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---
license: apache-2.0
datasets:
- alxxtexxr/indowebgen-dataset
language:
- id
---
# 🇮🇩🌐🤖 IndoWebGen: LLM for Automated (Bootstrap-Based) Website Generation Based-On Indonesian Instructions
Hugely inspired by [Web App Factory](https://huggingface.co/spaces/jbilcke-hf/webapp-factory-wizardcoder).
## Model Description:
- Base Model: [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) [[1](https://arxiv.org/abs/2308.12950)]
- Finetuning Method: LoRA [[2](https://arxiv.org/abs/2106.09685)]
- Dataset: [alxxtexxr/indowebgen-dataset](https://huggingface.co/datasets/alxxtexxr/indowebgen-dataset)
## Finetuning Hyperparameters:
- Number of Epochs: 20
- Microbatch Size: 4
- Gradient Accumulation Step: 8
- LoRA Rank: 16
- LoRA Alpha: 32
- LoRA Target Modules: [q_proj, v_proj]
## Inference:
Try the inference demo [here](https://indowebgen.alimtegar.my.id) or try running the inference code with the provided Google Colab notebook [here](https://colab.research.google.com/drive/1pqqLGcgRcUTBLCNeF0V6REi7INJ43IZb?usp=sharing). The inference code used is shown below:
```
# Install the required libraries
!pip install transformers bitsandbytes accelerate
# Import the neccessary modules
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and the tokenizer
model_id = 'alxxtexxr/indowebgen-7b'
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
# load_in_4bit=True, # for low memory
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Initialize the prompt
prompt_template = '''Berikut adalah instruksi pembuatan website beserta output-nya yang berupa kode HTML dari website yang dibuat:
### Instruksi:
{instruction}
### Output:
<!DOCTYPE html>
<html lang="id">'''
# INSERT YOUR OWN INDONESIAN INSTRUCTION BELOW
instruction = 'Buatlah website portfolio untuk Budi'
prompt = prompt_template.format(instruction=instruction)
# Generate the output
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=2400,
do_sample=True,
temperature=1.0,
top_k=3,
top_p=0.8,
repetition_penalty=1.1,
pad_token_id=tokenizer.unk_token_id,
)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```
## Limitations
- The dataset used in training is limited to only 500 data, so the model performance may still not be optimal.
- The model is designed to generate single-page static websites, constructed using HTML with internal CSS.
- The content of the generated websites is dummy (including the images), so the users need to further customize the websites.
- The generated websites leverage Bootstrap for the styling, Font Awesome for the icons, and dummyimage.com images for the dummy images.
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