Instructions to use LeeChanRX/LeeChan-LegalRights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeeChanRX/LeeChan-LegalRights with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeeChanRX/LeeChan-LegalRights")
model = AutoModelForCausalLM.from_pretrained("LeeChanRX/LeeChan-LegalRights")
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 LeeChanRX/LeeChan-LegalRights with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LeeChanRX/LeeChan-LegalRights

SGLang

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

Unsloth Studio new

How to use LeeChanRX/LeeChan-LegalRights 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 LeeChanRX/LeeChan-LegalRights 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 LeeChanRX/LeeChan-LegalRights to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeeChanRX/LeeChan-LegalRights to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LeeChanRX/LeeChan-LegalRights",
    max_seq_length=2048,
)

Docker Model Runner
How to use LeeChanRX/LeeChan-LegalRights with Docker Model Runner:
```
docker model run hf.co/LeeChanRX/LeeChan-LegalRights
```

LeeChanRX-LegalRights

LeeChanRX-LegalRights is a fine-tuned legal language model built using supervised fine-tuning (SFT) on curated legal datasets.

Features

Legal question answering
Contract explanation
Basic legal reasoning
Plain-language legal responses

Training Data

LegalFlow-v1 (50,000 samples)
LegalBench
LEDGAR
EURLEX

Method

LoRA fine-tuning (Unsloth optimized)
~29M trainable parameters
0.96% of total model parameters

Usage (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "LeeChanRX/LeeChanRX-LegalRights"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain breach of contract in simple terms"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Chat Format Usage

messages = [
    {"role": "system", "content": "You are a legal AI assistant."},
    {"role": "user", "content": "What are my rights if a contract is broken?"}
]

Disclaimer

This model is for educational purposes only and is not a substitute for professional legal advice.

Status

Training: Completed
Model: Ready for inference
Version: LegalRights v1

If you want next upgrade, I can help you build:

👉 LegalRights v2 (advanced reasoning + court prediction)

Downloads last month: 68

Safetensors

Model size

3B params

Tensor type

BF16