Instructions to use jinxsuperdev/gwen1.0-code-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Desktop
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license: apache-2.0
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language:
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- code
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- nextjs
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- react
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- unsloth
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- web-design
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# Gwen 1.0 Code Pro
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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.
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- **Developed by:** JinXSuper
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- **Model type:** Causal Language Model (Fine-tuned for Professional Web Development)
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- **Language(s) (NLP):** English, Indonesian (Natural Mix)
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- **License:** apache-2.0
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- **Finetuned from model:** Qwen Series
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## Uses
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### Direct Use
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Gwen 1.0 Code Pro is optimized for:
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* Designing and implementing complex **React 19** and **Next.js** systems.
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* Advanced UI/UX development using **Tailwind CSS v4** and **Geist Icons**.
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* Crafting fluid animations with **GSAP**, **Framer Motion**, and **Lenis**.
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* Managing modern state management and API integrations within the **Vercel** ecosystem.
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### Out-of-Scope Use
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* Low-level hardware or kernel-level programming.
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* General-purpose prose or non-technical content generation.
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## 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.
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## How to Get Started with the Model
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You can load the Pro model using the following snippet:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "jinxsuperdev/gwen1.0-code-pro"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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