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

Libraries

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Azzindani/Deepseek_ID_Legal_Preview_GGUF", dtype="auto")

llama-cpp-python

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Azzindani/Deepseek_ID_Legal_Preview_GGUF",
	filename="ID_Legal_Assistant_Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Use Docker

docker model run hf.co/Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Ollama:
```
ollama run hf.co/Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
```

Unsloth Studio

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF 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_GGUF 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_GGUF 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_GGUF to start chatting

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Docker Model Runner:
```
docker model run hf.co/Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M
```

Lemonade

How to use Azzindani/Deepseek_ID_Legal_Preview_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Azzindani/Deepseek_ID_Legal_Preview_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Deepseek_ID_Legal_Preview_GGUF-Q4_K_M

List all available models

lemonade list

📧 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

🗂️ Supported Software (No Coding Required)

You can use the GGUF version directly with these tools:

LM Studio – Desktop app (Windows/Mac) with chat interface and model downloader
Ollama – Simple local model runner with ollama run commands (Mac/Linux/Windows WSL)
KoboldCPP – For storytelling and roleplay, runs entirely on CPU or GPU
text-generation-webui – Web UI for running GGUF models with extensive settings
Jan – Cross‑platform AI desktop app with offline support
GPT4All – Desktop UI with GGUF compatibility for quick model loading

💻 How to Use

Download the GGUF file from the “Files and Versions” tab of this model page.
Open your preferred software (e.g., LM Studio, Ollama, KoboldCPP).
Load or import the GGUF file.
Customize 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.

Start chatting with the model — no Python code required!

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?

🤝 Acknowledgements

Downloads last month: 13

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Azzindani/Deepseek_ID_Legal_Preview_GGUF

Base model

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

Finetuned

Azzindani/Deepseek_ID_Legal_Preview

Quantized

(1)

this model

Dataset used to train Azzindani/Deepseek_ID_Legal_Preview_GGUF

Paper for Azzindani/Deepseek_ID_Legal_Preview_GGUF

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

Paper • 2402.03300 • Published Feb 5, 2024 • 148