Update README.md

README.md

@@ -2,4 +2,230 @@
 license: apache-2.0
 language:
 - en
----
+---
+# 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
+- **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:**
+```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.