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

Libraries

How to use Enfysyz/JurisPrae with Transformers:

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

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

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

llama-cpp-python

How to use Enfysyz/JurisPrae with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Enfysyz/JurisPrae",
	filename="JurisPrae_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Enfysyz/JurisPrae with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Enfysyz/JurisPrae:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Enfysyz/JurisPrae:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Enfysyz/JurisPrae:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Enfysyz/JurisPrae: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 Enfysyz/JurisPrae:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Enfysyz/JurisPrae: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 Enfysyz/JurisPrae:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Enfysyz/JurisPrae:Q4_K_M

Use Docker

docker model run hf.co/Enfysyz/JurisPrae:Q4_K_M

LM Studio
Jan

vLLM

How to use Enfysyz/JurisPrae with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Enfysyz/JurisPrae:Q4_K_M

SGLang

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

Ollama
How to use Enfysyz/JurisPrae with Ollama:
```
ollama run hf.co/Enfysyz/JurisPrae:Q4_K_M
```

Unsloth Studio

How to use Enfysyz/JurisPrae 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 Enfysyz/JurisPrae 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 Enfysyz/JurisPrae to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use Enfysyz/JurisPrae with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Enfysyz/JurisPrae:Q4_K_M

Run and chat with the model

lemonade run user.JurisPrae-Q4_K_M

List all available models

lemonade list

JurisPrae: Your AI Civil Law Assistant

JurisPrae is a fine-tuned, 4-bit quantized version of the meta-llama/Meta-Llama-3-8B-Instruct model. It has been specifically trained to understand and respond to real-world questions about civil law, making legal information more accessible and understandable.

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

🎬 Showcase

Check out this video to see JurisPrae in action:

Running with Ollama

To run this model locally using Ollama, follow these steps:

Download the GGUF file from the "Files and versions" tab of this repository.

Create a Modelfile in the same directory as the downloaded GGUF file. Copy the following content into it:

# Replace with the actual name of your GGUF file
FROM ./JurisPrae-8B-Instruct-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 by running the following command in your terminal:
```
ollama create JurisPrae -f ./Modelfile
```
Run the model and start chatting:
```
ollama run JurisPrae
```

Model Details

Model Description

JurisPrae is a state-of-the-art chatbot designed to provide information and answer questions related to civil law. It aims to democratize legal knowledge for students, legal professionals, and anyone curious about their rights and obligations.

Model type: Causal language model (decoder-only)
Base Model: meta-llama/Meta-Llama-3-8B-Instruct
Quantization: The model has been quantized to 4-bit precision (GGUF), allowing for efficient performance on consumer hardware.
Fine-tuning Data: See the "Training Data" section below.

Uses

JurisPrae is intended for educational and informational purposes.

Natural Language Understanding: Ask questions in plain English, just as you would on a forum like Reddit, and get clear, concise answers.
Legal Concept Explanations: Break down complex legal jargon and concepts into easy-to-understand language.
Topic Exploration: Learn about various civil law topics, including contracts, torts, property law, and family law.
Case Law Summaries: Get high-level summaries of important legal cases to understand how the law has been applied.

Training Data

The base model was fine-tuned on a curated dataset constructed from posts and comments from the r/legal_advice subreddit. This dataset was chosen to provide the model with a strong understanding of how non-lawyers formulate legal questions and the kinds of real-world issues they face. The data was carefully cleaned and formatted into a conversational instruction format.

Limitations and Bias

Not a Lawyer: JurisPrae is not a lawyer and its responses do not constitute legal advice. It is a tool for information, not a substitute for professional legal counsel.
Potential for Inaccuracies: The model was trained on public forum data from r/legal_advice, which may contain inaccuracies. The model may therefore generate responses that are incorrect, incomplete, or outdated.
Jurisdictional Nuances: The model may not capture the specific nuances of local or regional laws. Civil law can vary significantly between jurisdictions.
No Knowledge of Recent Events: The model's knowledge is limited to its training data and it will not be aware of very recent legal developments or court rulings.

⚠️ Disclaimer

JurisPrae does not provide legal advice. The information provided by this chatbot is for educational purposes only. Always consult with a qualified attorney for advice on your specific situation. We are not liable for any actions taken based on the information provided by JurisPrae.

Downloads last month: 6

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for Enfysyz/JurisPrae

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(643)

this model

Enfysyz
/

JurisPrae