---
language:
  - id
  - en
tags:
  - gemma4
  - aec
  - construction
  - fine-tuned
base_model: google/gemma-4-E2B-it
library_name: peft
---

# 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

```python
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