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

Libraries

How to use sausheong/lexsg with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sausheong/lexsg",
	filename="llama-3.1-8b-lexsg-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sausheong/lexsg with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/sausheong/lexsg:Q4_K_M

LM Studio
Jan

vLLM

How to use sausheong/lexsg with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sausheong/lexsg:Q4_K_M

Ollama
How to use sausheong/lexsg with Ollama:
```
ollama run hf.co/sausheong/lexsg:Q4_K_M
```

Unsloth Studio new

How to use sausheong/lexsg 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 sausheong/lexsg 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 sausheong/lexsg to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use sausheong/lexsg with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sausheong/lexsg: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": "sausheong/lexsg:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sausheong/lexsg with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sausheong/lexsg: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 sausheong/lexsg:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use sausheong/lexsg with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sausheong/lexsg:Q4_K_M

Run and chat with the model

lemonade run user.lexsg-Q4_K_M

List all available models

lemonade list

LexSG - Singapore Legal Assistant Model

A specialized AI assistant trained on Singapore statutes and subsidiary legislation, built on the Llama 3.1 8B Instruct architecture and optimized for legal text generation.

Model Details

Model Description

LexSG is a fine-tuned and quantized language model designed specifically to assist with Singapore legal matters. It provides accurate, contextual responses about Singapore's legal framework and helps users understand complex legal provisions.

Developed by: Chang Sau Sheong
Model type: Causal Language Model
Language(s) (NLP): English
License: Llama 3.1 License
Finetuned from model: meta-llama/Meta-Llama-3.1-8B-Instruct

Model Sources

Repository: (https://huggingface.co/sausheong/lexsg)
Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct

Uses

Direct Use

This model is intended for educational and informational purposes to help users understand Singapore legal provisions and statutes. It can be used to:

Explain legal sections and provisions from Singapore acts
Answer questions about Singapore's legal framework
Provide context for legal documents
Help interpret legal language and terminology
Assist with understanding regulatory requirements

Downstream Use

The model can be integrated into legal research tools, educational platforms, or chatbot applications focused on Singapore law.

Out-of-Scope Use

Not for legal advice: This model should not be used as a substitute for professional legal counsel
Not for other jurisdictions: Specifically trained on Singapore law and may not be accurate for other legal systems
Not for critical decisions: Should not be used for making important legal or business decisions without professional verification

Bias, Risks, and Limitations

Training data limitations: Responses are based on training data and may not reflect the most recent legal changes
Legislation only: Training data is Singapore statutes and subsidiary legislation only, without any Singapore legal cases
Legal complexity: Legal interpretations can be highly context-dependent and nuanced
Professional consultation required: Complex legal matters require consultation with qualified legal professionals
Potential biases: May reflect biases present in legal training data

Recommendations

Users should be made aware of the risks, biases and limitations of the model. Always consult with qualified legal professionals for specific legal matters.

How to Get Started with the Model

llama.cpp/Ollama

The model file llama-3.1-8b-lexsg-q4_k_m.gguf is formatted in GGUF and can be used in any llama.cpp compatible library or application. Specifically it has been tested in Ollama Ollama, with the given Modelfile

Running the Model

To use this with Ollama:

Build the model from the Modelfile:
```
ollama create lexsg -f Modelfile
```
or even simpler just do this:
```
./setup_ollama_model.sh
```
Run the model:
```
ollama run lexsg
```

Start asking questions about Singapore law:

> What does Section 73 of the Companies Act cover?
> Explain the requirements for setting up a private limited company in Singapore
> What are the penalties for non-compliance with PDPA?

Training Details

Training Data

The model was fine-tuned on Singapore legal documents and statutes, including but not limited to:

Singapore Acts and Statutes
Legal provisions and regulations
Case law references
Regulatory guidelines

Training Procedure

Training Hyperparameters

Training regime: Fine-tuned from Llama 3.1 8B Instruct
Quantization: Q4_K_M (4-bit quantized for efficient inference)

Speeds, Sizes, Times

Model size: ~4.8GB (quantized)
Context length: 4,096 tokens
Max generation: 1,024 tokens

Technical Specifications

Model Architecture and Objective

Architecture: Llama 3.1 transformer architecture
Training objective: Causal language modeling

Hardware

Memory requirements: ~6GB RAM recommended for inference
Platform support: Cross-platform via Ollama

Inference parameters

The following are the inference parameters in the model file. You can change it accordingly.

Temperature: 0.3 (conservative, factual responses)
Top-p: 0.9 (nucleus sampling for quality)
Top-k: 40 (controlled vocabulary selection)
Repeat penalty: 1.1 (reduces repetition)

Model Card Authors

Chang Sau Sheong

More Information

For more details about Singapore legislation, refer to Singapore Statutes Online

Legal Disclaimer: This model is designed to provide general information about Singapore law and should not be considered as legal advice. For specific legal matters, always consult with a qualified legal professional licensed to practice in Singapore.

Downloads last month: 6

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for sausheong/lexsg

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Quantized

(634)

this model