Update README.md

README.md

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 ---
-base_model: unsloth/Qwen2.5-Coder-14B-Instruct
+base_model: Qwen/Qwen2.5-Coder-14B-Instruct
 tags:
 - text-generation-inference
 - transformers
-- unsloth
 - 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!
+