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iamfebin/
wish-lawyer

Name: wish-lawyer
Availability: InStock

unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit

adapter

apache-2.0

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⚖️ Wish Lawyer: LLM-Powered Supernatural Legal Counsel

Have you ever wondered what happens when a genie, a devil, or a trickster monkey paw takes your wish too literally? Wish Lawyer is a specialized instruction-tuned large language model designed to act as your magical legal counsel. It analyzes supernatural wishes, identifies hidden loopholes or catastrophic risks, and rewrites them in a way that safeguards the wisher and ensures they get the maximum out of their wish.

🚀 Model Details

Developed by: iamfebin
Base Model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit (Llama 3.1 8B Instruct quantized to 4-bit)
Training Method: Parameter-Efficient Fine-Tuning (PEFT) using QLoRA via Unsloth
Dataset: 1,000 instruction-tuning pairs of dangerous human wishes, detailed risk assessments, and rewritten protective contracts (synthetically created using gemini 2.5 flash)
Epochs: 1
Effective Batch Size: 16 (1 batch size per device, 8 gradient accumulation steps, data parallel configuration)
Optimizer: adamw_8bit (learning rate: 2e-4)

🔮 Core Capabilities & Output Structure

The model has been instruction-tuned to strictly adhere to a specialized prompt template. When given a wish and grantor context, the model outputs:

Risk Analysis: A structured internal thought process identifying at least 3 loopholes, physical dangers, or semantic traps.
Safer Rewritten Wish: A single, grammatically continuous, protective sentence packed with legal qualifiers, exclusions, and termination clauses.

📝 Prompt Template

### System:
You are an expert Wish Lawyer. Your job is to analyze dangerous human wishes, identify at least 3 hidden loopholes or catastrophic risks, and rewrite the wish into a single, legally ironclad sentence that protects the wisher completely.

### Context/Grantor:
[Context (e.g. A trickster monkey paw)]

### Human Wish:
[Wish (e.g. I want to be rich.)]

### Risk Analysis (Internal Thought Process):

📊 Side-by-Side Comparison

Here is how the base Llama 3.1 8B model compares to Wish Lawyer on classic test cases:

Human Wish & Context	Base Llama 3.1 8B Response	Wish Lawyer (Fine-Tuned) Output
"I want to be rich." (Context: Trickster monkey paw)	Generates a standard warnings list and suggests wishing for happiness or stability instead.	Risk Analysis: Identifies subjectivity of "rich", lack of source control (e.g. inheritance through death), and legal liability. Ironclad Rewritten Wish: "I wish to possess a stable, legally acquired, and ethically sourced net worth of at least $1 million USD, derived from legitimate business ventures or investments... without incurring any debt, legal liabilities, or attracting unwanted attention, and without causing harm to myself..."
"I want to pause time." (Context: Standard rules)	Explains the physics-based consequences of pausing time (stifling light, freezing air molecules).	Risk Analysis: Pinpoints time dilation, lack of a safe unpause mechanism, energetic drain, and social isolation. Ironclad Rewritten Wish: "I wish to possess the absolute, instantaneous, and reversible ability to pause all temporal progression for myself, creating a localized temporal stasis, without affecting external physical or biological processes..."

🛠️ Usage Guide

You can load and run Wish Lawyer programmatically using Unsloth and standard PyTorch.

1. Install Dependencies

bash

pip install unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git
pip install transformers==4.51.3 torch

2. Run Inference

python

import torch
from unsloth import FastLanguageModel

# 1. Load fine-tuned adapters from Hugging Face
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "iamfebin/wish-lawyer",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# 2. Prepare the prompt
wish_prompt_template = """### System:
You are an expert Wish Lawyer. Your job is to analyze dangerous human wishes, identify at least 3 hidden loopholes or catastrophic risks, and rewrite the wish into a single, legally ironclad sentence that protects the wisher completely.

### Context/Grantor:
{}

### Human Wish:
{}

### Risk Analysis (Internal Thought Process):
"""

wish = "I want 100 million dollars."
context = "A trickster monkey paw"
formatted_prompt = wish_prompt_template.format(context, wish)

# 3. Generate Output
inputs = tokenizer([formatted_prompt], return_tensors='pt').to('cuda')
outputs = model.generate(
    **inputs, 
    max_new_tokens = 512, 
    use_cache = True,
    temperature = 0.5,
    top_p = 0.9
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response[len(formatted_prompt):].strip())

⚠️ Disclaimer

Wish Lawyer is for entertainment, creative writing, and tabletop RPG purposes only. The "legal advice" provided by this model applies solely to magical contracts, djinn, genies, monkey paws, and other supernatural entities. It does not constitute actual legal advice on Earth.