| --- |
| license: apache-2.0 |
| language: |
| - en |
| - id |
| tags: |
| - code |
| - nextjs |
| - typescript |
| - react |
| - unsloth |
| - web-design |
| --- |
| |
| # Gwen 1.0 Code Pro |
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|  |
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| **Gwen 1.0 Code Pro** is the balanced flagship of the Gwen series. It is an elegant, high-performance AI coding assistant designed for professional developers who require deep reasoning and clean implementation in modern web stacks. |
|
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| ## Model Details |
|
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| ### Model Description |
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| Gwen 1.0 Code Pro serves as the mid-tier powerhouse, striking the perfect balance between speed and advanced architectural reasoning. It is specialized in the **Vercel** aesthetic, focusing on React 19, Next.js 15/16, and high-end motion libraries. |
|
|
| - **Developed by:** JinXSuper |
| - **Model type:** Causal Language Model (Fine-tuned for Professional Web Development) |
| - **Language(s) (NLP):** English, Indonesian (Natural Mix) |
| - **License:** apache-2.0 |
| - **Finetuned from model:** Qwen Series |
|
|
| ## Uses |
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| ### Direct Use |
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| Gwen 1.0 Code Pro is optimized for: |
| * Designing and implementing complex **React 19** and **Next.js** systems. |
| * Advanced UI/UX development using **Tailwind CSS v4** and **Geist Icons**. |
| * Crafting fluid animations with **GSAP**, **Framer Motion**, and **Lenis**. |
| * Managing modern state management and API integrations within the **Vercel** ecosystem. |
|
|
| ### Out-of-Scope Use |
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| * Low-level hardware or kernel-level programming. |
| * General-purpose prose or non-technical content generation. |
|
|
| ## Bias, Risks, and Limitations |
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| Gwen 1.0 Pro is deeply aligned with the "sharp and elegant" design philosophy of **JinXSuper**. It may prioritize minimalist code structures and high-end performance over legacy support or verbose boilerplate. |
|
|
| ## How to Get Started with the Model |
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|
| You can load the Pro model using the following snippet: |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_id = "jinxsuperdev/gwen1.0-code-pro" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id) |
