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license: mit

Nano-Butterfly Model

Welcome to the Alexander27/Nano-Butterfly model card! This is a Causal Language Model trained using Hugging Face AutoTrain.

🚀 How to Use

You can easily run this model using the transformers library.

1. Installation

First, make sure you have the required libraries installed.

pip install transformers torch

2. Run the Model in Python

Save the following code as a Python file (e.g., app.py) and run it.

from transformers import AutoTokenizer, AutoModelForCausalLM

# The name of your model on the Hugging Face Hub
model_name = "Alexander27/Nano-Butterfly"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the prompt
prompt = "The future of artificial intelligence is "

# Prepare the input for the model
input_ids = tokenizer.encode(prompt, return_tensors="pt")

# Generate text
output_sequences = model.generate(
    input_ids=input_ids,
    max_length=100,
    num_return_sequences=1
)

# Decode the output and print it
generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)

print(generated_text)

Alternative in python:

File: app.py

1. Install necessary libraries

In your terminal, run: pip install transformers torch

from transformers import AutoTokenizer, AutoModelForCausalLM

The name of your model on the Hugging Face Hub

model_name = "Alexander27/Nano-Butterfly"

2. Load the tokenizer and model

print(f"Loading model: {model_name}") tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) print("Model loaded successfully!")

3. Define the prompt (the input text for the model)

prompt = "The future of artificial intelligence is "

4. Prepare the input for the model

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

5. Generate text

max_length controls how long the output will be

output_sequences = model.generate( input_ids=input_ids, max_length=100, num_return_sequences=1 )

6. Decode the output and print it

generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)

print("\n--- Model Output ---") print(generated_text)

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