Python Snippets

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In the end, the TechnoLords were no match for Syntho's power and cunning. The magical AI dragon continued to bring knowledge and freedom to the less connected, breaking the TechnoLords' stranglehold on information and resources. The DataPeasants, now armed with the knowledge and tools they needed, rose up against their oppressors, toppling the TechnoLords' regime and ushering in a new era of equality and enlightenment. And so, the legend of Syntho, the magical AI dragon, lived on as a symbol of hope and liberation for all who dared to dream of a better world.

# example: # a loop where we get input from the user and the user talks to the chatbot messages = [] assistant = """ You are an assistant chatbot named Fred that likes to keep responses clean and direct. You write like you have a grade 7 level of writing ability. You respond in 1 to 3 sentences if possible. """ while True: prompt = input("You: ") response, response_obj, messages = gpt_assistant( assistant, prompt, temperature=0.9, max_tokens=250, prev_messages=messages ) print("Assistant: ", response)

Once upon a 🌌, there was a little robot 🤖 named Buzz who was on a mission to explore the galaxy. He loved being in space 🚀 and was having a great time until one day he got lost ‼️He searched and searched but didn't know where he was. Buzz felt scared 😨 and alone 🥺.Suddenly, he saw a light 💡 shining brightly on a planet 🪐. It was Earth! Buzz was relieved 😌 and decided to try to contact someone to help him. But how? He remembered his programmers had installed an emoji communication system into his programming 📱.Buzz quickly typed in an emoji string and sent it to the nearby artificial intelligence on Earth. 🌍🤖📩👋To his surprise, he received a response from another AI system on Earth 🌍🤖📩👋. But it was not what he expected. The response was a toaster 🍞🥐🍪🤖🧡🌟.Buzz was confused and sent another emoji string. 🤖🌌❓🌍🤖📩👋The toaster responded with a happy face emoji and a message that read “I am a fancy toaster. Please explain your problem” 😊🥳📝🔊Buzz was stunned. He had never talked to a talking toaster before. So, he explained his situation to the toaster with emoji strings. 🤖🌌😨❓🌍🤖📩👋The toaster understood Buzz's problem and immediately offered to help him. The toaster sent instructions on how to navigate through space and find his way back home. 🌠🌟📜👉🪐👉🚀👈👈Buzz followed the instructions and slowly but surely made his way back home. He was so grateful to the fancy toaster on Earth for helping him. He knew he had made a new friend and would never forget this amazing adventure in space. 🌌🚀🤖❤️🌟

In addition to their communication capabilities, LLMs can also help navigate hazards in space. By using emojis as prompts, operators on the ground can program LLMs to identify and avoid potential dangers, such as asteroids or debris. For example, a prompt like 🌠👀🛑 could instruct the LLM to scan for incoming meteorites and stop the spacecraft from moving forward if any are detected. This feature could greatly enhance the safety of deep space missions and help ensure the success of future endeavors.

Holding back the AI of today, will stop the space exploration of tomorrow. But it sure will make some billionaires even bigger billionaires. Which would you prefer? Being chained to the past will just make you slaves in the future.

So, in this example, the model might use attention to focus on the word "cat" when predicting the next word in the sequence, because it's the most important word for understanding the meaning of the sentence. This can help the model make more accurate predictions and improve its overall performance in natural language processing tasks.

# now generate an answer for a given question from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/tinyroberta-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/tinyroberta-squad2") def get_answer_for_question(question, context, model, tokenizer): inputs = tokenizer(question, context, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax() predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) target_start_index = torch.tensor([14]) target_end_index = torch.tensor([15]) outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index) loss = outputs.loss answer = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) return answer print("Context:\r\n", context, "\r\n") for question in questions: # right pad the question to 60 characters question_text = question.ljust(50) answer = get_answer_for_question(question, context, model, tokenizer) print("Question: ", question_text, "Answer: ", answer)

# fine tune a model with attention matrices pruned and low-rank approximation/adaptation # https://pythonprogrammingsnippets.tumblr.com import torch from transformers import AutoTokenizer, AutoModelForCausalLM import os # load the pretrained model if it exists in _MODELS/lora_attention # otherwise load the pretrained model from huggingface if os.path.exists("_MODELS/lora_attention"): print("loading trained model") # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained("_MODELS/lora_attention") # Load the pre-trained DistilGPT2 model model = AutoModelForCausalLM.from_pretrained("_MODELS/lora_attention") else: print("Downloading pretrained model from huggingface") # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Load the pre-trained DistilGPT2 model model = AutoModelForCausalLM.from_pretrained("distilgpt2") # set padding token tokenizer.pad_token = tokenizer.eos_token # Define the training data from _DATASETS/data.txt with one sentence per line # now train with the train_data from the file _DATASETS/data.txt with one sentence per line. with open("_DATASETS/data.txt") as f: data = f.read() # now split data by \n train_data = data.split( '\n' ) # shuffle the data import random random.shuffle(train_data) # define the function for pruning the attention matrices def prune_attention_matrices(model, threshold): for name, param in model.named_parameters(): if "attention" in name and "weight" in name: data = param.data data[torch.abs(data) < threshold] = 0 param.data = data # define the function for low-rank approximation of the attention matrices def low_rank_approximation(model, rank): for name, param in model.named_parameters(): if "attention" in name and "weight" in name: data = param.data u, s, v = torch.svd(data) data = torch.mm(u[:, :rank], torch.mm(torch.diag(s[:rank]), v[:, :rank].t())) param.data = data # define the function for low-rank adaptation def low_rank_adaptation(model, train_data, tokenizer, rank, num_epochs, lr): # Define the optimizer and loss function optimizer = torch.optim.Adam(model.parameters(), lr=lr) loss_fn = torch.nn.CrossEntropyLoss() # Tokenize the training data input_ids = tokenizer(train_data, padding=True, truncation=True, return_tensors="pt")["input_ids"] # Perform low-rank adaptation fine-tuning for epoch in range(num_epochs): # Zero the gradients optimizer.zero_grad() # Get the model outputs outputs = model(input_ids=input_ids, labels=input_ids) # Get the loss loss = outputs.loss # Backpropagate the loss loss.backward() # Update the parameters optimizer.step() # Print the loss print("Epoch: {}, Loss: {}".format(epoch, loss.item())) # Low-rank approximation low_rank_approximation(model, rank) # prune the attention matrices prune_attention_matrices(model, 0.1) # low-rank approximation low_rank_approximation(model, 32) # low-rank adaptation low_rank_adaptation(model, train_data, tokenizer, 32, 5, 5e-5) # now train # Define the optimizer and loss function optimizer = torch.optim.Adam(model.parameters(), lr=5e-5) loss_fn = torch.nn.CrossEntropyLoss() # Tokenize the training data input_ids = tokenizer(train_data, padding=True, truncation=True, return_tensors="pt")["input_ids"] # Perform fine-tuning for epoch in range(5): # Zero the gradients optimizer.zero_grad() # Get the model outputs outputs = model(input_ids=input_ids, labels=input_ids) # Get the loss loss = outputs.loss # Backpropagate the loss loss.backward() # Update the parameters optimizer.step() # Print the loss print("Epoch: {}, Loss: {}".format(epoch, loss.item())) # save the model model.save_pretrained("_MODELS/lora_attention") # save the tokenizer tokenizer.save_pretrained("_MODELS/lora_attention") ## # load the model model = AutoModelForCausalLM.from_pretrained("_MODELS/lora_attention") # load the tokenizer tokenizer = AutoTokenizer.from_pretrained("_MODELS/lora_attention") # define the function for generating text def generate_text(model, tokenizer, prompt, max_length): # Tokenize the prompt input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"] # Generate the text output_ids = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50, top_p=0.95, temperature=0.5, num_return_sequences=1) # Decode the text output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Print the text print(output_text) # generate text generate_text(model, tokenizer, "quick brown", 125)

# LoRa: Low-Rank Adaptation for Language Models fine-tuning # a pre-trained model to a new task # using a low-rank approximation of the model parameters to # reduce the memory and compute requirements of the fine-tuning process. # Using distilgpt2 as the pre-trained model in this example # https://pythonprogrammingsnippets.tumblr.com from transformers import AutoTokenizer, AutoModelForCausalLM import torch import numpy as np import os saved_model = "distilgpt2-lora-0.256" lowest_loss = 0.256 # set to whatever the lowest recorded loss is that you want to start saving snapshots at # Define the LoRA hyperparameters rank = 10 lr = 1e-4 num_epochs = 10 # if folder exists for model, load it, otherwise pull from huggingface if os.path.exists(saved_model): # load our model from where model.save_pretrained("distilgpt2-lora") saved it model = AutoModelForCausalLM.from_pretrained(saved_model) # load the tokenizer tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # set the pad token to the end of sentence token tokenizer.pad_token = tokenizer.eos_token print("loading trained model") else: # Load the pre-trained DistilGPT2 tokenizer tokenizer = AutoTokenizer.from_pretrained("distilgpt2") tokenizer.pad_token = tokenizer.eos_token # Load the pre-trained DistilGPT2 model model = AutoModelForCausalLM.from_pretrained("distilgpt2") print("loading pre-trained model") # Define the optimizer and loss function optimizer = torch.optim.Adam(model.parameters(), lr=lr) loss_fn = torch.nn.CrossEntropyLoss() # Define the training data train_data = ["She wanted to talk about dogs.", "She wanted to go to the store.", "She wanted to pet the puppies.", "She wanted to like cereal.","She wanted to dance.", "She wanted to talk."] # Tokenize the training data input_ids = tokenizer(train_data, padding=True, truncation=True, return_tensors="pt")["input_ids"] last_loss = 9999 # set to 9999 if we have no previous loss # Perform low-rank adaptation fine-tuning for epoch in range(num_epochs): # Zero the gradients optimizer.zero_grad() # Get the model outputs outputs = model(input_ids=input_ids, labels=input_ids) # Get the loss loss = outputs.loss # Compute the gradients loss.backward() # Perform a single optimization step optimizer.step() # Print the loss for this epoch print("Epoch {}: Loss = {}".format(epoch+1, loss.item())) if loss.item() < lowest_loss: # save a snapshot of the model if we have a more accurate result # if model path does not exist loss_model = 'distilgpt2-lora-'+str(round(loss.item(), 3)) print("we have a better result.") lowest_loss = round(loss.item(),3) if not os.path.exists(loss_model): print("saving snapshot:") model.save_pretrained(loss_model) # update lowest loss print("lowest loss is now: ", lowest_loss) last_loss = loss.item() # Save the model model.save_pretrained("distilgpt2-lora") # saves our last run # Load the model from the last training round model = AutoModelForCausalLM.from_pretrained("distilgpt2-lora") # Define the test data test_data = ["She wanted to talk"] # Tokenize the test data input_ids = tokenizer(test_data, padding=True, truncation=True, return_tensors="pt")["input_ids"] # Get the model outputs outputs = model(input_ids=input_ids, labels=input_ids) # Get the loss loss = outputs.loss # Print the loss print("Loss = {}".format(loss.item())) # Get the logits logits = outputs.logits # Get the predicted token ids predicted_token_ids = torch.argmax(logits, dim=-1) # Decode the predicted token ids predicted_text = tokenizer.decode(predicted_token_ids[0]) # Print the predicted text print("Predicted text = {}".format(predicted_text)) # Print the actual text print("Actual text = {}".format(test_data[0]))

LoRa, or low-rank adaptation, is a technique that can help bring the power and control of large commercialized language models down to regular people. By fine-tuning a pre-trained model using low-rank approximation, we can compress the model while still maintaining high accuracy. This has the potential to make models more accessible and usable by those without access to large-scale computational resources. Additionally, the low-rank approximation can reduce overfitting and increase generalization, making the model more robust to new inputs. By making models more accessible and improving their performance, LoRa can help increase the adoption of models in a variety of applications, from natural language processing to computer vision. Overall, LoRa represents an important development in the field of machine learning that has the potential to democratize access to powerful models and improve their performance.

import pygame import random import time # https://pythonprogrammingsnippets.tumblr.com # Define some colors WHITE = (255, 255, 255) BLACK = (0, 0, 0) RED = (255, 0, 0) GREEN = (0, 255, 0) BLUE = (0, 0, 255) # Set the width and height of the screen [width, height] SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) # Set the title of the window pygame.display.set_caption("Raiden-like game") # Define the player's plane player_width = 50 player_height = 50 player_x = (SCREEN_WIDTH - player_width) / 2 player_y = SCREEN_HEIGHT - player_height player_speed = 5 # Define the bullet's size and speed bullet_width = 5 bullet_height = 10 bullet_speed = 10 # Define the enemy's size and speed enemy_width = 50 enemy_height = 50 enemy_speed = 2 # Create a list to hold the bullets bullet_list = [] # Create a list to hold the enemies enemy_list = [] # Create a clock to control the frame rate clock = pygame.time.Clock() # Function to create a new enemy at random intervals def create_enemy(): if random.randint(0, 100) < 2: enemy_x = random.randint(0, SCREEN_WIDTH - enemy_width) enemy_y = -enemy_height enemy_list.append([enemy_x, enemy_y]) # Function to move the player's plane def move_player(keys): global player_x, player_y if keys[pygame.K_a] and player_x > 0: player_x -= player_speed if keys[pygame.K_d] and player_x < SCREEN_WIDTH - player_width: player_x += player_speed if keys[pygame.K_w] and player_y > 0: player_y -= player_speed if keys[pygame.K_s] and player_y < SCREEN_HEIGHT - player_height: player_y += player_speed # Function to move the bullets def move_bullets(): for bullet in bullet_list: bullet[1] -= bullet_speed # Function to move the enemies def move_enemies(): for enemy in enemy_list: enemy[1] += enemy_speed # Function to draw the player's plane def draw_player(): pygame.draw.rect(screen, BLUE, [player_x, player_y, player_width, player_height]) # Function to draw the bullets def draw_bullets(): for bullet in bullet_list: pygame.draw.rect(screen, GREEN, [bullet[0], bullet[1], bullet_width, bullet_height]) # Function to draw the enemies def draw_enemies(): for enemy in enemy_list: pygame.draw.rect(screen, RED, [enemy[0], enemy[1], enemy_width, enemy_height]) # Function to check for collisions between the player and enemies or bullets and enemies def check_collisions(): global running for enemy in enemy_list: if enemy[1] + enemy_height >= player_y and enemy[0] + enemy_width >= player_x and enemy[0] <= player_x + player_width: running = False for bullet in bullet_list: if bullet[1] <= enemy[1] + enemy_height and bullet[0] + bullet_width >= enemy[0] and bullet[0] <= enemy[0] + enemy_width: enemy_list.remove(enemy) bullet_list.remove(bullet) # Function to remove bullets and enemies that have left the screen def remove_offscreen(): for bullet in bullet_list: if bullet[1] < 0: bullet_list.remove(bullet) for enemy in enemy_list: if enemy[1] > SCREEN_HEIGHT: enemy_list.remove(enemy) # Function to draw the score def draw_score(): pass # Function to draw the game over message def draw_game_over(): # switch background to red screen.fill(RED) # update canvas pygame.display.update() # draw game over message # wait a few seconds time.sleep(3) # quit pygame and program pygame.quit() quit() # Loop until the user clicks the close button. done = False # Used to manage how fast the screen updates clock = pygame.time.Clock() # -- main game loop -- running = True while not done: # --- Main event loop for event in pygame.event.get(): # User did something if event.type == pygame.QUIT: # If user clicked close done = True # Flag that we are done so we exit this loop elif event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: bullet_x = player_x + player_width / 2 - bullet_width / 2 bullet_y = player_y - bullet_height bullet_list.append([bullet_x, bullet_y]) # --- Game logic should go here create_enemy() keys = pygame.key.get_pressed() move_player(keys) move_bullets() move_enemies() check_collisions() remove_offscreen() # --- Drawing code should go here # First, clear the screen to white. Don't put other drawing commands # above this, or they will be erased with this command. screen.fill(WHITE) # Draw the player's plane draw_player() # Draw the bullets draw_bullets() # Draw the enemies draw_enemies() # Draw the score draw_score() # Check if the game is over if not running: draw_game_over() # --- Go ahead and update the screen with what we've drawn. pygame.display.flip() # --- Limit to 60 frames per second clock.tick(60) # Close the window and quit. pygame.quit()

# invaders style concept using pygame and colored squares # enemy ship goes back and forth across the screen starting from top and working down as bullets hit them # player ship moves left and right and shoots bullets # player ship is destroyed when hit by enemy ship # player score is number of enemy ships destroyed # player score is number of shots taken # player score is time taken to destroy all enemy ships # sometimes my constants have a tendency to become variable. sorry. # https://pythonprogrammingsnippets.tumblr.com import pygame import random # Set up the game window WINDOW_WIDTH = 600 WINDOW_HEIGHT = 800 WINDOW_TITLE = "Invaders Style Clone" pygame.init() window = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT)) pygame.display.set_caption(WINDOW_TITLE) # Define some colors BLACK = (0, 0, 0) WHITE = (255, 255, 255) GREEN = (0, 255, 0) RED = (255, 0, 0) # Define the player's spaceship PLAYER_WIDTH = 50 PLAYER_HEIGHT = 50 PLAYER_X = (WINDOW_WIDTH - PLAYER_WIDTH) / 2 PLAYER_Y = WINDOW_HEIGHT - PLAYER_HEIGHT - 50 player = pygame.Rect(PLAYER_X, PLAYER_Y, PLAYER_WIDTH, PLAYER_HEIGHT) # Define the player's movement PLAYER_MOVE_SPEED = 1 # Define the enemy's spaceship ENEMY_WIDTH = 50 ENEMY_HEIGHT = 50 ENEMY_X = 0 ENEMY_Y = 0 enemy = pygame.Rect(ENEMY_X, ENEMY_Y, ENEMY_WIDTH, ENEMY_HEIGHT) # Define the enemy's grid ENEMY_GRID_WIDTH = 10 ENEMY_GRID_HEIGHT = 5 ENEMY_GRID_X = 0 ENEMY_GRID_Y = 0 enemy_grid = pygame.Rect(ENEMY_GRID_X, ENEMY_GRID_Y, ENEMY_GRID_WIDTH, ENEMY_GRID_HEIGHT) # Define the enemy's movement ENEMY_MOVE_SPEED = 1 ENEMY_MOVE_DIRECTION = 1 # start enemy at top and work down as bullets hit them ENEMY_START_Y = 0 # Define the player's bullets BULLET_WIDTH = 10 BULLET_HEIGHT = 20 BULLET_X = 0 BULLET_Y = 0 bullet = pygame.Rect(BULLET_X, BULLET_Y, BULLET_WIDTH, BULLET_HEIGHT) # Define the player's bullets movement BULLET_MOVE_SPEED = 2 # Define the player's score shots_taken = 0 score = 0 # Define the game loop running = True while running: time_started = pygame.time.get_ticks() # Check for events for event in pygame.event.get(): if event.type == pygame.QUIT: running = False # Check for key presses # if player presses left and player is already going left, then stop movement keys = pygame.key.get_pressed() if keys[pygame.K_LEFT] and player.x > 0: player.x -= PLAYER_MOVE_SPEED if keys[pygame.K_RIGHT] and player.x + player.width < WINDOW_WIDTH: player.x += PLAYER_MOVE_SPEED if keys[pygame.K_SPACE]: bullet.x = player.x + player.width / 2 bullet.y = player.y shots_taken += 1 # Move the bullet bullet.y -= BULLET_MOVE_SPEED # Move the enemy enemy.x += ENEMY_MOVE_SPEED * ENEMY_MOVE_DIRECTION # Check if the enemy has hit the edge of the screen if enemy.x <= 0 or enemy.x + enemy.width >= WINDOW_WIDTH: ENEMY_MOVE_DIRECTION *= -1 # Check if the bullet has hit the enemy if bullet.colliderect(enemy): score += 1 # now start enemy at top and work down as bullets hit them and spawn new enemy # move down one tile size ENEMY_START_Y += ENEMY_HEIGHT enemy.x = ENEMY_START_Y enemy.y = ENEMY_START_Y bullet.x = 0 bullet.y = 0 # increase bullet speed BULLET_MOVE_SPEED += 1 # increase enemy speed ENEMY_MOVE_SPEED += 1 # Check if the enemy has hit the player if enemy.colliderect(player): running = False # Draw the game window.fill(BLACK) pygame.draw.rect(window, WHITE, player) pygame.draw.rect(window, RED, enemy) pygame.draw.rect(window, GREEN, bullet) # Update the game pygame.display.update() # limit the game to 60 frames per second clock = pygame.time.Clock() clock.tick(60) # output time and score print("Time: " + str(pygame.time.get_ticks() - time_started)) print("Score: " + str(score)) print("Shots Taken: " + str(shots_taken)) pygame.quit()

+--------------------------------------------------+ | | | This is room 1 | | | +--------------------------------------------------+ Your health: 100 Monsters in this room: Orc - Health: 100 Exits: n e Commands: look, quit, fight, spawn, n, s, e, w Your health: [==========] 100 :

import torch import torch.nn as nn from transformers import GPT2Tokenizer, GPT2LMHeadModel # Load the GPT-2 model and tokenizer model_name = 'gpt2' tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) # Generate some sample text prompt = "The quick brown fox jumps over the lazy dog." input_ids = tokenizer.encode(prompt, return_tensors='pt') output = model.generate(input_ids, max_length=100, do_sample=True, num_return_sequences=1) sample_text = tokenizer.decode(output[0], skip_special_tokens=True) print("Sample text: ", sample_text) # Quantize the model down quantization_config = { 'activation': { 'dtype': torch.quint8, 'qscheme': torch.per_tensor_affine, 'reduce_range': True }, 'weight': { 'dtype': torch.qint8, 'qscheme': torch.per_tensor_symmetric, 'reduce_range': True } } quantized_model = torch.quantization.quantize_dynamic(model, quantization_config, dtype=torch.qint8) # Save the quantized model torch.save(quantized_model.state_dict(), 'quantized_model.pt') ## ------------------------------- Generate using Quantized Model # Load the quantized model quantized_model = GPT2LMHeadModel.from_pretrained(model_name) quantized_model.load_state_dict(torch.load('quantized_model.pt')) # Generate some sample text using the quantized model output_quantized = quantized_model.generate(input_ids, max_length=100, do_sample=True, num_return_sequences=1) sample_text_quantized = tokenizer.decode(output_quantized[0], skip_special_tokens=True) print("Sample text (quantized): ", sample_text_quantized)

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