bhismaperkasa/form-generator-lora-adapters

Form Generator - Gemma 3 270M (LoRA Adapters)

Fine-tuned LoRA adapters for google/gemma-3-270m-it to generate form definitions in JSON format from natural language descriptions in Bahasa Indonesia.

🎯 Model Description

This repository contains LoRA adapters (not a merged model) that can be loaded on top of the base Gemma 3 270M model. The adapters were trained to generate dynamic form definitions in JSON format.

⚠️ Important Note

This is a LoRA adapter model, NOT a standalone model. You need to load it together with the base model google/gemma-3-270m-it.

🚀 Usage

Installation

pip install transformers peft torch

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-3-270m-it",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "bhismaperkasa/form-generator-lora-adapters")
tokenizer = AutoTokenizer.from_pretrained("bhismaperkasa/form-generator-lora-adapters")

# Generate
messages = [
    {"role": "system", "content": "You are a helpful assistant that generates form definitions in JSON format based on user requests."},
    {"role": "user", "content": "buatkan form pendaftaran event dengan nama dan email"}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated = result.split("<start_of_turn>model\n")[-1].strip()
print(generated)

📊 Training Details

• Base Model: google/gemma-3-270m-it
• Method: QLoRA (4-bit quantization + LoRA)
• Dataset: bhismaperkasa/form_dinamis (10,000 samples)
• Training Samples: ~9,000
• Validation Samples: ~1,000
• Epochs: 3
• Final Eval Loss: 0.2256
• Token Accuracy: 93.5%

Hyperparameters

• LoRA Rank: 16
• LoRA Alpha: 32
• LoRA Dropout: 0.05
• Learning Rate: 5e-5
• Batch Size: 4
• Max Length: 512 tokens

📈 Performance

• Evaluation Loss: 0.2256
• Token Accuracy: 93.50%
• Train-Eval Gap: 4.9% (healthy, no overfitting)
• Entropy: 0.1881 (high confidence)

💡 Example Outputs

Input:

buatkan form login sederhana

Output:

{
  "id": "form_login_sims",
  "title": "Form Login",
  "description": "Form untuk login akun",
  "formDefinition": {
    "fields": [
      {"fieldId": "email", "label": "Email", "fieldType": "EMAIL", "required": true},
      {"fieldId": "password", "label": "Password", "fieldType": "PASSWORD", "required": true}
    ]
  }
}

🎓 Use Cases

• Dynamic form generation for web applications
• Survey and questionnaire creation
• User registration forms
• Data collection forms
• Event registration forms

⚙️ Technical Details

Why LoRA Adapters?

• Smaller size: ~50MB vs ~500MB for merged model
• Better quality: Works more reliably than merged model
• Flexibility: Can be combined with different base models
• Efficiency: Faster to download and deploy

Model Architecture

• Base: Gemma 3 270M (270 million parameters)
• Adapters: LoRA with rank 16 (few million trainable parameters)
• Target modules: All linear layers
• Quantization: 4-bit NormalFloat (NF4)

📝 Citation

@misc{form-generator-gemma-lora,
  author = {bhismaperkasa},
  title = {Form Generator - Gemma 3 270M LoRA Adapters},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/bhismaperkasa/form-generator-lora-adapters}}
}

📄 License

This model is based on Gemma 3 270M which is licensed under Apache 2.0.

🙏 Acknowledgments

• Google for Gemma 3 270M model
• Hugging Face for transformers, PEFT, and TRL libraries
• Dataset: bhismaperkasa/form_dinamis

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Note: For production use, consider using these adapters instead of a merged model for better reliability and performance.