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README.md
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# 🚀 **Model Card for Tess-T1**
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## 🌟 **Model Overview**
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"Landing Page"
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---
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##
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---
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##
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*See examples demonstrating the powerful reasoning and component creation capabilities of
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---
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##
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###
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- **Automatic Component Generation**: Quickly produce React components from textual prompts.
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- **Agent-based Web Development**: Integrate into automated coding systems for faster frontend workflows.
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- **Frontend Refactoring**: Automate the optimization and semantic enhancement of React code.
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###
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- **Focused on React**: Limited use outside React.js frameworks.
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- **Complex State Management**: May require manual adjustments for highly dynamic state management scenarios.
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##
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### **Inference Example**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "smirki/
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
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##
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- **Strengths**:
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- Strong semantic React component generation.
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##
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- **Architecture**: Transformer-based LLM
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- **Base Model**: Qwen2.5-Coder-14B-Instruct
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##
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```bibtex
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@misc{
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title={
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author={tesslate},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/tesslate/
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}
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```
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##
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- **Creator:** [smirki](https://huggingface.co/tesslate)
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- **Repository & Demo**: Coming soon!
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- Tesslate/Tessa-T1-Dataset
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---
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"Landing Page"
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## **Model Overview**
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Tessa-T1 is an innovative transformer-based **React reasoning model**, fine-tuned from the powerful **Qwen2.5-Coder-14B-Instruct** base model. Designed specifically for React frontend development, Tessa-T1 leverages advanced reasoning to autonomously generate well-structured, semantic React components. Its integration into agent systems makes it a powerful tool for automating web interface development and frontend code intelligence.
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## **Model Highlights**
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- **React-specific Reasoning**: Accurately generates functional and semantic React components.
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- **Agent Integration**: Seamlessly fits into AI-driven coding agents and autonomous frontend systems.
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- **Context-Aware Generation**: Effectively understands and utilizes UI context to provide relevant code solutions.
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---
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## **Example Outputs**
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*See examples demonstrating the powerful reasoning and component creation capabilities of Tessa-T1:*
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## **Use Cases**
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### **Recommended Uses**
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- **Automatic Component Generation**: Quickly produce React components from textual prompts.
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- **Agent-based Web Development**: Integrate into automated coding systems for faster frontend workflows.
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- **Frontend Refactoring**: Automate the optimization and semantic enhancement of React code.
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### **Limitations**
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- **Focused on React**: Limited use outside React.js frameworks.
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- **Complex State Management**: May require manual adjustments for highly dynamic state management scenarios.
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---
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## **How to Use**
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### **Inference Example**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "smirki/Tessa-T1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
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---
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## **Performance and Evaluation**
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- **Strengths**:
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- Strong semantic React component generation.
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---
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## **Technical Specifications**
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- **Architecture**: Transformer-based LLM
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- **Base Model**: Qwen2.5-Coder-14B-Instruct
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## **Citation**
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```bibtex
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@misc{smirki_Tessa-T1,
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title={Tessa-T1: React-Focused Reasoning Model for Component Generation},
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author={tesslate},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/tesslate/Tessa-T1}
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}
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```
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---
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## **Contact & Community**
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- **Creator:** [smirki](https://huggingface.co/tesslate)
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- **Repository & Demo**: Coming soon!
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