From c4fe697a9a7587dbcfa8533cce709a90ba8c813a Mon Sep 17 00:00:00 2001 From: tcsenpai Date: Mon, 13 Jan 2025 15:07:30 +0100 Subject: [PATCH] first commit --- .gitignore | 10 ++ .python-version | 1 + README.md | 66 +++++++++ llm_rng/__init__.py | 0 llm_rng/prompt_generator.py | 14 ++ llm_rng/rng_generator.py | 30 ++++ llm_rng/utils.py | 4 + main.py | 17 +++ pyproject.toml | 11 ++ requirements.txt | 2 + tests/__init__.py | 0 tests/test_entropy.py | 48 +++++++ tests/test_rng.py | 29 ++++ uv.lock | 265 ++++++++++++++++++++++++++++++++++++ 14 files changed, 497 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 llm_rng/__init__.py create mode 100644 llm_rng/prompt_generator.py create mode 100644 llm_rng/rng_generator.py create mode 100644 llm_rng/utils.py create mode 100644 main.py create mode 100644 pyproject.toml create mode 100644 requirements.txt create mode 100644 tests/__init__.py create mode 100644 tests/test_entropy.py create mode 100644 tests/test_rng.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/README.md b/README.md new file mode 100644 index 0000000..ffd70e0 --- /dev/null +++ b/README.md @@ -0,0 +1,66 @@ +# LLM-Based Random Number Generator + +This project demonstrates how to use a Large Language Model (LLM) to generate pseudo-random numbers by hashing its text outputs. + +## Features + +- Uses LLM outputs for randomness. +- Hashing ensures uniform distribution. +- Modular design for extensibility. +- Includes unit tests and entropy analysis. + +## Why + +This is just a fun experiment to see if we can use LLMs to generate random numbers. It builds on the idea that LLMs outputs are difficult to predict and can be used to generate random numbers as hashing their outputs will generate different numbers. + +## How to Run + +1. Install dependencies: + + ```bash + pip install -r requirements.txt + ``` + +2. Run the experiment: + + ```bash + python main.py + ``` + +3. Run tests: + ```bash + python -m unittest discover tests/ -v + ``` + +## Dependencies + +- ollama +- numpy +- tqdm + +## Entropy Analysis + +The project includes entropy testing to ensure quality randomness. For our range (1-100), the entropy values mean: + +- 6.64 bits: Theoretical maximum (perfect uniformity) +- 5-6 bits: Very good randomness +- 3-4 bits: Moderate randomness +- 1-2 bits: Poor randomness +- 0 bits: No randomness + +Our implementation typically achieves entropy values above 5 bits, indicating good randomness quality comparable to standard pseudo-random number generators (Python's random.randint() achieves ~6.2-6.4 bits). + +Example entropy test results using the test_entropy.py file: + +``` +INFO:test_entropy: +Final entropy value: 5.253660689688187 +INFO:test_entropy:Checking if entropy (5.253660689688187) > 3 +ok +``` + +## Notes + +- This approach is not suitable for cryptographic applications. +- The LLM response length is limited to 50 tokens for performance. +- First run might be slower due to model loading. diff --git a/llm_rng/__init__.py b/llm_rng/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/llm_rng/prompt_generator.py b/llm_rng/prompt_generator.py new file mode 100644 index 0000000..9561853 --- /dev/null +++ b/llm_rng/prompt_generator.py @@ -0,0 +1,14 @@ +import random + + +class PromptGenerator: + def __init__(self): + self.prompts = [ + "What is the most unpredictable string you can generate?", + "Generate a random assortment of words unrelated to each other.", + "Provide a single, creative sentence with no context.", + ] + + def get_random_prompt(self) -> str: + """ Get a random prompt from the predefined list. """ + return random.choice(self.prompts) diff --git a/llm_rng/rng_generator.py b/llm_rng/rng_generator.py new file mode 100644 index 0000000..486e170 --- /dev/null +++ b/llm_rng/rng_generator.py @@ -0,0 +1,30 @@ +import hashlib +import ollama +from llm_rng.utils import map_hash_to_range + + +class LLMRNG: + def __init__(self, model_name="llama3.1"): + self.client = ollama.Client() + self.model_name = model_name + + def generate_text(self, prompt: str) -> str: + """Query the LLM with a given prompt to generate a response.""" + response = self.client.generate( + model=self.model_name, + prompt=prompt, + options={ + "num_predict": 50, # Limit output to 50 tokens + "temperature": 1.0, # Keep high temperature for randomness + "timeout": 10, # 10 second timeout + }, + ) + return response["response"].strip() + + def generate_random_number( + self, prompt: str, range_min: int, range_max: int + ) -> int: + """Generate a pseudo-random number by hashing the LLM's output.""" + text_output = self.generate_text(prompt) + hash_object = hashlib.sha256(text_output.encode()) + return map_hash_to_range(hash_object.hexdigest(), range_min, range_max) diff --git a/llm_rng/utils.py b/llm_rng/utils.py new file mode 100644 index 0000000..9967a79 --- /dev/null +++ b/llm_rng/utils.py @@ -0,0 +1,4 @@ +def map_hash_to_range(hash_hex: str, range_min: int, range_max: int) -> int: + """ Map a SHA-256 hash to a number within a given range. """ + hash_int = int(hash_hex, 16) # Convert hex to integer + return range_min + (hash_int % (range_max - range_min + 1)) diff --git a/main.py b/main.py new file mode 100644 index 0000000..f7b7741 --- /dev/null +++ b/main.py @@ -0,0 +1,17 @@ +from llm_rng.rng_generator import LLMRNG +from llm_rng.prompt_generator import PromptGenerator + + +def main(): + rng = LLMRNG() + prompt_gen = PromptGenerator() + + print("Running LLM-based random number generation experiment...") + for i in range(5): + prompt = prompt_gen.get_random_prompt() + random_number = rng.generate_random_number(prompt, 1, 100) + print(f"Random number {i + 1}: {random_number}") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..89cbc72 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "llm-rng-project" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "numpy>=2.2.1", + "ollama>=0.4.5", + "tqdm>=4.67.1", +] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e6173ce --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +ollama +numpy \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_entropy.py b/tests/test_entropy.py new file mode 100644 index 0000000..52fe3af --- /dev/null +++ b/tests/test_entropy.py @@ -0,0 +1,48 @@ +import unittest +import numpy as np +import logging +from tqdm import tqdm +from llm_rng.rng_generator import LLMRNG +from llm_rng.prompt_generator import PromptGenerator + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class TestEntropy(unittest.TestCase): + def setUp(self): + logger.info("Initializing test environment...") + self.rng = LLMRNG() + self.prompt_gen = PromptGenerator() + + def test_entropy_of_generated_numbers(self): + samples = [] + n_samples = 50 # Reduced from 1000 for faster testing + logger.info(f"Generating {n_samples} random numbers for entropy test...") + + for i in tqdm(range(n_samples), desc="Generating numbers"): + prompt = self.prompt_gen.get_random_prompt() + try: + number = self.rng.generate_random_number(prompt, 1, 100) + samples.append(number) + if i % 10 == 0: # Log every 10th number + logger.info(f"Generated number {i}: {number}") + except Exception as e: + logger.error(f"Error generating number: {str(e)}") + + # Calculate and display distribution statistics + logger.info("\nCalculating distribution statistics...") + unique_values = np.unique(samples, return_counts=True) + for value, count in zip(unique_values[0], unique_values[1]): + logger.info(f"Value {value} appeared {count} times") + + # Calculate Shannon entropy + value_counts = unique_values[1] + probabilities = value_counts / len(samples) + entropy = -np.sum(probabilities * np.log2(probabilities)) + logger.info(f"\nFinal entropy value: {entropy}") + + # Reduced entropy threshold due to smaller sample size + min_entropy = 3 + logger.info(f"Checking if entropy ({entropy}) > {min_entropy}") + self.assertGreater(entropy, min_entropy) diff --git a/tests/test_rng.py b/tests/test_rng.py new file mode 100644 index 0000000..05e5709 --- /dev/null +++ b/tests/test_rng.py @@ -0,0 +1,29 @@ +import unittest +import logging +from llm_rng.rng_generator import LLMRNG +from llm_rng.utils import map_hash_to_range + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class TestLLMRNG(unittest.TestCase): + def setUp(self): + logger.info("Initializing LLMRNG for testing...") + self.rng = LLMRNG() + + def test_map_hash_to_range(self): + logger.info("Testing hash mapping function...") + hash_hex = "abcd" * 16 + result = map_hash_to_range(hash_hex, 1, 100) + logger.info(f"Mapped hash to number: {result}") + self.assertTrue(1 <= result <= 100) + + def test_generate_random_number(self): + logger.info("Testing 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