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)