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Model: Azzindani/Deepseek_ID_Legal_Preview
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---
library_name: transformers
tags:
- unsloth
license: apache-2.0
datasets:
- Azzindani/Indonesian_Legal_QA
base_model:
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
---
📧 [Github](https://github.com/azzindani)
🔗 [LinkedIn](https://www.linkedin.com/in/azzindan1/)
# 🧠 DeepSeek-R1-0528-Qwen3-8B Instruct Fine-Tuned with GRPO on Indonesian Legal QA Dataset
Welcome! This repository hosts a **fine-tuned version of [DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/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](https://www.kaggle.com/code/azzindani/indonesian-legal-assistant-preview)
---
## 🚀 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](https://huggingface.co/datasets/Azzindani/Indonesian_Legal_QA)
* **Compute**: Nvidia RTX6000 Ada
* **Provider**: [vast.ai](https://cloud.vast.ai/)
* **Training Steps**: 2000
* **Number of Generation**: 16
* **Cost**: 50 USD
* **Distilled Knowledge**: [DeepSeek_0528_8B_Legal_Distill](https://huggingface.co/datasets/Azzindani/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:
1. **Understand the legal context**
2. **Identify the relevant law**
3. **Apply reasoning with facts**
4. **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
)
```
---
## 🤝 Acknowledgements
* [Deepseek team](https://huggingface.co/deepseek-ai)
* [GRPO research paper](https://arxiv.org/abs/2402.03300)
---

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import gradio as gr
import os
import torch
from transformers import GemmaTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Indonesian Legal Assistant</h1>
</div>
'''
LICENSE = """
<p/>
---
Built with Deepseek Qwen3
"""
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
<h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">DeepSeek-R1-0528-Qwen3-8B</h1>
<p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Finetuned with Indonesian Legal Consulting Data</p>
</div>
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
"""
# Load the tokenizer and model
model_name = 'Azzindani/Deepseek_ID_Legal_Preview'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code = True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map = 'auto', trust_remote_code = True, torch_dtype = torch.float16)
#model.to('cuda' if torch.cuda.is_available() else 'cpu')
def chat(message, history, temperature = 0.7, max_new_tokens = 1024, show_thinking = True):
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.
"""
# Prepare conversation history
conversation = [{'role' : 'system', 'content' : SYSTEM_PROMPT}]
for user_msg, assistant_msg in history:
conversation.append({'role' : 'user', 'content' : user_msg})
conversation.append({'role' : 'assistant', 'content' : assistant_msg})
conversation.append({'role' : 'user', 'content' : message})
# Ensure tokenizer and model are properly referenced
try:
input_ids = tokenizer.apply_chat_template(
conversation,
tokenize = True,
add_generation_prompt = True,
return_tensors = 'pt'
).to(model.device)
except Exception as e:
return f"Error preparing input: {str(e)}"
streamer = TextIteratorStreamer(tokenizer, skip_prompt = True, skip_special_tokens = True)
generate_kwargs = {
'input_ids' : input_ids,
'streamer' : streamer,
'max_new_tokens' : max_new_tokens,
'do_sample' : temperature > 0,
'temperature' : temperature if temperature > 0 else 1.0,
'min_p' : 0.1,
'top_p' : 1.0,
'top_k' : 20,
#'repetition_penalty' : 1.1
}
# Start generation in a thread to avoid blocking
thread = Thread(target = model.generate, kwargs = generate_kwargs)
thread.start()
# Buffers and flags
thinking_content = []
final_answer = []
live_output = []
in_thinking_block = False
saw_think_tag = False
thinking_header_shown = False
# Tracking streaming state
has_started_output = False
accumulated_text = ''
for new_text in streamer:
accumulated_text += new_text
has_started_output = True
# Detect thinking tags
if '<think>' in new_text:
in_thinking_block = True
saw_think_tag = True
new_text = new_text.replace('<think>', '')
if not thinking_header_shown:
live_output.append('\n🧠 **Sedang berfikir...**\n')
thinking_header_shown = True
if '</think>' in new_text:
in_thinking_block = False
new_text = new_text.replace('</think>', '')
live_output.append('\n\n-----\n✅ **Sedang menjawab...**\n')
# Handle output based on current state
if saw_think_tag:
# Normal operation with thinking tags
if in_thinking_block:
thinking_content.append(new_text)
live_output.append(new_text)
else:
# After thinking block, everything is the answer
final_answer.append(new_text)
live_output.append(new_text)
else:
# No thinking tags detected yet
if len(accumulated_text) > 20 and not saw_think_tag:
# No thinking format detected, treat everything as answer
if not thinking_header_shown:
live_output.append("\n⏭️ **Jawaban langsung:**\n\n")
thinking_header_shown = True
final_answer.append(new_text)
live_output.append(new_text)
else:
# Still accumulating to detect format
live_output.append(new_text)
# Always yield current output
yield ''.join(live_output)
# Final output format handling
if thinking_content:
# Show collapsible thinking section if there was thinking content
final_output = (
'<details><summary>🧠 <b>Proses berfikir (klik untuk melihat)</b></summary>\n\n'
+ ''.join(thinking_content) +
'\n</details>\n\n'
+ '-----\n✅ **Jawaban:**\n'
+ ''.join(final_answer)
)
yield final_output
# If no final answer was produced but we have output, use the entire output
if not final_answer and has_started_output:
final_answer = [accumulated_text]
# Update chat history with the clean answer only (no thinking/formatting)
history.append((message, ''.join(final_answer)))
return None # Explicit return to end function after yielding
# Gradio block
chatbot = gr.Chatbot(height = 500, placeholder = PLACEHOLDER, label = 'Legal Assistant')
with gr.Blocks(fill_height = True, css = css) as demo:
gr.Markdown(DESCRIPTION)
#gr.DuplicateButton(value = "Duplicate Space for private use", elem_id = "duplicate-button")
gr.ChatInterface(
fn = chat,
chatbot = chatbot,
fill_height = True,
additional_inputs_accordion = gr.Accordion(label = '⚙️ Parameters', open = False, render = False),
additional_inputs = [
gr.Slider(minimum = 0,
maximum = 1,
step = 0.1,
value = 0.6,
label = 'Temperature',
render = False),
gr.Slider(minimum = 128,
maximum = 4096,
step = 1,
value = 2048,
label = 'Max new tokens',
render = False),
],
examples = [
['Saya seorang CPNS di lingkungan Kementerian Hukum dan HAM RI, bekerja dengan sistem (anggota regu pengamanan lapas). Lazimnya PNS dengan jabatan yang sama diberikan libur kerja selama 2 hari. Saya selaku CPNS diwajibkan untuk melaksanakan dinas pada hari libur ke-2 secara berkala tanpa diberikan perintah secara tertulis/surat perintah. Apakah boleh CPNS bekerja dengan jam kerja yang sama dengan PNS? Dan Apakah CPNS boleh bekerja di luar aturan kerja yang sudah ditentukan?'],
['Mohon bantu jelaskan apa itu politik luar negeri? Indonesia menganut konsep politik luar negeri apa? Apa dasar hukum politik luar negeri Indonesia?'],
['Saya adalah istri kedua dari pernikahan siri, yang mana pernikahannya dilaksanakan tanpa sepengetahuan istri pertama. Dari pernikahan ini kami telah dikaruniai anak. Namun, saya ingin menggugat cerai suami. Bisakah istri siri menggugat cerai atau minta cerai? Bagaimana caranya?'],
['Apa yang dimaksud dengan bank perantara dan bank kustodian?Bagaimana langkah untuk meminta eksekusi dari putusan arbitrase nasional? Bisakah kita mengajukan eksekusi terhadap harta salah satu pihak yang tidak disebutkan dalam putusan arbitrase?'],
['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?'],
],
cache_examples = False,
)
gr.Markdown(LICENSE)
if __name__ == '__main__':
demo.launch()

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{%- if not add_generation_prompt is defined %}
{%- set add_generation_prompt = false %}
{%- endif %}
{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}
{%- for message in messages %}
{%- if message['role'] == 'system' %}
{%- if ns.is_first_sp %}
{%- set ns.system_prompt = ns.system_prompt + message['content'] %}
{%- set ns.is_first_sp = false %}
{%- else %}
{%- set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}
{%- endif %}
{%- endif %}
{%- endfor %}
{#- Adapted from https://github.com/sgl-project/sglang/blob/main/examples/chat_template/tool_chat_template_deepseekr1.jinja #}
{%- if tools is defined and tools is not none %}
{%- set tool_ns = namespace(text='You are a helpful assistant with tool calling capabilities. ' + 'When a tool call is needed, you MUST use the following format to issue the call:\n' + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>FUNCTION_NAME\n' + '```json\n{"param1": "value1", "param2": "value2"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>\n\n' + 'Make sure the JSON is valid.' + '## Tools\n\n### Function\n\nYou have the following functions available:\n\n') %}
{%- for tool in tools %}
{%- set tool_ns.text = tool_ns.text + '\n```json\n' + (tool | tojson) + '\n```\n' %}
{%- endfor %}
{%- if ns.system_prompt|length != 0 %}
{%- set ns.system_prompt = ns.system_prompt + '\n\n' + tool_ns.text %}
{%- else %}
{%- set ns.system_prompt = tool_ns.text %}
{%- endif %}
{%- endif %}
{{- bos_token }}
{{- ns.system_prompt }}
{%- set last_index = (messages|length - 1) %}
{%- for message in messages %}
{%- set content = message['content'] %}
{%- if message['role'] == 'user' %}
{%- set ns.is_tool = false -%}
{%- set ns.is_first = false -%}
{%- set ns.is_last_user = true -%}
{%- if loop.index0 == last_index %}
{{- '<｜User｜>' + content }}
{%- else %}
{{- '<｜User｜>' + content + '<｜Assistant｜>'}}
{%- endif %}
{%- endif %}
{%- if message['role'] == 'assistant' %}
{%- if '</think>' in content %}
{%- set content = (content.split('</think>')|last) %}
{%- endif %}
{%- endif %}
{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}
{%- set ns.is_last_user = false -%}
{%- if ns.is_tool %}
{{- '<｜tool▁outputs▁end｜>'}}
{%- endif %}
{%- set ns.is_first = false %}
{%- set ns.is_tool = false -%}
{%- set ns.is_output_first = true %}
{%- for tool in message['tool_calls'] %}
{%- set arguments = tool['function']['arguments'] %}
{%- if arguments is not string %}
{%- set arguments = arguments|tojson %}
{%- endif %}
{%- if not ns.is_first %}
{%- if content is none %}
{{- '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + arguments + '\n' + '```' + '<｜tool▁call▁end｜>'}}
}
{%- else %}
{{- content + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + arguments + '\n' + '```' + '<｜tool▁call▁end｜>'}}
{%- endif %}
{%- set ns.is_first = true -%}
{%- else %}
{{- '\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + arguments + '\n' + '```' + '<｜tool▁call▁end｜>'}}
{%- endif %}
{%- endfor %}
{{- '<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}
{%- endif %}
{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none) %}
{%- set ns.is_last_user = false -%}
{%- if ns.is_tool %}
{{- '<｜tool▁outputs▁end｜>' + content + '<｜end▁of▁sentence｜>'}}
{%- set ns.is_tool = false -%}
{%- else %}
{{- content + '<｜end▁of▁sentence｜>'}}
{%- endif %}
{%- endif %}
{%- if message['role'] == 'tool' %}
{%- set ns.is_last_user = false -%}
{%- set ns.is_tool = true -%}
{%- if ns.is_output_first %}
{{- '<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}
{%- set ns.is_output_first = false %}
{%- else %}
{{- '\n<｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}
{%- endif %}
{%- endif %}
{%- endfor -%}
{%- if ns.is_tool %}
{{- '<｜tool▁outputs▁end｜>'}}
{%- endif %}
{#- if add_generation_prompt and not ns.is_last_user and not ns.is_tool #}
{%- if add_generation_prompt and not ns.is_tool %}
{{- '<｜Assistant｜>'}}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 131072,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": 151654,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"attn_factor": 0.8782488562869419,
"factor": 4.0,
"original_max_position_embeddings": 32768,
"rope_type": "yarn"
},
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.53.2",
"unsloth_fixed": true,
"unsloth_version": "2025.6.12",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"_from_model_config": true,
"bos_token_id": 151643,
"eos_token_id": 151645,
"max_length": 131072,
"pad_token_id": 0,
"transformers_version": "4.53.2"
}

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