---
language:
- en
license: apache-2.0
tags:
- code
- code-generation
- full-stack
- react
- nextjs
- typescript
- tailwindcss
- prisma
- zustand
- qwen2.5-coder
- vibe-coding
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
---

# 🚀 VIBE CODER v2.0 MAX (7B)

<div align="center">

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/shawaz03/LLM/blob/main/vibe_coder_quickstart.ipynb)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Base Model](https://img.shields.io/badge/Base%20Model-Qwen2.5--Coder--7B--Instruct-purple.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
[![Model Size](https://img.shields.io/badge/Parameters-7.61B-green.svg)]()
[![Precision](https://img.shields.io/badge/Weights-FP16%20Safetensors-orange.svg)]()

**The Autonomous Full-Stack AI Software Engineer & Modern UI/UX Designer.**  
*Zero Placeholders. Modern Anti-AI Aesthetics. Production-Grade TypeScript & Next.js Architecture.*

</div>

---

## ⚡ Quickstart on Google Colab (1-Click Run)

Run Vibe Coder on a **Free Google Colab T4 GPU** with zero memory warnings:

👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/shawaz03/LLM/blob/main/vibe_coder_quickstart.ipynb)

---

## 📌 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common LLM coding pitfalls—such as lazy placeholder comments (`// TODO: implement logic`), broken imports, and outdated visual tropes.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)

```python
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)

```bash
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**.