Qwen3_0.6B_Legal_Checkpoint

⚖️ Fine-Tuning Indonesian Legal QA with GRPO (In Progress)

This repository documents the real-world fine-tuning process of an Indonesian legal case QA dataset using Group Relative Policy Optimization (GRPO) — a reinforcement learning technique optimized for language models.

Rather than a "proof of concept," this is a proof of practice:

✅ That fine-tuning legal QA models is achievable ✅ That free tools like Kaggle and Hugging Face are enough ✅ That you don’t need a paid GPU or local compute to build powerful NLP systems

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🔄 Fine-Tuning Workflow

• ✅ Model: Lightweight, efficient transformer-based model (ideal for legal QA)
• ✅ Technique: GRPO (Group Relative Policy Optimization)
• ✅ Compute: Kaggle GPU (30-hour quota / week)
• ✅ Pipeline: Cloud-to-cloud push/pull of checkpoints via Hugging Face Hub
• ✅ Epochs: Trained across 3 full epochs on the complete dataset

Checkpoints are updated incrementally and stored in Hugging Face for easy access and reproducibility.

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🛠️ Current Status

• 🟡 Training in progress
• 📈 GRPO working efficiently within Kaggle limits
• ⏱️ Approx. 72 hours projected for full run (within Kaggle’s free GPU quota)
• ✅ All training, saving, and transfer operations are done via cloud tools — no local GPU needed

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🎯 Why This Matters

This repository is a proof that:

• 🧑‍🎓 Students and researchers can fine-tune advanced models using only free cloud tools
• 🧪 GRPO can be applied in resource-constrained environments
• 📚 Legal NLP is now more accessible than ever
• 🌍 Anyone, from anywhere, can participate in open AI development — no expensive infrastructure required

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📦 What’s Inside

• 🧠 Indonesian legal QA dataset fine-tuned with GRPO
• 📊 Checkpoints from each training epoch
• 🔁 Compatible with transformers, peft, and RL libraries
• 📝 Simple logs and setup for reproducibility

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⚠️ Disclaimer

This repository is for learning, research, and development only. The dataset is derived from public consultations and may not represent finalized legal interpretations.

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🙏 Acknowledgments

• 🤗 Powered by Hugging Face
• 🧠 Trained with Kaggle Notebooks GPU
• 💡 Inspired by open-access ML for public good

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