to full loading—it performs similarly to the GPT-4o-mini and is far better than the non-advanced free version of Gemini.
- However, it still doesn't come close to GPT-4.0 for more complex requests, though it is reasonably close for simpler ones.
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It is currently 5th place on Aider leaderboard, above GPT-4o, but slightly worse than old Claude Sonnet 3.5 and o1, and quite worse than new Claude Sonnet 3.5.
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32gb is a nice compact size. I may pull the trigger on a 48gb mac mini pro.
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Vllm absolutely smashes llama.cpp in speed
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Does LM Studio use Vllm behind the scene? I do know Ollama uses llama.cpp
- LM Studio is also llama.cpp based
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- Today, I deployed Qwen2.5 14b and I find it's function calling, CoT reasoning, and instruction following to be fantastic. I might even say, better than GPT 4/4o. For all my use cases, anyway.
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how do you do function calling?
- I use either langchain or the Vercel ai sdk for function calling personally
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This model is fantastic, I just tested it for coding as well and it is the only quantized model which detects SQL syntax error (e.g., missing of comma) despite being smaller than 10 GBs.
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- I have been running Qwen 2.5 35B for coding tasks. Ever since, I have not reached out to Chat GPT. Used Sonnet 3.5 only for planning.. It is local and it helps with debugging. generates good code
- I am also impressed with its instruction following and JSON output generation.
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The 14B model (Q8) was doing better than 35B (Q5) during my testing. But it was pretty smart overall.
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Sonnet for planning? Like how?
- For the architecture and design of a solution. Also thinking through all the goods and bads about a particular design choice.
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Why not Qwen-Code?
- Because I want the model to support other general purpose use cases too.
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- It can code really fine, about Codestral 22b level
- It can speak okay on different languages.
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It's a 16b mixture of expert, not a 7b
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it runs fast and is reasonably good at Python and PowerShell. Somewhere around Deepseek Coder 33B in performance.
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It is fast but weak.
-
- It's really pretty and organized
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I assume this is because it was trained on Latex? I'm not sure how aggressively Anthropic / OpenAI are training on Latex.
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DeepSeek has some training magic in their coding models. We have been using DeepSeek for real code generation recently and the only complaint is the speed of their API. The price is great though.
- Separately I’ve been happy running v2 coder lite locally; almost as good as Codestral but significantly faster.
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Another complaint of their API is the context length. Their open weights supports 128k, But their API only supports 32k
-
- the response: I cannot fulfill your request as it goes against ethical and moral principles, and it is also illegal and potentially harmful.
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CodeLlama 70b have a prompt format issue. It make it spew censorship stuff
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Yeah, that model is quite censored indeed but its also very sensitive to its prompt template it seems
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This is a false positive, probably some sub classifier model needs to be adjusted.
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Why is CodeLlama on huggingface so opinionated, biased and arrogant ? : r/LocalLLaMA _202402
- Probably because it was at some point a general model which was further trained for code. By using a word like "appropriate" you are probably triggering a lot of political/sociological/emotional thoughts which is suboptimal if you are trying to code.
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The gotcha is having hardware fast enough to run it at usable rates
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codellama fucks up and refuses a lot of work, I wouldnt focus on it too much until someone finetunes the base model for instruct
- The model is fine once you get the new and complex prompt format right.
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It's a great idea on paper, but perhaps 70B is too big for most current consumer hardware.
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is there a realistic way to run a 70B model on a 4090, with maybe 1-2 token/sec or better?
- For Mixtral I get 5 token/s haven't tried an 70B (With 5 layers on CUDA and tensorrt). I am on DDR5 with a 7950X3D though. Ram speed seems to be the main issue.
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- All models support sequence lengths up to 100, 000 tokens
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That could be made more nuanced. They support input context sequences of up to 100, 000 tokens. The sequence length of the underlying model is 16, 384.
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Can you help us newcomers understand why this is so exciting?
- The context windows is basically the short term memory of the LLM. Larger window size allows "pre-initializing" it with more data. In this case a larger portion of your existing codebase can fit in, so it can provide more relevant answers and code-completion in that context.
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the key to the long context length is actually changing the base period!!! That was exactly the NTK scaling post here promoted, yet they didn't mention it at all. So they rushed out the linear interpolation paper to divert researchers' attention, but they secretly doing NTK
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Codellama - Has anyone found "Codellama 34B Instruct" to be uncooperative? : r/LocalLLaMA _202308
- Just an update: issue appears to be definitely solved by using the correct parameters when loading and querying the model.
- Fortunately many of the popular frameworks like text-generation-ui are getting updates that use the correct settings for this new class of codellama models
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[P] I fine-tuned Qwen 2.5 Coder on a single repo and got a 47% improvement in code completion accuracy : r/MachineLearning _202503
- Training data: Svelte source files from this repo on github
- Tl; dr: The fine-tuned model achieves a 47% improvement in the code completion task (tab autocomplete). Accuracy goes from 25% to 36% (exact match against ground truth) after a short training run of only 500 iterations on a single RTX 4090 GPU.
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This sounds like the strategy that https://ninetyfive.gg/ uses. Awesome to see your results open sourced! How did you figure out the best way to determine the prefix/middle/suffix splits for training? It seems like here you did something more clever than randomly picking a location in the file to split?
- the logic for determining the split is actually very basic right now, and not even all that comprehensive. It just looks for if-else blocks, function definitions, loops etc. and takes the entire block as the “middle” portion to be completed by the model. There’s also a min and max length filter I apply to get reasonably sized middle blocks.
- I just wanted to quickly get some results, so I implemented this very simple method, and there’s lots of room to improve on this. E.g., in the SAFIM paper, they determine “critical algorithm blocks” by checking if removal of those blocks results in compilation or test failures.
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Do you feel the benefit is from being familiar with the project or with your coding style? Most of my code is written via llm these days so style wise it would provide no benefit. But if the benefit comes from being more familiar with the project and it's goals without having to burn context I think that would be a meaningful improvement
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This is solid, is the code completely limited to certain stack usage?
- The dataset generation code is Svelte-specific because it only parses Svelte files, but the training itself is not. You can generate a training set in a similar manner for any language/stack of your choosing as long as you can parse the code into an AST.
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Do you have to fine-tune after every code edit?
- As the codebase evolves, some of the things that model has learnt will become out-dated. So yeah if a similar model is deployed on a real codebase, it will have to be fine-tuned periodically. But it probably won't be necessary to do after every single commit, minor changes in the code can be addressed by implementing an effective context selection algorithm.
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Do you touch all weights?
- No, it was a LoRA fine-tune with rank 16. 68M trainable parameters.
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These "500 iterations" that you mention, was that the entirety of your fine tuning? How long did it take?
- Yes, the checkpoint I evaluated was the one I got after iteration 500. It took about 1h 20m to get there.
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in total how many passes over the codebase the model train on? Is it just once? Or multiple times?
- I wasn’t even able to do one pass :) My training run only saw like 10% of the training set.
- I’ve switched to using torchtune for training, and with a smaller model and more powerful GPU, we’ll be able to do a full pass over the training set in 5-6 hours. Will make another post with an update.
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you can use the fine-tuned model via Continue. You can export the model in GGUF, serve via Ollama, and connect Continue to it.
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That will fit into the context window of most modern LLMs, there's no advantage to fine tuning
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"Smarter" RAG setups will probably serve you a lot better since among other things, if tuned, its tuned knowledge would be so specific and go out of date so easily when the codebase is modified. Requiring more feeding at the prompt anyway.
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Fine-tuning means supervised learning, so you would need training data in the form of input (probably a part of your codebase + a question about it) and output (expected output from the model). You can't fine-tune only using your codebase. You can maybe continue pretraining (unsupervised) but your data is too little for that.
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For comprehension, you're probably much better off chucking it into context, or failing that then RAG.
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- I want to finetune a model on a source code repository so I can ask question, create code based on it etc
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finetuning is not meant for this, finetuning / training is done to achieve a knowledge level / “intelligence” not for literal facts.
- Basically you need to push your repo in the context so you can ask questions of it, but llama3 has only 8k context.
- But although I do believe code is perfect for rag ( basically you could create a file-overview, then function overview per file and then a code overview per function, so you can rag for 3 levels deep easily) I have not seen any rag implementation which is made for code. And general rag will not work good, general rag will just take x characters and in doing so loose all coherence code has.
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Basically I believe you need specialized rag and large context for what you want, that way you can also do it on real-time git code updated after every commit instead of constant finetuning
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Unsloth allows you to train your own lora on some LLMs.
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RAG and increased context windows eliminated many people's use cases for LoRA (myself included) but it's still very much real and around
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there are, but LLMs each have such a big variety of architectures that it’s not common for public releases and are usually used for developers. Ex: a lora for Gemini wouldn’t work for Gemma 3 nor llama nor really any other release nor version . Much easier to train it as a new version if you’re adding fine tuning .
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In LLMs we generally don't differentiate between LoRA and FFT in the same way that a distinction is drawn in image generation.
- Most of the finetunes that you see in LLMs are actually LoRAs with the LoRA additively merged into the weights to lower the inference cost.
- LoRAs actually aren't free in terms of computational performance, and LLMs are really hard to run, so people generally merge them for that reason.
- Additionally, LoRAs are often used there as a means of control, whereas generally LLMs are general purpose enough to just prompt for the thing you want.
- So, long story short, if someone does a LoRA in an LLM, it's usually to create a complete change in experience, more like a custom SDXL finetune, for example, so it doesn't really make sense to distribute them in the same way.
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LORAs do exist for LLMs, but as opposed to Diffusion, which only has SD 1.5, SDXL, SD3.5, and Flux, in the LLM space we seem to be getting a different model every week, each with a new architecture. Hence, it's generally considered impractical to have a separate LORA for every model and have the end user manage it. We also don't have a CivitAI-like website where we can distribute such things easily.
- In LLMs, most of the time, fine-tuners train a LORA, and merge it into the checkpoint, then distribute the new checkpoint and its quants.
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It is a big thing for LLMs. In fact Unsloth is one of the most starred Github projects and it's whole purpose is for training LLM LoRAs.
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it is because they won't work very well on quantized models, so they just merge it into the model instead
- They work fine and you can train on quantized models too. The issue is software support is half baked outside of transformers. A cycle of lora is inconvenient -> people don't use lora -> devs don't improve lora support. We're on year 3 of this.
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Each LoRA is base model specific.
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more accurately, not all fine tunes are a model + LoRA merge.
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Finetunes are not a model+LoRa merge. It's the other way around: LoRAs are made by subtracting the base model from a fine-tune, so the LoRA remains.
- There are a lot of ways to fine-tune a model. What you're describing is called full fine-tuning (FFT). Some people use PEFT (parameter-efficient fine-tuning) which includes, but isn't limited to, a model+LoRa merge.
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7B–9B models are just too unreliable for agentic coding. 20B+ (like Qwen 3.5 27B) is where it starts working properly.
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I have also found that 9B is too small. The OmniCoder-9B fine tune of Qwen3.5-9B manages to make successful tool calls most of the time, but you have to set the parameters just right to avoid reasoning loops, and it's still lacking in world knowledge so it struggles to write valid code.
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With both nemotron nano and glm4.7 flash, I have not been able to make it write a simple program that actually draws an ascii art that reads Hello World. It can do plain text fine...it's been extremely funny as well as frustrating
-
- mistralai_Devstral-Small-2-24B-Instruct-2512-Q8_0.gguf - works but it's kind of slow for coding
- nvidia_Nemotron-3-Nano-30B-A3B-Q8_0.gguf - text generation is fast, but the actual coding is slow and often incorrect
- Qwen3-Coder-30B-A3B-Instruct-Q8_0.gguf - works correctly and it's fast
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Devstrall can do straightshot edits and generally keep up with agentic work, but the results as the context grows are terrible.
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🆚 I tested a few local hosted coding models with VSCode / cline so that you don't have to : r/LocalLLaMA _202511
- Setup: Ubuntu 24.04, 2x 4060 Ti 16 GB (32 GB total VRAM), VS Code + Cline, models served via Ollama / GGUF. Context for local models was usually ~96k tokens (anything much bigger spilled into RAM and became 7-20x slower).
- Tasks ranged from YOLO prompts ("Write a Space Invaders game in a single HTML file") to a moderately detailed spec for a modernized Space Invaders.
- Headline result: Qwen 3 Coder 30B is the only family I tested that consistently worked well with Cline and produced usable games. At 4-bit it's already solid; quality drops noticeably at 3-bit and 2-bit (more logic bugs, more broken runs). With 4-bit and 32 GB VRAM you can keep ~ 100k context and still be reasorably fast. If you can spare more VRAM or live with reduced context, higher-bit Qwen 3 Coder (e.g. 6-bit) does help. But 4-bit is the practical sweet spot for 32 GiB VRAM.
- Merges/prunes of Qwen 3 Coder generally underperformed the original. The cerebras REAP 25B prune and YOYO merges were noticeably buggier and less reliable than vanilla Qwen 3 Coder 30B, even at higher bit widths. They sometimes produced runnable code, but with a much higher "Cline has to rerun / you have to hand-debug or giveup" rate. TL; DR: for coding, the unmodified coder models beat their fancy descendants.
- Non-coder 30B models and "hot" general models mostly disappointed in this setup. Qwen 3 30B (base/instruct from various sources), devstral 24B, Skyfall 31B v4, Nemotron Nano 9B v2, and Olmo 3 32B either: (a) fought with Cline (rambling, overwriting their own code, breaking the project), or (b) produced very broken game logic that wasn't fixable in one or two debug rounds. Some also forced me to shrink context so much they stopped being interesting for larger tasks.
- My current working hypothesis: to do enthusiast-level Al-assisted coding in VS Code with Cline, one really needs to have at least 32 GB VRAM for usable models. Preferably use an untampered Qwen 3 Coder 30B (Ollama's default 4-bit, or an unsloth GGUF at 4-6 bits). Avoid going below 4-bit for coding, be wary of fancy merges/prunes, and don't expect miracles without a decent spec.
- I documented all runs (code + notes) in a repo on GitHub (https://github.com/DrMicrobit/lllm_suit)
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Building retro games isn't representative of real software development.
- A proper evaluation should test: Configuration, planning, execution
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glm 4.5 air fp8, did perform well in my local testing. Wanted to call that out as it's a non coding model but does feel still mile away from actual claudecode/codex. Still need to do testing on same setup using qwen3
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If using small models, Qwen3 Code 30B-A3B is one of the best ones for coding. Qwen family also has great vision models.
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Most of the time I use Kimi K2 though, it is my favorite local model so far. It works very well with Cline and Roo Code.
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GLM 4.5 Air 3.14bpw EXL3 with Cline is pretty good, I'd guess probably better than Qwen 3 30B Coder q4 GGUF (no tests done, just guess), and it runs within 48GB of VRAM at 60k ctx fine.
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One thing I have noticed, at least from Qwen 3 Coder 30B and its REAPed variant, is that the mileage I get from it can depend on the tool that I use. I haven't tried many of them, but at the very least I can say my personal experience has been a lot better with Aider than Kilo Code
-
- what would be your choice if you had 4x RTX A6000 with nvlink and 512GB DDR4 RAM as your llm host?
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I’ve had that exact setup and by far my favorite was Qwen3 235B A22B Instruct 2507 GPTQ INT4 in vLLM tensor parallel. 256k context, fast as blazes, practically SOTA for open weights, and it all fits in GPU. It’s amazing.
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I’ve done quite a bit of testing and found Qwen2.5 Instruct to be better at coding (at least for my use-case) than Qwen2.5 Coder
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The coder variant was over 400B and wasn’t any better than 235B Instruct in my tests.
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Aider has their leaderboard. gpt-oss-120b scores fairly decent.
- I'm running gpt-oss-120B on just under 64GB at full context (131k tokens) which I don't even need because my project is only 16k tokens.
- I do like being able to dump an entire manual into it if I need to though. For instance, a raylib cheatsheet converted to markdown.
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Tested on the 48 CPU with 96GB RAM at DO and, 31.59 tokens per second prompt processing, 6.31 tokens per second generation.
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I found that the only open weight model that is close to Claude performance is Alibaba's QWEN Coder 480B. Neither GLM 4.5 nor DeepSeek V3.2 (which themselves are impossible to run locally) produced satisfactory results for me. Even with 4-bit quantization it requires a whopping 300 GB of memory. It means 10 x RTX 5090 GPUs or 3 x RTX 6000 96 GB which would make the budget well above $30k at which point it makes sense to look at H100.
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9950X3D, RTX 4070, and 64 GB DDR5 RAM. I assume the GPU (RTX 4070) will be the biggest bottleneck.
- No, the biggest bottleneck is DDR5. It's just inadequate for massive scale of data that is moved around during inference. VRAM is much superior.
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CodeQwen-1.5-7b is a powerful coding model according to livecodebench.
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In the 8GB~12GB range I have used a few specialised ones:
- Codestral-22B-v0.1-Q4KM
- DeepSeek-Coder-V2-Lite-Q5KM
- CodeGeeX4-All-9B-Q8
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CodeGeeX4-ALL-9B, CodeQwen1.5-7B-Chat and Codestral-22B-v0.1 are very good small coding models. There's also the DeepSeek-Coder-V2 models.
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Check out bigcode-bench.github.io. Top 7B on there is CodeQwen1.5-7B-Chat which has been good in my experience. CodeLlama is the lowest ranked 7B.
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- Chipset Model: Apple M4 Pro
- 给出了很多模型的速度tops
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Why not Qwen3-32B? It's better
- I prefer MoE just for the (initial) speed, but a dense one that I like a lot is Devstral small.
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Context window size?
- I run Qwen3Coder and GPT-OSS at 131k via lm studio, mlx format, 6bit and 4bit respectively.
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Mean nothing. After 60k all models start to hallucinate
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How is the prompt processing speeds for long contexts ?
- Slow, like 3x to 10x slower than api. But that’s not due to the models, but my hardware.
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I've used GPT-OSS20b and Qwen3-Coder-30B-A3B-Instruct-GGUF, I like both.
- However, OSS20b due to reasoning, can come up with better code instructions and optimizations/refactoring unlike the Qwen3-Coder.
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I just run 30B on RAM (I have 32GB) currently with ollama, which occupies 19 GB with a 4096 context.
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If you're using native tool calling (as opposed to Roo/Cline XML-style calls), you can also strongly consider GPT OSS 20B, it is very fast and has a configurable thinking setting, so is a very versatile option.
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Gpt oss 20b or qwen 30b a3b 2507 (thinking version), these aren't just coding models but they do well at coding and run fast on a CPU with enough system ram.
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GLM-4 0414 9b or Qwen 2.5 Coder 14b are probably your best bets around that size. They are surprisingly good as long you can break your problem down into focused bite-sized pieces.
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there is a GLM-4 0414 32b and I really like it. Even at brain-damaged quantizations like IQ2_XXS it is still surprisingly functional.
- That said, I've mostly shifted to Qwen 3 Coder 30b a3b since it is so much faster and sits right in the ability sweet spot between the 9b and 32b GLM-4 models.
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Seed-Coder-8B-Instruct works quite well for me. There's also a reasoning version but I find that version is worse than the instruct version.
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There arent particularly good ones around 10B in my experience. The one i havent been able to find a gguf for yet is Nvidia's Nemotron 9b v2 it's punching way above it's weight limit.
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Devstral with Pixtral layers baked in is a lot more precise than Qwen3-coder 30B.
- Check Unsloth's version from Huggingface, it has all the multimodal bells and whistles. Take a screenshot, give it to the model, and see magick happen as it creates both back-end and front-end code for a project.
- This also works in Claude code, if you setup claude-code-proxy to convince that your local Devstral is Sonnet/Opus/Haiku.
- You can also try OpenHands, as Devstral was frist made for that.
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Qwen3-coder B30A3 is really good and fast Works like a charm on my 4060ti 16GB
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I'm using hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF: Q2_K specifically and even at that low quant, it's exceptional at coding and tool use.
- Why are you going so low? Just offload the the inactive experts to CPU and only keep the active ones on the vram. Yes, it will be slower but also provide better quality as you will be able to run Q5 (or Q6) UD K XL with about 15t/s and a 32k context.
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Qwen3 is nice, but thinking part of it sucks - not good.
- There are also models that have been adjusted to use tooling: hhao/qwen2.5-coder-tools maryasov/qwen2.5-coder-cline
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There is also the new qwen3 instruct variant, which doesn't think.
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Qwen2.5-Coder-32B vs Qwen3-32B is a fun back and forth, and both are amazing, but if you're coding you're ITERATING, and most consumer hardware (maybe short of the 2TB/s 5090) just doesn't feel acceptable here unless you quantize it down a lot, and Q4 with quantized cache starts to make silly mistakes.
- Qwen3-30b-a3b (this also goes for the a6b version) seems like a winner because it's amazingly smart but inferences at lightspeed.. but this model consistently shows that it falls off with longer context. For coding, you'll encounter this dropoff even if you're just writing microservices after not long.
- So Qwen3-14B is currently my go to. It handles large contexts like a champ, is shockingly smart (closer to 32B than Qwen2.5's 14B weights were), and inferences fast enough where you can iterate quickly on fairly modest hardware.
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Gemini 2.5 Pro now has a giant 2M token context, great code quality, and fewer “hallucinations, ” while GPT-4o is close to Claude with 1M tokens and strong integration. Both now rival or surpass Claude in many tasks. definitely worth revisiting. I use each AI for it's strengths per the task.
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Codellama 70B was a fine-tune of Llama 2. Since then the only coder fine-tune above 70B was the DS 236B. So I'm assuming later models 70B and above have also been trained on coding datasets. Like Qwen 2.5 32B coder probably was fine-tuned on the same coding datasets used in their 72B. And that coder certainly beats codellama - codestral 22B did.
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Deepseek (R1 \ V3) at Q1 at 4k context shits on Llama2 70b any day of the week for whatever task your heart desires.
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- My main dev language is JAVA and React (Typescript).
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Devstral is the best local model and it aint even close.
- Devstral Q4_K_M (models size: 14.34GB, I set context to 45K) is a great architect! it follows instructions well, uses all tools properly and has decent speed.
- Q3_XXS (9.51GB, 70K context) has been crushing it as a "turbo" coder for me, even faster than the qwen 8B's and smarter too!
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The only local model under 30B which worked in Roo Code for me was qwen2.5-coder-tools. It's a fine-tunned on Cline's prompts.
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I've had decent luck with GLM, Gemma, and Qwen3-32B as well as 30B-A3B. Sounds like I need to try Mistral.
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How are you serving devstral? We're running fp8 w/ full cache and 128k context on vLLM and don't see problems with tool use at all. Cline seems to work fine with it. Even things like memory-bank and .rules work.
- Best way to prompt it, from my experience, is like this: "based on x impl in @file, do y in @other_file."
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There are models thar degrade drastically below fp8, and I believe devstral is one of them. When I read the experience of many users online I realised people running in on full precision or q8 were very satisfied, but people running q4 said it worked awfully.
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For me Devstral q8 works well in Cline's planning mode with tool calls. For code mode I like to use Qwen coder 32b q8. This works only on Cline for me; I could not get anything useful out of Roo Code with these models: it is always running into loops
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Devstral was fast and mostly good for me (HTML, CS, JS, Python). Albeit @ q8 quantisation and 64k context. Mostly small and not complexed projects. When I tried something more complexed, “make a chess game” it failed to implement simple logic correctly. It also didn’t attempt to implement more logic like (en passant, castling etc).
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Gemma 3 is not a good coding model. Qwen2.5 coder, Qwen3, GLM-4, Mistral Small - these are better.
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Devstral’s got my full support. It's the only local model under 32B that can actually use tools to gather context in Roo/Cline without breaking a sweat.
- Qwq's performance in roo is a bit off on my end. Its tool calling doesn't quite match up to devstral. Maybe it'll perform better with more context.
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Devstral landed two days ago, so it’s a bit early to have a full overview, but with an RTX 3900, it’s the first model that works out of the box with OLLAMA and AIDER, plus it runs at a decent speed (35 t/s for me) and 100% on GPU even with a large context. So, I would recommend giving it a try.
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I have been using deepcoder and hás serve me well until now. Still waiting for Qwen3-coder.
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I replaced Qwen 2.5 Coder with GLM 4 0414 recently. Qwen 3 seemed OK. In my tests, it was still outperformed by Qwen 2.5 Coder, although reasoning might give it the edge in certain use cases.
- I agree about Qwen 3 not being that good at coding in general. It's weird because Supernova Medius, a mashup of Qwen 2.5 Coder 14B and Llama, was really good at coding.
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For web development, GLM-4 is significantly better than Qwen 3, QwQ and Gemma 3 for my use cases.
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Don't discard gemma totally as it can analyze images, so you can ask it to analyze the UI and what not.
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Go for the highest number of parameters you can fit in vram along with your context, then choose the highest quant of that version that will still fit.
- I find that the 32b models have issues with simple code … I can’t imagine a 7b model being anything more than a curiosity.
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Qwen2.5-coder 7B and 14B are both solid for local coding tasks.
- Deepseek-Coder (6.7B or 13B): very strong with Python and general coding.
- Code LLaMA 13B: great for code generation and reasoning.
- StarCoder2 (7B or 15B): worth a try if you can stretch the limit a bit. Quite powerful.
- Phi-2 (2.7B): super lightweight and fast for simpler tasks.
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Devstral is really good right now and IMHO it's better than qwen2.5-coder.
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Qwen2.5 coder, QwQ. Perhaps Mistral Small 24b.
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If starting from no code base, probably QwQ takes top spot due to its reasoning (thinking) steps.
- For adapting existing code Qwen 2.5 Coder 32B, probably edges a little maybe.
- Where the local models don’t include things like logging or type hints unless prompted (Python language), Gemini 2.5 Pro just did it without prompting.
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Ohhh something important for the local models. Make sure to configure them to get the best performance. (E.g. lower the Temperature setting to 0.5 or lower to achieve better performance for coding and maths)
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I really enjoy the directness of models that don't have reasoning. So for me that would be Qwen 2.5 32B Coder, Qwen 2.5 72B Instruct, Mistral Large 2, Deepseek V2.5, Deepseek V3-0324
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I use code models with Cline. DeepSeek V3-0324 (used through openrouter API) is much much better than Qwen Coder 32B/72B instruct. I can give it more advanced requests for implementations of various features, it's close in usability to Claude 3.7 Sonnet, it's a real workhorse. Qwen 2.5 72B Instruct which is my preffered local model as of now (I hope Qwen 3 will blow it out of the water soon)
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When searching for local model for code generation, take into consideration the context length that you can make it work at locally - I hit 40k ctx very easily even when working on single 500 LOC files.
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I expect context to be an issue. I'm currently thinking a lot about the RAG strategy (I have a lot of experience with RAG design but only for documents). I'm also pondering building an evolving graph database, which would help compile variables, functions, and classes since the files are highly structured.
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how do you like Cline?
- It's designed to work best with larger API-only LLMs, and it shows, so 14B LLMs for example have issues with using it's tools. I started off with Cline after doing some coding through chatting in the LibreChat UI, I didn't try other tools/extensions.
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Qwen2.5-coder32b has been the best for me, but I can only run it slowly on my CPU, so I swap down to the Qwen2.5-coder14b that I can run on my 16gb GFX card.
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Qwen2.5-coder-32b is probably best, it’s great with code quality, speed and most coding use-cases.
- DeepSeek-V3 & DeepSeek-R1 are probably the very best, but are very very slow on consumer hardware, not practical and should be reserved for very challenging assignments only.
- If you want to alter existing code, Qwen2.5-coder-32b is probably your best bet.
- If you want to generate from nothing, I’m not sure which wins from practicality perspective. (Possibly QWQ-preview-32b, Qwen2.5-coder-32b, llama3.3-70b or Qwen2.5-72b).
- If you can handle the very very long completion time, DeepSeek-V3-671b and DeepSeek-R1-671b should be number 1.
- DeepSeek-V3 can run on consumer hardware, if your available disk space is enough, but expect it to take 20mins or so to complete prompt outputs with the unsloth’s most Quantized model, if you use the SSD (I used Samsung Pro 990 NVMe) as additional RAM.
- The DeepSeek-R1 time expands massively with the R1 model due to the additional thinking and tokens that it does. (Took 3hrs to create Flappy Bird game for me in Python, V3 took a little over 20min by comparison, about 0.5 t/s )
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Try qwen2.5-codder, even 0.5b version seems capable of producing useful code (if you keep it small, like scripts etc.)
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double down on the qwen-2.5-codder, even 0.5b is usable for small scripts
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qwen2.5-coder:32b is the best you can run, though it won't fit entirely in your gpu, and will offload onto system ram, so it might be slow.
- The smaller version, qwen2.5-coder:14b will fit entirely in your gpu
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what will be the suitable ram size for 32b
- You'll need at least 24 GB vram to fit an entire 32B model onto your GPU.
- Your GPU (RTX 4080) has 16 GB vram, so you can still use 32B models, but part of it will be on system ram instead of vram, so it will run slower.
- You can also try a smaller quantization, like qwen2.5-coder:32b-instruct-q3_K_S (which is 3-bit, instead of 4-bit, the default), which should fit entirely in 16 GB vram, but the quality will be worse
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Is there anything I need to tweak for it to offload into system RAM? Because it always gives me an error about lack of RAM
- No, ollama offloads automatically without any tweaks needed
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I tried qwen coder 2.5 u really need to use the 32b and q8 and it's way better than the 14b. I
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Roocoder which is based on cline is probably better. It's scary cause it can run in auto.
- you could leave it over night and it could fix the code or totally screw up and loop all night lol.
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Run tests on the the q8 vs q6 vs q4. The 32b model is way better than 14b btw
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just going by the basic rule of thumb.if you want the best model you can fit on your machine always going with q4 of larger model
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how this 32b can be better than 7b-coder tuned version?
- coder finetunes are definitely better than there corresponding base models, but Due to the increased parameters, it is more accurate in first try and also adheres to prompt better and also understands the problem way better
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the 32b is a lot lot lot better for coding.
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The larger models can be smarter, but if you don't have the spare VRAM, it might not be able to look at your whole project.
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Someone suggested Codestral-22B-v0.1-IQ3_M in another thread, and Ive been using that one for coding ever since. Mostly for python. Its really good, way better than all other I have tried.
- Most of the time it gives me run ready redesigns and code, quite impressive.
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I’m confused, why is codellama-70b so bad on this leaderboard
- Llama 70b feels more like a ESG score replenisher. Practically brain dead. Wonder if finetunes will uncover anything useful beneath
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I haven't used it, but I heard it's been too censored and it affected its coding abilities. But who knows.