"Landing Page"
Model Overview
Tessa-T1 is an innovative transformer-based React reasoning model, fine-tuned from the powerful Qwen2.5-Coder-7B-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
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-7B-Instruct
- Precision: bf16 mixed precision, quantized to q8
- Hardware Requirements: Recommended 12GB VRAM
- Software Dependencies:
- Hugging Face Transformers
- PyTorch
Citation
@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
- Repository & Demo: Coming soon!