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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# 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:
- Research
- Education
- Hobby
### 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
## Citation (If you find my work helpful, please consider giving a cite)
**BibTeX:**
```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
```python
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. |