README.md

metadata

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

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🌟 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.

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👨‍💻 Creator & Author Identity

• Creator: SHAWAZ
• Portfolio: https://shawaz.vercel.app/
• Role: Chief AI Architect & Systems Engineer

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⚡ 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))

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🦙 Ollama / GGUF Local Usage

ollama run shawaz03/RAIZEN

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📜 License

Apache 2.0. Open for commercial and research use.