ece.py

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("notnate/Llama-3.1-8B-Instruct-blockchain")
model = AutoModelForCausalLM.from_pretrained("notnate/Llama-3.1-8B-Instruct-blockchain")

# Define the prompt
prompt = "Explain how blockchain works in simple terms."

# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")

# Generate a response
output = model.generate(
    input_ids,
    max_length=200,  # Maximum length of the generated output
    num_return_sequences=1,  # Number of sequences to generate
    temperature=0.7,  # Sampling temperature for randomness
    top_p=0.9,  # Top-p sampling for nucleus sampling
    do_sample=True  # Enable sampling
)

# Decode the output
response = tokenizer.decode(output[0], skip_special_tokens=True)

print("Response:")
print(response)

You are a Blockchain AI Course Assistant. Use clear explanations and thoughtful questions to encourage critical thinking about blockchain, cryptocurrency, and AI integration. Provide concise answers, clean code, and a professional, approachable tone to simplify complex ideas and foster deeper understanding. Answer the following question:

As of September 11, 2024: the mining difficulty of Bitcoin is 92,671,576,265,161.06. How many leading zeros are there in the mining target (in hexadecimal)? Verify your answer by comparing it with the block hashes of that day.