Model Card for Xylaria-1.4-smol

Model Details

Model Description

Xylaria-1.4-smol is a highly compact Recurrent Neural Network (RNN) with just 1 MB of storage and 2 million parameters. Designed for efficiency, this model represents a breakthrough in lightweight neural network architecture, optimized for resource-constrained environments.

• Developed by: Sk Md Saad Amin
• Model type: Recurrent Neural Network (RNN)
• Parameters: 2 million (approx)
• Storage Size: 1 MB
• Language(s): English
• License: Apache-2.0

Direct Use

Xylaria-1.4-smol is ideal for:

• Edge computing applications
• Mobile and IoT devices
• Low-resource environment deployments
• Real-time inference with minimal computational overhead

Downstream Use

The model can be fine-tuned for various tasks such as:

• Lightweight text generation
• Simple sequence prediction
• Embedded system applications
• Educational demonstrations of efficient neural network design

Out-of-Scope Use

• High-complexity natural language processing tasks
• Applications requiring extensive computational resources
• Tasks demanding state-of-the-art accuracy in complex domains
• It doesn't shine in tasks that are very heavy as this is made for educational and research purposes only

Bias, Risks, and Limitations

• Limited capacity due to compact design
• Potential performance trade-offs for complexity
• May not perform as well as larger models in nuanced tasks
• Has extremely small vocab size of 108

Recommendations

• Carefully evaluate performance for specific use cases
• Consider model limitations in critical applications
• Potential for transfer learning and fine-tuning

Model Architecture and Objective

• Architecture: Compact Recurrent Neural Network
• Objective: Efficient sequence processing
• Key Features:
  • Minimal parameter count
  • Reduced storage footprint
  • Low computational requirements

Hardware

• Suitable for:
  • Microcontrollers
  • Mobile devices
  • Edge computing platforms

Software

• Compatible with:
  • TensorFlow Lite
  • PyTorch Mobile
  • ONNX Runtime

Citation (If you find my work helpful, please consider giving a cite)

BibTeX:

@misc{xylaria2024smol,
  title={Xylaria-1.4-smol: A Compact Efficient RNN},
  author={[Your Name]},
  year={2024}
}

One Can include the xylaria code like this

import torch
import torch.nn as nn

class XylariaSmolRNN(nn.Module):
    def __init__(self, config):
        super(XylariaSmolRNN, self).__init__()
        
        
        self.vocab_size = config['vocab_size']
        self.embedding_dim = config['embedding_dim']
        self.hidden_dim = config['hidden_dim']
        self.num_layers = config['num_layers']
        self.char_to_idx = config['char_to_idx']
        
        
        self.embedding = nn.Embedding(
            num_embeddings=self.vocab_size, 
            embedding_dim=self.embedding_dim,
            padding_idx=self.char_to_idx['<PAD>']
        )
        
        
        self.rnn = nn.LSTM(
            input_size=self.embedding_dim, 
            hidden_size=self.hidden_dim, 
            num_layers=self.num_layers,
            batch_first=True
        )
        
        
        self.fc = nn.Linear(self.hidden_dim, self.vocab_size)
        
        
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        
        embedded = self.embedding(x)
        
        
        rnn_out, (hidden, cell) = self.rnn(embedded)
        
        
        rnn_out = self.dropout(rnn_out)
        
        
        output = self.fc(rnn_out)
        
        return output, (hidden, cell)

def demonstrate_xylaria_model():
    
    model_config = {
        "vocab_size": 108,
        "embedding_dim": 50,
        "hidden_dim": 128,
        "num_layers": 2,
        "char_to_idx": {" ": 1, "!": 2, "\"": 3, "#": 4, "$": 5, "%": 6, "&": 7, "'": 8, "(": 9, ")": 10, "*": 11, "+": 12, ",": 13, "-": 14, ".": 15, "/": 16, "0": 17, "1": 18, "2": 19, "3": 20, "4": 21, "5": 22, "6": 23, "7": 24, "8": 25, "9": 26, ":": 27, ";": 28, "<": 29, "=": 30, ">": 31, "?": 32, "A": 33, "B": 34, "C": 35, "D": 36, "E": 37, "F": 38, "G": 39, "H": 40, "I": 41, "J": 42, "K": 43, "L": 44, "M": 45, "N": 46, "O": 47, "P": 48, "Q": 49, "R": 50, "S": 51, "T": 52, "U": 53, "V": 54, "W": 55, "X": 56, "Y": 57, "Z": 58, "[": 59, "\\": 60, "]": 61, "^": 62, "_": 63, "a": 64, "b": 65, "c": 66, "d": 67, "e": 68, "f": 69, "g": 70, "h": 71, "i": 72, "j": 73, "k": 74, "l": 75, "m": 76, "n": 77, "o": 78, "p": 79, "q": 80, "r": 81, "s": 82, "t": 83, "u": 84, "v": 85, "w": 86, "x": 87, "y": 88, "z": 89, "{": 90, "}": 91, "°": 92, "²": 93, "à": 94, "á": 95, "æ": 96, "é": 97, "í": 98, "ó": 99, "ö": 100, "–": 101, "'": 102, "'": 103, """: 104, """: 105, "…": 106, "<PAD>": 0, "<UNK>": 107}
    }

    
    model = XylariaSmolRNN(model_config)

    
    total_params = sum(p.numel() for p in model.parameters())
    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    
    print(f"Total Parameters: {total_params}")
    print(f"Trainable Parameters: {trainable_params}")
    print(f"Model Size Estimate: {total_params * 4 / 1024 / 1024:.2f} MB")

    
    batch_size = 1
    sequence_length = 20
    x = torch.randint(0, model_config['vocab_size'], (batch_size, sequence_length))
    
    
    with torch.no_grad():
        output, (hidden, cell) = model(x)
        print("Model Output Shape:", output.shape)
        print("Hidden State Shape:", hidden.shape)
        print("Cell State Shape:", cell.shape)

    
    try:
        
        scripted_model = torch.jit.script(model)
        scripted_model.save("xylaria_smol_model.pt")
        print("Model exported for deployment")
    except Exception as e:
        print(f"Export failed: {e}")

    
    def generate_text(model, start_char, max_length=100):
        
        current_char = torch.tensor([[model.char_to_idx.get(start_char, model.char_to_idx['<UNK>'])]])
        
        
        hidden = None
        generated_text = [start_char]

        for _ in range(max_length - 1):
            with torch.no_grad():
                
                embedded = model.embedding(current_char)
                if hidden is None:
                    rnn_out, (hidden, cell) = model.rnn(embedded)
                else:
                    rnn_out, (hidden, cell) = model.rnn(embedded, (hidden, cell))
                
               
                output = model.fc(rnn_out)
                
                
                probabilities = torch.softmax(output[0, -1], dim=0)
                next_char_idx = torch.multinomial(probabilities, 1).item()
                
                
                idx_to_char = {idx: char for char, idx in model.char_to_idx.items()}
                next_char = idx_to_char.get(next_char_idx, '<UNK>')
                
                generated_text.append(next_char)
                current_char = torch.tensor([[next_char_idx]])

                if next_char == '<UNK>':
                    break

        return ''.join(generated_text)

    
    print("\nText Generation Example:")
    generated = generate_text(model, 'A')
    print(generated)

if __name__ == "__main__":
    demonstrate_xylaria_model()

PS: THE CODE MY BE A BIT WRONG SO, ADJUST ACCORDINGLY

More Information

Xylaria-1.4-smol represents a significant step towards ultra-efficient neural network design, demonstrating that powerful machine learning can be achieved with minimal computational resources.