DeepSeek_0528_8B_Legal_Ckpt

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☁️ Cloud Checkpoint Repository for GRPO Fine-Tuning on DeepSeek-R1-0528-Qwen3-8B

This repository serves as a cloud-to-cloud checkpoint for distributed fine-tuning and model continuation of the DeepSeek-R1-0528-Qwen3-8B model using Group Relative Policy Optimization (GRPO).

The checkpoints here are designed to be:

• 🔄 Resumable — continue training seamlessly across different cloud providers (e.g., Vast.ai → RunPod → Kaggle)
• ⚙️ Mergeable — combine multiple training shards or checkpoints for final model assembly
• ☁️ Cloud-native — optimized for upload/download efficiency and reusability in multi-cloud workflows

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🧠 Project Overview

This project fine-tunes DeepSeek-R1-0528-Qwen3-8B on the Indonesian Legal QA Dataset to enhance structured legal reasoning in Bahasa Indonesia 🇮🇩.
The end goal is a final merged LLM specialized for Indonesian legal analysis, powered by GRPO.

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☁️ Cloud-to-Cloud Checkpointing Workflow

This repository is built to support inter-cloud checkpoint transfers, enabling you to pause and resume training between different compute environments.

🏋️ Training Summary

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🧩 About GRPO

Group Relative Policy Optimization (GRPO) improves model alignment by optimizing within structured sample groups (e.g., legal case types). Compared to standard RLHF, GRPO encourages contextual reasoning improvements rather than simple accuracy gains.

✨ Benefits

• Better step-by-step reasoning
• More logical flow in legal Q&A
• Higher consistency in applying Indonesian law references

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🧠 Structured Reasoning Capability

During GRPO training, the model learns to reason like a legal analyst:

1. Identify legal issues
2. Recall relevant regulations
3. Apply reasoning to context
4. Formulate clear conclusions

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🤝 Acknowledgements

• DeepSeek team for the base model
• GRPO Paper (2024) for the optimization method
• unsloth.ai for efficient fine-tuning
• vast.ai for affordable compute

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🧾 License

This project is licensed under the Apache 2.0 License.

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🪄 Notes for Collaborators

If you are continuing training or merging checkpoints:

• Always pull the latest checkpoint from this repo before training.
• Push your progress under a new tag (e.g., checkpoint-2000) for traceability.
• When merging, verify consistency using the model.index.json structure to avoid parameter conflicts.

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💡 “Train anywhere, resume everywhere — building the Indonesian Legal LLM through cloud collaboration.”