Instructions to use alxxtexxr/IndoWebGen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alxxtexxr/IndoWebGen-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="alxxtexxr/IndoWebGen-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alxxtexxr/IndoWebGen-7B")
model = AutoModelForCausalLM.from_pretrained("alxxtexxr/IndoWebGen-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alxxtexxr/IndoWebGen-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alxxtexxr/IndoWebGen-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alxxtexxr/IndoWebGen-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alxxtexxr/IndoWebGen-7B

SGLang

How to use alxxtexxr/IndoWebGen-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "alxxtexxr/IndoWebGen-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alxxtexxr/IndoWebGen-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "alxxtexxr/IndoWebGen-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alxxtexxr/IndoWebGen-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alxxtexxr/IndoWebGen-7B with Docker Model Runner:
```
docker model run hf.co/alxxtexxr/IndoWebGen-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🇮🇩🌐🤖 IndoWebGen: LLM for Automated Website Generation Based on Indonesian Instructions

Hugely inspired by Web App Factory.

Model Description:

Base Model: codellama/CodeLlama-7b-hf [1]
Finetuning Method: LoRA [2]
Dataset: 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 running the inference code with the provided Google Colab notebook here. 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.

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

F32

Model tree for alxxtexxr/IndoWebGen-7B

Quantizations

1 model

Dataset used to train alxxtexxr/IndoWebGen-7B

Papers for alxxtexxr/IndoWebGen-7B

Code Llama: Open Foundation Models for Code

Paper • 2308.12950 • Published Aug 24, 2023 • 29

LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 61