riosst/gemma4-aec

Gemma 4 AEC - Fine-tuned Model

Model ini di-fine-tune dari google/gemma-4-E2B-it menggunakan dataset AEC (Architecture, Engineering, Construction) Indonesia.

Dataset

• Sumber: riosst/gemma4-aec-dataset
• Jumlah contoh: 10
• Bahasa: Indonesia
• Domain: Konstruksi, SNI, Peraturan PUPR

Cara Menggunakan

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("riosst/gemma4-aec")
tokenizer = AutoTokenizer.from_pretrained("riosst/gemma4-aec")

# Generate
inputs = tokenizer("Apa itu SNI untuk beton?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Fine-tuning Details

• Method: LoRA (QLoRA 4-bit)
• Base Model: google/gemma-4-E2B-it
• Epochs: 3
• Learning Rate: 2e-4
• LoRA Rank: 16