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syaher/web-builder-dataset

Web Builder Dataset — Fine-tuning untuk LLM Dataset untuk fine-tune model pada pembangunan web builder (WeWeb clone) menggunakan Nuxt 4 + Tailwind CSS 4 + Cloudflare. 📊 Dataset Overview Metric Value Total examples 500 Train 400 (80%) Eval 100 (20%) Format ChatML JSONL Language UI: Bahasa Malaysia, Code: English 🎯 Fokus Dataset Kategori Bilangan Contoh Nuxt 4 Pages & Config ~50 API Handlers (Drizzle) ~80… See the full description on the dataset page: https://huggingface.co/datasets/syaher/web-builder-dataset.

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Web Builder Dataset — Fine-tuning untuk LLM

Dataset untuk fine-tune model pada pembangunan web builder (WeWeb clone) menggunakan Nuxt 4 + Tailwind CSS 4 + Cloudflare.

📊 Dataset Overview

MetricValue
Total examples500
Train400 (80%)
Eval100 (20%)
FormatChatML JSONL
LanguageUI: Bahasa Malaysia, Code: English

🎯 Fokus Dataset

KategoriBilangan Contoh
Nuxt 4 Pages & Config~50
API Handlers (Drizzle)~80
Builder Components (Blocks)~120
Auth & Middleware~40
Dashboard & Admin~60
Payment & Billing~40
Landing Pages~60
Utilities & Composables~50

🏗️ Stack yang Dijarab

  • —Framework: Nuxt 4.5.2
  • —CSS: Tailwind CSS 4
  • —Database: Drizzle ORM (SQLite/Turso)
  • —Cloud: Cloudflare (Pages, Workers, R2)
  • —Payment: ToyyibPay
  • —Drag & Drop: @dnd-kit
  • —Icons: @nuxt/icon (Iconify)

📁 Struktur Fail

dataset/
├── train.jsonl     # 400 contoh latihan
├── eval.jsonl      # 100 contoh evaluation
└── README.md       # dokumen ini

🔧 Cara Penggunaan

1. LLaMA-Factory

bash
# Clone framework
git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory

# Upload dataset
scp dataset/train.jsonl root@<instance>:/root/LLaMA-Factory/data/web_builder.jsonl

# Edit data_config.yaml
# - dataset_name: web_builder
# - file_name: web_builder.jsonl

# Train (QLoRA 4-bit)
llamafactory-cli train \
  --model_name_or_path Qwen/Qwen2.5-7B-Instruct \
  --dataset web_builder \
  --finetuning_type lora \
  --quantization_bit 4 \
  --template chatml \
  --output_dir ./output/web_builder

2. Unsloth (Faster training)

bash
# Install unsloth
pip install unsloth

# Run training
python train.py \
  --model Qwen/Qwen2.5-7B-Instruct \
  --dataset data/web_builder.jsonl \
  --max_seq_length 2048 \
  --per_device_train_batch_size 4 \
  --learning_rate 2e-4 \
  --num_train_epochs 3 \
  --lora_r 16 \
  --lora_alpha 32

3. Axolotl

yaml
# config.yaml
model: Qwen/Qwen2.5-7B-Instruct
dataset: web_builder
dataset_format: chatml
sequence_len: 2048

lora:
  r: 16
  alpha: 32
  dropout: 0.05
  target_modules: all

train:
  num_epochs: 3
  batch_size: 4
  learning_rate: 2e-4

📝 Format Data

Setiap baris adalah JSON object dengan messages array:

json
{
  "messages": [
    {"role": "system", "content": "Kau developer Nuxt 4 + Tailwind CSS 4 + Cloudflare..."},
    {"role": "user", "content": "Buat API handler untuk list pages"},
    {"role": "assistant", "content": "export default defineEventHandler..."}
  ]
}

System Prompt (Fixed)

Kau developer Nuxt 4 + Tailwind CSS 4 + Cloudflare. UI strings Bahasa Malaysia, kod English. Monorepo pnpm workspace.

⚙️ Training Hyperparameters (Cadangan)

ParameterValue
Model baseQwen2.5-7B-Instruct
Max sequence2048
Batch size4
Learning rate2e-4
Epochs3
LoRA r16
LoRA alpha32
Quantization4-bit

🔍 Evaluation

Run inference pada eval.jsonl untuk test model:

bash
llamafactory-cli inference \
  --model_name_or_path ./output/web_builder \
  --template chatml \
  --messages "Buat API handler untuk create page"

📦 Download Dataset

bash
# Clone
git clone https://github.com/your-repo/web-builder-dataset.git
cd web-builder-dataset

# Verify
wc -l dataset/train.jsonl  # Should be 400
wc -l dataset/eval.jsonl   # Should be 100

🏷️ Tags & Categories

  • —nuxt4
  • —tailwind4
  • —cloudflare
  • —drizzle
  • —web-builder
  • —landing-page
  • —drag-drop
  • —saas
  • —malay-ui

📜 License

MIT — Free untuk penggunaan komersial dan bukan-komersial.

Dataset dijana secara automatik berdasarkan pattern pembangunan web builder. Semua kod adalah contoh training, verify sebelum penggunaan production.