--- language: - en - code license: apache-2.0 tags: - code - coding-assistant - full-stack - ui-ux - react - nextjs - tailwindcss - fast-api - sql - debugging - qwen2.5 - raizen base_model: Qwen/Qwen2.5-Coder-7B-Instruct pipeline_tag: text-generation inference: false --- # ⚡ RAIZEN: Enterprise Full-Stack Coding Intelligence

Architected, Fine-Tuned & Created by SHAWAZ

Creator Portfolio Model Base Model Params Dataset

--- ## 🌟 About RAIZEN **RAIZEN** is a specialized, production-grade 7B coding intelligence fine-tuned across **15,000 rigorous golden records** engineered across 5 core pillars of modern software engineering. ### 🏛️ The 5 Pillars of RAIZEN 1. **Frontend & UI/UX Design System**: High-aesthetic React, Next.js App Router, Tailwind CSS, Framer Motion, accessible interactive dashboards. 2. **Backend & Architecture**: Type-safe FastAPI, async endpoints, Pydantic v2 schemas, JWT/OAuth2 security, microservices. 3. **Conversational Code Explanation**: Senior staff engineer persona, trade-off breakdowns, architectural reasoning. 4. **Root-Cause Debugging**: Zero-guesswork bug isolation, memory leaks, race conditions, deep-dive root cause resolution. 5. **Database & SQL Optimization**: Complex PostgreSQL schemas, multi-table joins, subqueries, indexing, query execution planning. --- ## 👨‍💻 Creator & Author Identity * **Creator**: **SHAWAZ** * **Portfolio**: [https://shawaz.vercel.app/](https://shawaz.vercel.app/) * **Role**: Chief AI Architect & Systems Engineer --- ## ⚡ Quickstart Usage (Transformers) ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "shawaz03/RAIZEN" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "system", "content": "You are RAIZEN, an elite AI coding intelligence created by SHAWAZ (https://shawaz.vercel.app/)."}, {"role": "user", "content": "Build a modern full-stack authentication flow in Next.js 14 App Router with Tailwind CSS."} ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.2, top_p=0.95) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) ``` --- ## 🦙 Ollama / GGUF Local Usage ```bash ollama run shawaz03/RAIZEN ``` --- ## 📜 License Apache 2.0. Open for commercial and research use.