---
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
---

# 🚀 **Model Card for Tess-T1**

---

## 🌟 **Model Overview**

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/GfOobqW0RbMdS0pV0y4JM.png)
"Landing Page"

Tess-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, Tess-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 Tess-T1:*


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/54P6h76jEN6jLz7iMO78V.png)

Make a functioning AI training waitlist

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zU3yln3xGdUIywGRtxSij.png)

Prompt: "add in a calendar"

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/eDpCj-eV3DmDjkDdB1Ux1.png)

---

## 🛠️ **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/Tess-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_Tess-T1,
  title={Tess-T1: React-Focused Reasoning Model for Component Generation},
  author={tesslate},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/tesslate/Tess-T1}
}
```

---

## 🤝 **Contact & Community**
- **Creator:** [smirki](https://huggingface.co/tesslate)
- **Repository & Demo**: Coming soon!