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---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
base_model:
- black-forest-labs/FLUX.1-dev
---
#This prompt is from message 2. #The goal is to generate 100 messages per prompt.
prompt2 = "Vaping is risky"
#Below, we specify to use pytorch machine learning framework.
#You can also choose Tensorflow, but we use Pytorch here.
inputs = tokenizer(prompt2, return_tensors="pt")
---
#We generate 50 messages each time due to restrictions in Ram storage.
sample_outputs = bloom.generate(inputs["input_ids"],
temperature = 0.7,
max_new_tokens = 60,
do_sample=True,
top_k=40,
top_p=0.9,
num_return_sequences=50
)
print("Output:\n" + 100 * '-')
messages = []
for i, sample_output in enumerate(sample_outputs):
generated_messages = tokenizer.decode(sample_output, skip_special_tokens=True)
print("{}: {}".format(i, generated_messages))
messages.append(generated_messages)
print(messages)
---
#We save the AI-generated messages to google drive.
AI_messages = pd.DataFrame(messages, columns = ['tweet'])
AI_messages.to_csv('Vaping is risky1.csv', index = False)
---
#Then generate another 50 messages with prompt1 and then save to google drive.
AI_messages = pd.DataFrame(messages, columns = ['tweet'])
AI_messages.to_csv('Vaping is risky2.csv', index = False)
---
#This prompt is from message 3. #The goal is to generate 100 messages per prompt.
prompt3 = "Vapes and e-cigarettes increase your risk"
#Below, we specify to use pytorch machine learning framework.
#You can also choose Tensorflow, but we use Pytorch here.
inputs = tokenizer(prompt3, return_tensors="pt")
---
#We generate 50 messages each time due to restrictions in Ram storage.
sample_outputs = bloom.generate(inputs["input_ids"],
temperature = 0.7,
max_new_tokens = 60,
do_sample=True,
top_k=40,
top_p=0.9,
num_return_sequences=50
)
print("Output:\n" + 100 * '-')
messages = []
for i, sample_output in enumerate(sample_outputs):
generated_messages = tokenizer.decode(sample_output, skip_special_tokens=True)
print("{}: {}".format(i, generated_messages))
messages.append(generated_messages)
print(messages)
---
#We save the AI-generated messages to google drive.
AI_messages = pd.DataFrame(messages, columns = ['tweet'])
AI_messages.to_csv('Vapes and e-cigarettes increase your risk1.csv', index = False) |