---
base_model: ismaprasetiyadi/Biawak-8B-Base
library_name: peft
pipeline_tag: text-generation
language:
- id
tags:
- base_model:adapter:ismaprasetiyadi/Biawak-8B-Base
- lora
- sft
- transformers
- trl
- unsloth
- biawak
- indonesian
- instruction-following
license: apache-2.0
datasets:
- dtp-fine-tuning/dtp-singleturn-AGQ-9k
---

# Model Card for SFT-Biawak-8B-AGQ-9k-Unsloth
## Model Details

### Model Description

This model is a fine-tuned version of **[ismaprasetiyadi/Biawak-8B-Base](https://huggingface.co/ismaprasetiyadi/Biawak-8B-Base)**. It was trained using **Unsloth** and **LoRA** (Low-Rank Adaptation) on the **[dtp-singleturn-AGQ-9k](https://huggingface.co/datasets/dtp-fine-tuning/dtp-singleturn-AGQ-9k)** dataset.

The model is specifically optimized for **Indonesian single-turn instruction following**, utilizing the Qwen3 chat template structure. It leverages 4-bit quantization for memory efficiency during training and inference.

- **Developed by:** DTP2 Team
- **Model type:** Causal Language Model (LoRA Adapter)
- **Language(s) (NLP):** Indonesian (id)
- **License:** Apache-2.0
- **Finetuned from model:** [ismaprasetiyadi/Biawak-8B-Base](https://huggingface.co/ismaprasetiyadi/Biawak-8B-Base)

### Model Sources

- **Repository:** [dtp-fine-tuning/DTP_AGQ_Question_Diploy_9K](https://huggingface.co/dtp-fine-tuning/DTP_AGQ_Question_Diploy_9K)
- **Dataset:** [dtp-fine-tuning/dtp-singleturn-AGQ-9k](https://huggingface.co/datasets/dtp-fine-tuning/dtp-singleturn-AGQ-9k)
- **Training Logs:** [View full W&B Report](https://api.wandb.ai/links/DTP2/zh541s0e)

## Uses

### Direct Use

The model is designed for Indonesian chat and instruction-following tasks. It performs best in single-turn question-answering scenarios involving general knowledge, reasoning, and cultural context provided by the AGQ dataset.

### Out-of-Scope Use

- **Long-context conversations:** The model was fine-tuned on single-turn data; multi-turn performance may be inconsistent.
- **High-stakes decision making:** As an 8B model, it may hallucinate facts and should not be used for medical or legal advice without verification.

## Bias, Risks, and Limitations

This model inherits the biases present in the base `Biawak-8B` model and the `AGQ-9k` dataset. While fine-tuning improves instruction adherence, users should be aware that the model can still generate plausible-sounding but incorrect information.

### Recommendations

Users should verify important information generated by the model. It is recommended to use the `qwen3` chat template for optimal performance.

## How to Get Started with the Model

Use the code below to load the model and run inference:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# 1. Load Base Model
base_model_name = "ismaprasetiyadi/Biawak-8B-Base"
adapter_model_name = "YOUR_USERNAME/SFT-Biawak-8B-AGQ-9k-Unsloth"

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(adapter_model_name, trust_remote_code=True)

# 2. Load Adapter
model = PeftModel.from_pretrained(model, adapter_model_name)

# 3. Inference
messages = [
    {"role": "user", "content": "Jelaskan sejarah singkat kemerdekaan Indonesia."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data

The model was trained on dtp-fine-tuning/dtp-singleturn-AGQ-9k.
- Size: ~9k examples
- Content: Indonesian general questions and instructions (Single Turn).
- Split: Train (90%) / Test (10%)

### Training Procedure

The model was fine-tuned using the Unsloth library, which provides 2x faster training and ~60% less memory usage compared to standard Hugging Face implementations.


#### Training Hyperparameters
- **Training regime**: bf16 mixed precision (via Unsloth/LoRA)
- **Quantization**: 4-bit (nf4)
- **LoRA Rank**: 16
- **LoRA Alpha**: 32
- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Batch Size**: 8 per device (Effective batch size: 32 via Gradient Accumulation)
- **Learning Rate**: 2e-5 (Linear Schedule with 0.05 warmup)
- **Epochs**: 2
- **Max Sequence Length**: 8192
- **Optimizer**: adamw_8bit
#### Speeds, Sizes, Times [optional]

- **Hardware**: 1x NVIDIA A100 80GB
- **Training Duration**: ~10 hours
- **GPU Memory Usage**: Peaked at ~45GB (56% utilization)

## Evaluation
### Results

The model demonstrated stable convergence over 2 epochs.
* **Final Training Loss:** ~0.70
* **Final Validation Loss:** ~0.67
* **Observation:** The validation loss consistently decreased alongside training loss, indicating no overfitting occurred during the training process.

**[View the full training run plots and metrics on Weights & Biases](https://api.wandb.ai/links/DTP2/zh541s0e)**

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type**: NVIDIA A100 80GB
- **Hours used**: 10 hours
- **Cloud Provider**: University Server / Private Infrastructure
- **Compute Region**: Indonesia

### Framework versions
- Unsloth 2024.x
- Transformers 4.x
- Pytorch 2.x
- Datasets 2.x
- Tokenizers 0.x