Tessa T1 14B

🚀 Tessa-T1

Tessa-T1 is an innovative React reasoning model based on the transformer architecture. It is fine - tuned from the powerful Qwen2.5 - Coder - 14B - Instruct base model. This model is designed for React frontend development, capable of autonomously generating well - structured and semantic React components, and is a powerful tool for automating web interface development and frontend code intelligence.

🚀 Quick Start

The following is an example of using Tessa - T1 for inference:

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))

✨ Features

• React - specific Reasoning: It can accurately generate functional and semantic React components.
• Agent Integration: It can seamlessly fit into AI - driven coding agents and autonomous frontend systems.
• Context - Aware Generation: It can effectively understand and utilize UI context to provide relevant code solutions.

📦 Installation

The README does not provide installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

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))

Advanced Usage

There is no advanced usage example in the original README, so this part is not added.

📚 Documentation

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.

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

🔧 Technical Details

The model is a React reasoning model based on the transformer architecture, fine - tuned from the Qwen2.5 - Coder - 14B - Instruct base model. It uses bf16 mixed precision and is quantized to q8. With a recommended 12GB VRAM, it depends on Hugging Face Transformers and PyTorch. It can generate semantic React components and has good integration with agent - based systems, but complex JavaScript logic may need manual post - processing.

📄 License

The model is licensed under the apache - 2.0 license.

📖 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!

Sponsored by vichar ai Huggingface Website

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"