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IzzulGod/
GPT2-Indo-chat-tuned

Name: GPT2-Indo-chat-tuned
Availability: InStock

safetensors

gpt2

FreedomIntelligence/evol-instruct-indonesian

cahya/gpt2-small-indonesian-522M

finetune

mit

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GPT2-Small Indonesian Instruct-Tuned Model

An Indonesian conversational AI model fine-tuned from GPT2-Small (124M Parameters) using instruction-following techniques to enable natural chat-like interactions in Bahasa Indonesia.

📋 Model Overview

This model transforms a base Indonesian GPT-2 text generator into a conversational chatbot capable of following instructions and engaging in question-answering dialogues. The model has been specifically optimized for Indonesian language understanding and generation.

Base Model: GPT2-Small (124M Parameters)
Fine-tuning Method: SFT-LoRA (Supervised Fine-Tuning with Low-Rank Adaptation)
Training Datasets:
- indonesian-nlp/wikipedia-id (knowledge base)
- FreedomIntelligence/evol-instruct-indonesian (instruction following)
- FreedomIntelligence/sharegpt-indonesian (conversational patterns)
Primary Language: Indonesian (Bahasa Indonesia)
Task: Conversational AI / Chat Completion
License: MIT

🧪 Project Background

This model was developed as part of a personal learning journey in AI and Large Language Models (LLMs). The entire training process was conducted on Google Colab's free tier using T4 GPU, demonstrating how to build effective Indonesian conversational AI with limited computational resources.

The project focuses on creating an accessible Indonesian language model that can understand context, follow instructions, and provide helpful responses in natural Bahasa Indonesia.

🚀 Quick Start

Installation

pip install transformers torch

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load model and tokenizer
model_path = "izzulgod/gpt2-indo-instruct-tuned"  
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path).to(device)

# Create prompt
prompt = "User: Siapa presiden pertama Indonesia?\nAI:"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=True,
        temperature=0.7,
        top_p=0.95,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.convert_tokens_to_ids("<|endoftext|>")
    )

# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🎯 Model Capabilities

The model demonstrates strong performance across several Indonesian language tasks:

Question Answering: Provides accurate responses to factual questions in Indonesian
Instruction Following: Capable of understanding and executing various instructions and tasks
Conversational Context: Maintains coherent context throughout chat-like interactions
Code Generation: Can generate simple code snippets (Python, R, etc.) with clear Indonesian explanations
Educational Content: Explains complex concepts in accessible Indonesian language
Cultural Awareness: Understands Indonesian cultural context and references

📊 Training Details

Dataset Composition

The model was trained on a carefully curated combination of datasets to balance knowledge, instruction-following, and conversational abilities:

Training Data Format:

json

[
  {
    "from": "human", 
    "value": "Question or instruction in Indonesian"
  },
  {
    "from": "gpt",
    "value": "Detailed and helpful response in Indonesian"
  }
]

Training Configuration

The model was fine-tuned using LoRA (Low-Rank Adaptation) technique, which allows efficient training while preserving the base model's capabilities:

LoRA Configuration:

Rank (r): 64 - Higher rank for better adaptation capacity
Alpha: 128 - Scaling factor for LoRA weights
Target Modules: ["c_attn", "c_proj", "mlp.c_fc", "mlp.c_proj"] - Key transformer components
Dropout: 0.05 - Regularization to prevent overfitting
Bias: "none" - Focus adaptation on weight matrices

Training Hyperparameters:

Epochs: 3 - Sufficient for convergence without overfitting
Batch Size: 16 per device - Optimized for T4 GPU memory
Gradient Accumulation: 2 steps - Effective batch size of 32
Learning Rate: 2e-4 - Conservative rate for stable training
Scheduler: Cosine annealing - Smooth learning rate decay
Weight Decay: 0.01 - L2 regularization
Mixed Precision: FP16 enabled - Memory and speed optimization

Training Progress

The model showed consistent improvement throughout training:

Training Progress (5535 total steps over 3 epochs):
Step	Training Loss
200	    3.533500  # Initial high loss
400	    2.964200  # Rapid initial improvement
...
4000	2.416200  # Stable convergence
...
5400	2.397500  # Final optimized loss

Final Metrics:
- Training Loss: 2.573
- Training Time: 3.5 hours
- Samples per Second: 14.049
- Total Training Samples: ~177k

The steady decrease from 3.53 to 2.39 demonstrates effective learning and adaptation to the Indonesian instruction-following task.

🔧 Advanced Usage

Generation Parameter Tuning

For Creative Responses:

python

outputs = model.generate(
    **inputs,
    max_new_tokens=256,      # Longer responses
    temperature=0.8,         # More randomness
    top_p=0.9,              # Diverse vocabulary
    repetition_penalty=1.2   # Avoid repetition
)

For Focused/Factual Responses:

python

outputs = model.generate(
    **inputs,
    max_new_tokens=128,      # Concise responses  
    temperature=0.6,         # More deterministic
    top_p=0.95,             # High-quality tokens
    repetition_penalty=1.1   # Mild repetition control
)

Prompt Engineering

Recommended Format:

User: [Your question or instruction in Indonesian]
AI: [Expected response starts here]

⚠️ Limitations and Considerations

Knowledge Limitations:

Training Data Cutoff: Knowledge is limited to the training datasets, primarily Wikipedia-based information
Factual Accuracy: Generated responses may not always be factually accurate or up-to-date
Real-time Information: Cannot access current events or real-time data

Technical Limitations:

Model Size: With 124M parameters, complex reasoning capabilities are limited compared to larger models
Context Length: Limited context window may affect very long conversations
Language Specialization: Optimized primarily for Indonesian; other languages may produce suboptimal results

Response Characteristics:

Formality: Responses may occasionally sound formal due to Wikipedia-based training data
Consistency: May generate repetitive patterns or inconsistent information across sessions
Cultural Nuances: While trained on Indonesian data, may miss subtle cultural references or regional variations

🚀 Future Development Roadmap

Short-term Improvements:

Fine-tuning with more diverse conversational datasets
Integration of current Indonesian news and cultural content
Specialized domain adaptations (education, healthcare, business)

📝 License

This model is released under the MIT License, Please see the LICENSE file for complete terms.

🙏 Acknowledgments

Base Model: Thanks to Cahya for the Indonesian GPT-2 base model
Datasets: FreedomIntelligence team for Indonesian instruction and conversation datasets
Infrastructure: Google Colab for providing accessible GPU resources for training

Disclaimer: This model was developed as an experimental project for educational and research purposes. While it demonstrates good performance on various tasks, users should validate outputs for critical applications and be aware of the limitations outlined above.

IzzulGod/GPT2-Indo-chat-tuned

GPT2-Small Indonesian Instruct-Tuned Model

📋 Model Overview

🧪 Project Background

🚀 Quick Start

Installation

Basic Usage

🎯 Model Capabilities

📊 Training Details

Dataset Composition

Training Configuration

Training Progress

🔧 Advanced Usage

Generation Parameter Tuning

Prompt Engineering

⚠️ Limitations and Considerations

🚀 Future Development Roadmap

📝 License

🙏 Acknowledgments

IzzulGod/
GPT2-Indo-chat-tuned