Update README.md

README.md

@@ -17,4 +17,69 @@ datasets:
 - smirki/UI_REASONING_v1.01
 - smirki/Parkytest
 ---
-![zdbfcbdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/75Ic8lgquvpGjSLJnm8V7.png)
+![zdbfcbdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/75Ic8lgquvpGjSLJnm8V7.png)
+
+# **Viper-OneCoder-UIGEN**  
+
+Viper-OneCoder-UIGEN is based on the Qwen 2.5 14B modality architecture, designed to be the **best** for web development and structured coding logic. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its **step-by-step logic breakdown** and **front-end problem-solving** abilities. The model demonstrates significant improvements in **context understanding, structured UI development, and long-context comprehension**, making it ideal for **web-based coding tasks, HTML/CSS/Tailwind development, and detailed instruction following**.  
+
+### **Key Improvements**  
+1. **Best-in-Class Web Development Proficiency**: Advanced understanding of **HTML, CSS, Tailwind, JavaScript**, and front-end frameworks.  
+2. **Fine-Tuned Step-by-Step Logic Breakdown**: Optimized for structured explanations, component-based UI coding, and logic-driven development.  
+3. **Advanced Instruction Following**: Delivers precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (**8K+ tokens**).  
+4. **Long-Context Mastery**: Handles up to **128K tokens** with an output capability of **8K tokens** per response.  
+5. **Multilingual Code Support**: Excels in **HTML, CSS, JavaScript, React, Tailwind CSS, Python**, and other major web-related languages, with documentation in **29+ languages**.  
+
+### **Quickstart with Transformers**  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Viper-OneCoder-UIGEN"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Create a responsive navigation bar using Tailwind CSS."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level UI coding and reasoning abilities."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+### **Intended Use**  
+- **Elite Web Development & UI Design**: Best-in-class model for writing, analyzing, and optimizing front-end code.  
+- **Step-by-Step Coding Logic Breakdown**: Guides developers through structured programming approaches and best practices.  
+- **Component-Based UI Development**: Generates reusable Tailwind and React components with clear explanations.  
+- **Structured Data Processing**: Handles JSON, XML, and structured UI component automation.  
+- **Multilingual Programming Support**: Proficient in HTML, CSS, Tailwind, JavaScript, React, Python, and Go.  
+- **Extended Technical Content Generation**: Ideal for writing documentation, blog posts, and front-end tutorials.  
+
+### **Limitations**  
+1. **High Computational Demand**: Requires powerful GPUs/TPUs for smooth inference due to **14B parameters**.  
+2. **Framework-Specific Variability**: Performance may vary across different front-end frameworks.  
+3. **Possible Error Propagation**: Extended text outputs might introduce logical inconsistencies.  
+4. **Limited Real-World Awareness**: The model does not have access to real-time internet updates.  
+5. **Prompt Sensitivity**: Performance depends on how well the prompt is structured.