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Ramikan-BR/Qwen2-0.5B-v30
Qwen2-0.5B-v30 is a text generation model from Ramikan-BR. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
1 - ['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n Input:\nContinue the fibonnaci sequence.\n\n Outp…
Downloads · 30 days
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.gguf1.9 GB · 49%
From the Hugging Face model README
1 - ['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811']
2 - Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
What is fibonacci sequence?
The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence goes like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46374, 75031, 121305, 196600, 317905, 514511, 832416, 1347927, 2180343, 3528260, 5705503, 9233763, 14949263,
3 - Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
crie o jogo da cobrinha completo.
import random
class Game:
def __init__(self):
self.board = []
self.player = 'X'
self.player_score = 0
self.computer_score = 0
def play(self):
while True:
self.board = [['.' for _ in range(3)] for _ in range(3)]
self.player = 'X'
self.computer_score = 0
self.player_score = 0
for _ in range(3):
for _ in range(3):
self.board[_][_] = '.'
print(self.board)
while True:
user_input = input('Player {}, pick a cell: '.format(self.player))
if user_input in self.board:
self.board[user_input] = self.player
self.player = 'O' if self.player == 'X' else 'X'
self.player_score += 1
if self.player_score == 9:
print('Player {} wins!'.format(self.player))
return
else:
print('Invalid input. Try again.')
while True:
computer_input = random.choice(self.board)
if computer_input == '.':
print('Computer picks {}.'.format(computer_input))
break
self.board[computer_input] = self.computer_score
self.computer_score += 1
if self.computer_score == 9:
print('Computer wins!'.format(self.computer_score))
return
if self.player == 'X' and self.computer_score == 9:
print('It is a draw!')
return
if self.player == 'O' and self.computer_score == 9:
print('Player {} wins!'.format(self.player))
return
if __name__ == '__main__':
game = Game()
game.play()<|endoftext|>
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.