File size: 1,932 Bytes
650b184 e4f3304 650b184 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | ---
license: apache-2.0
language:
- en
- id
tags:
- code
- nextjs
- typescript
- react
- unsloth
- web-design
---
# Gwen 1.0 Code Mini
**Gwen 1.0 Code Mini** is an elegant, high-efficiency AI coding assistant specialized in modern web development stacks. It is designed to be sharp, loyal, and highly effective for developers working with the Vercel ecosystem.
## Model Details
### Model Description
Gwen 1.0 Code Mini is the lightweight variant of the Gwen series, optimized for speed and efficiency without sacrificing the "sharp" reasoning required for complex UI/UX tasks. It excels in generating clean, minimalist code following the Geist Design System.
- **Developed by:** JinXSuper
- **Model type:** Causal Language Model (Fine-tuned for Web Development)
- **Language(s) (NLP):** English, Indonesian (Natural Mix)
- **License:** apache-2.0
- **Finetuned from model:** Qwen Series
## Uses
### Direct Use
Gwen 1.0 Code Mini is intended for:
* Building modern web interfaces using **React 19** and **Next.js 15/16**.
* Styling with **Tailwind CSS v4** and implementing **shadcn/ui** components.
* Integrating motion libraries like **GSAP**, **Framer Motion**, and **Lenis**.
### Out-of-Scope Use
* Low-level system programming (C, Rust, or Assembly).
* Non-technical creative writing or general-purpose tasks outside of coding.
## Bias, Risks, and Limitations
Gwen 1.0 is heavily biased towards minimalist, high-contrast aesthetics and the **Vercel** design philosophy. It may prioritize "sharp" and "clean" code structures over more verbose legacy patterns.
## How to Get Started with the Model
You can load the model using the following snippet:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "jinxsuperdev/gwen1.0-code-mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id) |