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---
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- text-generation-inference
- transformers
- qwen2
- trl
license: apache-2.0
language:
- en
datasets:
- Tesslate/Tessa-T1-Dataset
---
"Landing Page"
## **Model Overview**
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.
---
## **Model Highlights**
- **React-specific Reasoning**: Accurately generates functional and semantic React components.
- **Agent Integration**: Seamlessly fits into AI-driven coding agents and autonomous frontend systems.
- **Context-Aware Generation**: Effectively understands and utilizes UI context to provide relevant code solutions.
---
## **Example Outputs**
*See examples demonstrating the powerful reasoning and component creation capabilities of Tessa-T1:*
AI upload
Virtual Machine Console
Playlist Management
Prompt: "add in a calendar"
---
## **Use Cases**
### **Recommended Uses**
- **Automatic Component Generation**: Quickly produce React components from textual prompts.
- **Agent-based Web Development**: Integrate into automated coding systems for faster frontend workflows.
- **Frontend Refactoring**: Automate the optimization and semantic enhancement of React code.
### **Limitations**
- **Focused on React**: Limited use outside React.js frameworks.
- **Complex State Management**: May require manual adjustments for highly dynamic state management scenarios.
---
## **How to Use**
### **Inference Example**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "smirki/Tessa-T1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
prompt = """<|im_start|>user
Create a React component for a user profile card.<|im_end|>
<|im_start|>assistant
<|im_start|>think
"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1500, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## **Performance and Evaluation**
- **Strengths**:
- Strong semantic React component generation.
- Excellent integration capabilities with agent-based systems.
- **Weaknesses**:
- Complex JavaScript logic may require manual post-processing.
---
## **Technical Specifications**
- **Architecture**: Transformer-based LLM
- **Base Model**: Qwen2.5-Coder-14B-Instruct
- **Precision**: bf16 mixed precision, quantized to q8
- **Hardware Requirements**: Recommended 12GB VRAM
- **Software Dependencies**:
- Hugging Face Transformers
- PyTorch
---
## **Citation**
```bibtex
@misc{smirki_Tessa-T1,
title={Tessa-T1: React-Focused Reasoning Model for Component Generation},
author={tesslate},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/tesslate/Tessa-T1}
}
```
---
## **Contact & Community**
- **Creator:** [smirki](https://huggingface.co/tesslate)
- **Repository & Demo**: Coming soon!
**Sponsored by vichar ai [Huggingface](https://huggingface.co/vicharai) [Website](https://vichar.io)** |