130 lines
4.0 KiB
Markdown
130 lines
4.0 KiB
Markdown
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---
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base_model: Qwen/Qwen2.5-Coder-3B-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-3B-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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---
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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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---
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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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---
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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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---
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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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---
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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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---
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## **Technical Specifications**
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- **Architecture**: Transformer-based LLM
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- **Base Model**: Qwen2.5-Coder-3B-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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---
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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)**
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