metadata
license: mit
tags:
- jekyll
- static-site
- code-generation
- ruby
- liquid
- web-development
base_model: deepseek-ai/deepseek-coder-1.3b-instruct
datasets:
- daffaaditya/jekyll-master-dataset
language:
- en
- id
🎯 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]))