Instructions to use Azzindani/Deepseek_ID_Legal_Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azzindani/Deepseek_ID_Legal_Preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Azzindani/Deepseek_ID_Legal_Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azzindani/Deepseek_ID_Legal_Preview")
model = AutoModelForCausalLM.from_pretrained("Azzindani/Deepseek_ID_Legal_Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azzindani/Deepseek_ID_Legal_Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azzindani/Deepseek_ID_Legal_Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzindani/Deepseek_ID_Legal_Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azzindani/Deepseek_ID_Legal_Preview

SGLang

How to use Azzindani/Deepseek_ID_Legal_Preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Azzindani/Deepseek_ID_Legal_Preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzindani/Deepseek_ID_Legal_Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Azzindani/Deepseek_ID_Legal_Preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzindani/Deepseek_ID_Legal_Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Azzindani/Deepseek_ID_Legal_Preview with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azzindani/Deepseek_ID_Legal_Preview to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azzindani/Deepseek_ID_Legal_Preview to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Azzindani/Deepseek_ID_Legal_Preview to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Azzindani/Deepseek_ID_Legal_Preview",
    max_seq_length=2048,
)

Docker Model Runner
How to use Azzindani/Deepseek_ID_Legal_Preview with Docker Model Runner:
```
docker model run hf.co/Azzindani/Deepseek_ID_Legal_Preview
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

📧 Github 🔗 LinkedIn

🧠 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 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

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

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

BF16

F16

Model tree for Azzindani/Deepseek_ID_Legal_Preview

Base model

deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Finetuned

(46)

this model

Quantizations

1 model

Dataset used to train Azzindani/Deepseek_ID_Legal_Preview

Paper for Azzindani/Deepseek_ID_Legal_Preview

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5, 2024 • 145