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---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
```python
<s> Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Input:
Write a code to create the snake game
### Output:
```python
import pygame
import random
# Initialize the pygame library
pygame.init()
# Set the width and height of the game window
width = 600
height = 600
# Set the title of the game window
pygame.display.set_caption("Snake Game")
# Create a white background surface
background = pygame.Surface((width, height))
background.fill((255, 255, 255))
# Create a snake object
snake = []
snake.append([width // 2, height // 2])
# Create a food object
food = [random.randint(0, width - 1), random.randint(0, height - 1)]
# Create a clock object to control the game speed
clock = pygame.time.Clock()
# Game loop
running = True
while running:
# Handle events
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
# Move the snake
keys = pygame.key.get_pressed()
if keys[pygame.K_LEFT]:
snake.append([snake[-1][0] - 10, snake[-1][1]])
elif keys[pygame.K_RIGHT]:
snake.append([snake[-1][0] + 10, snake[-1][1]])
elif keys[pygame.K_UP]:
snake.append([snake[-1][0], snake[-1][1] - 10])
elif keys[pygame.K_DOWN]:
snake.append([snake[-1][0], snake[-1][1] + 10])
# Check if the snake has eaten the food
if snake[-1] == food:
food = [random.randint(0, width - 1), random.randint(0, height - 1)]
# Check if the snake has hit a wall or itself
if snake[-1][0] < 0 or snake[-1][0] >= width or snake[-1][1] < 0 or snake[-1][1] >= height or snake[-1] in snake[:-1]:
running = False
# Update the game window
pygame.display.update()
clock.tick(10)
# Quit the pygame library
pygame.quit()
```</s>
````
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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