Update README.md

README.md

@@ -20,88 +20,3 @@ Contributions are welcome! Please fork the repository, make your changes, and su
 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.
-
-```bash
-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.
-
-```python
-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)