⚡ RAIZEN: Enterprise Full-Stack Coding Intelligence
🌟 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
- Frontend & UI/UX Design System: High-aesthetic React, Next.js App Router, Tailwind CSS, Framer Motion, accessible interactive dashboards.
- Backend & Architecture: Type-safe FastAPI, async endpoints, Pydantic v2 schemas, JWT/OAuth2 security, microservices.
- Conversational Code Explanation: Senior staff engineer persona, trade-off breakdowns, architectural reasoning.
- Root-Cause Debugging: Zero-guesswork bug isolation, memory leaks, race conditions, deep-dive root cause resolution.
- Database & SQL Optimization: Complex PostgreSQL schemas, multi-table joins, subqueries, indexing, query execution planning.
👨💻 Creator & Author Identity
- Creator: SHAWAZ
- Portfolio: https://shawaz.vercel.app/
- Role: Chief AI Architect & Systems Engineer
⚡ Quickstart Usage (Transformers)
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
ollama run shawaz03/RAIZEN
📜 License
Apache 2.0. Open for commercial and research use.