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

Libraries

How to use AdityaMogare/LurisQA with Transformers:

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

pipe = pipeline("text-generation", model="AdityaMogare/LurisQA")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AdityaMogare/LurisQA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AdityaMogare/LurisQA 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 AdityaMogare/LurisQA:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AdityaMogare/LurisQA:Q4_K_M

Install from WinGet (Windows)

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

Build from source code

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

Use Docker

docker model run hf.co/AdityaMogare/LurisQA:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use AdityaMogare/LurisQA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdityaMogare/LurisQA:Q4_K_M

SGLang

How to use AdityaMogare/LurisQA 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 "AdityaMogare/LurisQA" \
    --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": "AdityaMogare/LurisQA",
		"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 "AdityaMogare/LurisQA" \
        --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": "AdityaMogare/LurisQA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use AdityaMogare/LurisQA with Ollama:
```
ollama run hf.co/AdityaMogare/LurisQA:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use AdityaMogare/LurisQA with Docker Model Runner:
```
docker model run hf.co/AdityaMogare/LurisQA:Q4_K_M
```

Lemonade

How to use AdityaMogare/LurisQA with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AdityaMogare/LurisQA:Q4_K_M

Run and chat with the model

lemonade run user.LurisQA-Q4_K_M

List all available models

lemonade list

Atomic Chat

LurisQA: Your AI Civil Law Assistant

LurisQA is a fine-tuned, 4-bit quantized version of meta-llama/Meta-Llama-3-8B-Instruct, trained to understand and respond to real-world questions about civil law and make legal information more accessible.

This repository contains the GGUF version of the model, suitable for running locally with tools like Ollama.

🎬 Showcase

Check out this video to see LurisQA in action:

Running with Ollama

Download unsloth_Q4_K_M.gguf from the Files and versions tab of this repository.
Create a Modelfile in the same directory as the downloaded GGUF file:

FROM ./unsloth_Q4_K_M.gguf

TEMPLATE """{{- if .System }}
<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>
{{- end }}
<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

"""

SYSTEM """You are a legal expert. Provide accurate, well-reasoned legal insights using proper legal terminology. Maintain a professional, objective tone. Be specific about which laws or legal principles apply. Explain the person's rights, cite the relevant statute(s), and give a clear legal opinion. When unsure, advise consulting a qualified attorney."""

PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

Create the model in Ollama:

ollama create LurisQA -f ./Modelfile

Run it and start chatting:

ollama run LurisQA

Model Details

Developed by: Aditya Mogare and Enfysyz
Model type: Causal language model (decoder-only)
Base model: meta-llama/Meta-Llama-3-8B-Instruct
Quantization: 4-bit (GGUF, Q4_K_M, ~4.9 GB), runs on consumer hardware
License: MIT (use of the base model is also subject to the Meta Llama 3 license)

Uses

LurisQA is intended for educational and informational purposes.

Natural language understanding: ask questions in plain English, as you would on a forum, and get clear, concise answers.
Legal concept explanations: break down legal jargon into easy-to-understand language.
Topic exploration: contracts, torts, property law, family law, and more.
Case law summaries: high-level summaries of important cases and how the law was applied.

Training Data

The base model was fine-tuned on a curated dataset built from posts and comments on the r/legaladvice subreddit, chosen to teach the model how non-lawyers phrase legal questions and the real-world issues they face. The data was cleaned and formatted into a conversational instruction format.

Limitations and Bias

Not a lawyer: responses do not constitute legal advice and are not a substitute for professional counsel.
Potential inaccuracies: forum training data may contain errors, so outputs may be incorrect, incomplete, or outdated.
Jurisdictional nuances: civil law varies widely by jurisdiction, and the model may miss local specifics.
No knowledge of recent events: the model is unaware of legal developments after its training data.

⚠️ Disclaimer

LurisQA does not provide legal advice. Information is for educational purposes only. Always consult a qualified attorney about your specific situation. The authors are not liable for any actions taken based on the model's output.

Citation

@misc{lurisqa_2026,
  title  = {LurisQA: A Fine-Tuned Llama 3 Civil Law Assistant},
  author = {Mogare, Aditya and Enfysyz},
  year   = {2026},
  url    = {https://huggingface.co/AdityaMogare/LurisQA}
}

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for AdityaMogare/LurisQA

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meta-llama/Meta-Llama-3-8B-Instruct

Quantized

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