---
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

```python
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)