🚀 VIBE CODER v2.0 MAX (7B)
The Autonomous Full-Stack AI Software Engineer & Modern UI/UX Designer.
Zero Placeholders. Modern Anti-AI Aesthetics. Production-Grade TypeScript & Next.js Architecture.
⚡ Quickstart on Google Colab (1-Click Run)
Run Vibe Coder on a Free Google Colab T4 GPU with zero memory warnings:
👉
📌 Overview
Vibe Coder v2.0 MAX is a specialized, fine-tuned code generation model based on Qwen2.5-Coder-7B-Instruct. It is engineered specifically to eliminate common LLM coding pitfalls—such as lazy placeholder comments (// TODO: implement logic), broken imports, and outdated visual tropes.
🌟 Core Capabilities:
- 🛡️ Zero Placeholders Guaranteed: Generates complete, functional components, state hooks, and API routes with zero missing logic.
- 🎨 Modern Anti-AI Aesthetic Directives: Built-in design system rules that enforce dark neutral palettes (
bg-neutral-900,border-neutral-800), custom typography, responsive grid layouts, and Lucide React icons. - ⚡ Full-Stack Ecosystem Mastery: Native expertise in Next.js 15 App Router, React 19, TypeScript, Tailwind CSS, Zustand, Prisma ORM, Zod validation, and WebSockets.
- 🛠️ Self-Healing & Debugging: Diagnoses runtime hydration errors and type mismatches with exact root-cause explanations and drop-in code patches.
📊 Dataset & Training Architecture
Vibe Coder was trained on a 64,000-record Master Dataset structured in strict ChatML format across 7 specialized pipelines:
| Pipeline | Dataset Focus | Size |
|---|---|---|
| 1. Open-Source Repositories | Production Next.js server actions, Prisma schemas, Zustand stores | 25,000 records |
| 2. Handcrafted Vibe Templates | Complete Bento showcases, pricing matrices, checkout wizards, audio players | 12,000 records |
| 3. Multi-Turn Refinement | Multi-turn developer dialogues simulating feature additions and refactoring | 10,000 records |
| 4. Self-Healing & Debugging | Runtime errors, TypeScript compilation bugs, hydration fixes | 5,000 records |
| 5. Full-Stack Architectures | WebSocket chat rooms, Stripe webhook signature verifiers, Redis caching | 12,000 records |
⚡ Hyperparameters:
- Base Model:
Qwen/Qwen2.5-Coder-7B-Instruct - Method: 4-bit NF4 QLoRA $\rightarrow$ Full 16-bit FP16 Safetensors Merger
- LoRA Config: Rank $r = 64$, $\alpha = 128$,
rsLoRA = True(161.4M trainable parameters) - Attention Kernel: PyTorch SDPA (Scaled Dot-Product Flash Attention)
- Final Validation Loss:
0.035 – 0.045 - Token Accuracy:
98.5%
💻 Quick Start & Usage
1. Using Transformers in 4-bit (Google Colab / Low-VRAM GPUs)
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = "shawaz03/vibe-coder-7b-max"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True
)
system_prompt = """You are Vibe Coder, a world-class principal full-stack software engineer and UI/UX designer.
Write complete, modern, production-grade code in TypeScript, React, Next.js, and Node.js with ZERO placeholders."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "Build an interactive pricing matrix in React with Tailwind CSS, supporting monthly/annual toggle and feature checkmarks."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.2,
top_p=0.95,
repetition_penalty=1.05,
do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
2. High-Speed Production Serving (vLLM)
vllm serve shawaz03/vibe-coder-7b-max --port 8000 --dtype float16
🛡️ License
This project is open-source and licensed under the Apache 2.0 License.