metadata
license: apache-2.0
base_model: unsloth/Qwen3-1.7B
tags:
- gasing
- indonesian
- mathematics
- curriculum-learning
- qwen
language:
- id
pipeline_tag: text-generation
GASING Qwen3 1.7B - Curriculum Learning
This model was trained using curriculum learning on the GASING dataset for Indonesian mathematical reasoning.
Model Details
- Base Model: unsloth/Qwen3-1.7B
- Training Method: Curriculum Learning (6 epochs with progressive difficulty)
- Dataset: GASING (Indonesian mathematical problems)
- Fine-tuning: LoRA (r=8, alpha=32) → Merged to full weights
Training Results
- Best Training Loss: 0.0026 (Epoch 6)
- Training Strategy: Progressive difficulty curriculum
Curriculum Schedule
Epoch | Easy | Medium | Hard |
---|---|---|---|
1 | 5% | 0% | 0% |
2 | 30% | 65% | 5% |
3 | 10% | 80% | 10% |
4 | 5% | 80% | 15% |
5 | 5% | 75% | 20% |
6 | 5% | 70% | 25% |
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"Cbgcbg/gasing-qwen3-1.7b-curriculum-v1",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"Cbgcbg/gasing-qwen3-1.7b-curriculum-v1",
trust_remote_code=True
)
# Example usage
question = "Bagaimana cara mencari panjang sisi segitiga jika diketahui sudut alpha dan sisi miringnya 1?"
messages = [
{"role": "system", "content": "Mulai sekarang anda adalah AI Asisten bernama 'GASING'..."},
{"role": "user", "content": question}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.7,
do_sample=True
)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
Training Configuration
- Learning Rate: 0.0001
- Batch Size: 16
- Gradient Accumulation: 8
- LoRA r: 8
- LoRA alpha: 32
- Max Sequence Length: 8192
Created by Institut Teknologi Del (IT Del)