vibe-coder-7b-max

🚀 VIBE CODER v2.0 MAX (7B)

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

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⚡ Quickstart on Google Colab (1-Click Run)

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

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eliminate common LLM coding pitfalls—such as lazy placeholder comments (// TODO: implement logic), broken imports, and outdated visual tropes.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

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📊 Dataset & Training Architecture

Vibe Coder was trained on a 64,000-record Master Dataset structured in strict ChatML format across 7 specialized pipelines:

⚡ 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%

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💻 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))

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2. High-Speed Production Serving (vLLM)

vllm serve shawaz03/vibe-coder-7b-max --port 8000 --dtype float16

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🛡️ License

This project is open-source and licensed under the Apache 2.0 License.