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