metadata
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
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