Welcome! This repository hosts a fine-tuned version of DeepSeek-R1-0528-Qwen3-8B using Group Relative Policy Optimization (GRPO), trained on a custom Indonesian Legal Q&A Dataset. The goal is to enhance the model's reasoning and structured thinking capabilities for legal question-answering tasks. You can try the demo here

🚀 Model Summary

Purpose: Research and Development
Base Model: DeepSeek-R1-0528-Qwen3-8B
Language: Bahasa Indonesia 🇮🇩
Domain: Legal / Law (Q&A format)
Purpose: Boost performance in structured, legal reasoning under Indonesian legal context

🏋️ Training Summary

Fine-tuning Method: Group Relative Policy Optimization (GRPO) combined with Knowledge Distillation
Pipeline: Cloud to Cloud training
Dataset: Indonesian Legal Questions and Answers pertanyaan hukum
Compute: Nvidia RTX6000 Ada
Provider: vast.ai
Training Steps: 2000
Number of Generation: 16
Cost: 50 USD
Distilled Knowledge: DeepSeek_0528_8B_Legal_Distill

🧩 What is GRPO?

Group Relative Policy Optimization (GRPO) is an advanced reinforcement learning fine-tuning technique that:

Groups samples by difficulty or topic (e.g., legal concepts)
Encourages policies (model outputs) to optimize within their group context
Promotes structured and relative improvements, not just raw accuracy

This method leads to:

Better structured answers
Improved logical flow
Greater consistency in domain-specific reasoning (e.g., answering legal queries with relevant laws and regulations)

🧠 Structured Thinking Enabled

The fine-tuned model is trained to think in steps using GRPO:

Understand the legal context
Identify the relevant law
Apply reasoning with facts
Summarize the legal conclusion clearly

This mimics how law students or practitioners approach legal cases, making the model suitable for:

Law education
Legal chatbot assistants
Indonesian legal exam prep

💻 How to Use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

# Load the tokenizer and model
model_name = 'Azzindani/Deepseek_ID_Legal_Preview'

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map = 'auto', torch_dtype = torch.float16)

SYSTEM_PROMPT = '''
Anda adalah asisten AI yang ahli di bidang hukum Indonesia. Anda dapat membantu konsultasi hukum, menjawab pertanyaan, dan memberikan analisis berdasarkan peraturan perundang-undangan yang relevan.
Untuk setiap respons, Anda harus berfikir dan menjawab dengan Bahasa Indonesia, serta gunakan format:
<think>
...
</think>
Tuliskan jawaban akhir secara jelas, ringkas, profesional, dan berempati jika diperlukan. Gunakan bahasa hukum yang mudah dipahami. Sertakan referensi hukum Indonesia yang relevan. Selalu rekomendasikan konsultasi dengan ahli hukum untuk keputusan final.
'''

prompt = '''
Adakah hukumnya yang mengatur pembagian persentase/laba dalam mendirikan suatu perusahaan?
Dan berapa persenkah yang didapat oleh si pemilik ide untuk mendirikan perusahaan,
jika dia tidak menyetor modal sedikit pun atau hanya menjalankan saja?
'''

conversation = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "Apa dasar hukum pemecatan PNS di Indonesia?"}
]

input_ids = tokenizer.apply_chat_template([
    {'role' : 'system', 'content' : SYSTEM_PROMPT},
    {'role' : 'user', 'content' : prompt}],
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = 'pt'
).to(model.device)

streamer = TextStreamer(tokenizer, skip_prompt = True, skip_special_tokens = True)

result = model.generate(
    input_ids,
    streamer = streamer,
    max_new_tokens = 2048,
    do_sample = True,
    temperature = 0.7,
    min_p = 0.1,
    top_p = 1.0,
    top_k = 20
)

Azzindani/Deepseek_ID_Legal_Preview

🧠 DeepSeek-R1-0528-Qwen3-8B Instruct Fine-Tuned with GRPO on Indonesian Legal QA Dataset

🚀 Model Summary

🏋️ Training Summary

🧩 What is GRPO?

🧠 Structured Thinking Enabled

💻 How to Use

🤝 Acknowledgements

Azzindani/
Deepseek_ID_Legal_Preview