vndee

def generate(
    self,
    prompt_tokens: List[List[int]],
    max_gen_len: int,
    temperature: float = 0.6,
    top_p: float = 0.9,
    logprobs: bool = False,
    echo: bool = False,
) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
    # ....

vndee

import matplotlib.pyplot as plt
import numpy as np

def plot_temperature_effects(logits, temperatures):
    """
    Visualize how different temperatures affect probability distribution
    """
    plt.figure(figsize=(12, 6))
    x = np.arange(len(logits))
    

vndee

def sample_next_token(logits, temperature=1.0):
    """Sample next token from language model logits"""
    # Apply temperature
    scaled_logits = [l/temperature for l in logits]
    
    # Convert to probabilities with softmax
    max_logit = max(scaled_logits)
    exp_logits = [math.exp(l - max_logit) for l in scaled_logits]
    total = sum(exp_logits)
    probs = [e/total for e in exp_logits]

vndee

# For most things, Python's built-in tools work great
import random
random.seed(42)
random.random()  # Gets you a random decimal

vndee

# Different parts of your program should get their own random numbers
game_rng = MersenneTwister(seed1)  # For game stuff
ai_rng = MersenneTwister(seed2)    # For AI stuff

vndee

# Same starting number = same "random" sequence
rng1 = MersenneTwister(42)
rng2 = MersenneTwister(42)
assert rng1.extract_number() == rng2.extract_number()

vndee

# Using operating system's secure random source
import os
true_random_bytes = os.urandom(16)

# On Linux systems
with open("/dev/random", "rb") as f:
    hardware_random = f.read(16)

vndee

def sample_next_token(rng, logits, temperature=1.0):
    """Sample next token from language model logits"""
    # Apply temperature
    scaled_logits = [l/temperature for l in logits]
    
    # Convert to probabilities with softmax
    max_logit = max(scaled_logits)
    exp_logits = [math.exp(l - max_logit) for l in scaled_logits]
    total = sum(exp_logits)
    probs = [e/total for e in exp_logits]

vndee

def estimate_pi(rng, n=1000000):
    inside = 0
    for _ in range(n):
        x = uniform(rng, -1, 1)
        y = uniform(rng, -1, 1)
        if x*x + y*y <= 1:
            inside += 1
    return 4 * inside / n

mt = MersenneTwister(42)

vndee

def random_choice(rng, options, weights=None):
    """Pick something randomly from a list"""
    if weights is None:
        # Simple case: everything has equal chance
        index = rng.extract_number() % len(options)
        return options[index]
    
    # Complex case: some things are more likely than others
    # Generate random value in [0, sum(weights))
    r = random_float(rng) * sum(weights)