daffaaditya/jekyll-master-ai

🎯 Jekyll Master AI

Fine-tuned DeepSeek-Coder model specialized in Jekyll static site generator.

Model Description

This model is fine-tuned from DeepSeek-Coder-1.3B to become an expert in Jekyll, a static site generator written in Ruby.

Specializations:

• Liquid templating language
• YAML configuration files (_config.yml)
• Jekyll plugins development
• Sass/SCSS styling
• GitHub Pages deployment
• SEO optimization

Training Data

The model was fine-tuned on 192 examples covering:

• Configuration files (15%)
• Layouts & templates (20%)
• Includes & components (15%)
• Plugins (10%)
• Sass/SCSS (15%)
• Liquid filters (10%)
• Deployment configs (10%)
• Front matter & Data files (5%)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = "deepseek-ai/deepseek-coder-1.3b-instruct"
model = AutoModelForCausalLM.from_pretrained(base_model)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load adapter
model = PeftModel.from_pretrained(model, "daffaaditya/jekyll-master-ai")

# Generate code
prompt = "Buat file _config.yml untuk blog teknologi"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500)
print(tokenizer.decode(outputs[0]))