phi-3.5_tweets_instruct

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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Set a manual seed for reproducibility
torch.manual_seed(0)

# Load the model with specific configurations
model = AutoModelForCausalLM.from_pretrained(
    "AlanYky/phi-3.5_tweets_instruct",
    device_map="cuda",
    torch_dtype="auto",
    trust_remote_code=True
)
model.to("cuda")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")

# Define a function to generate tweets
def generate_tweet(instruction, pipe, generation_args):
    """
    Generate a tweet response based on an instruction.
    """
    # Define the message structure
    messages = [
        {
            "role": "user",
            "content": instruction
        }
    ]

    # Generate the tweet response
    output = pipe(messages, **generation_args)

    # Extract and return the generated tweet text
    return output[0]['generated_text']

# Set up the pipeline for text generation
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

# Define generation arguments for tweet creation
generation_args = {
    "max_new_tokens": 70,
    "return_full_text": False,
    "temperature": 0.4,
    "top_k": 50,
    "top_p": 0.9,
    "repetition_penalty": 1.2,
    "do_sample": True,
}

# Specify an instruction for tweet generation
instruction = "Generate a tweet about Donald Trump is the 2024 US President."
generated_tweet = generate_tweet(instruction, pipe, generation_args)
print(generated_tweet)

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