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--- |
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base_model: Qwen/Qwen2.5-Coder-14B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- qwen2 |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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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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## **Example Outputs** |
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*See examples demonstrating the powerful reasoning and component creation capabilities of Tessa-T1:* |
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AI upload |
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Virtual Machine Console |
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Playlist Management |
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Prompt: "add in a calendar" |
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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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## **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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prompt = """<|im_start|>user |
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Create a React component for a user profile card.<|im_end|> |
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<|im_start|>assistant |
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<|im_start|>think |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=1500, do_sample=True, temperature=0.7) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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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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- Excellent integration capabilities with agent-based systems. |
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- **Weaknesses**: |
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- Complex JavaScript logic may require manual post-processing. |
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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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- **Precision**: bf16 mixed precision, quantized to q8 |
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- **Hardware Requirements**: Recommended 12GB VRAM |
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- **Software Dependencies**: |
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- Hugging Face Transformers |
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- PyTorch |
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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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**Sponsored by vichar ai [Huggingface](https://huggingface.co/vicharai) [Website](https://vichar.io)** |
