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README.md

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+---
+language: en
+license: mit
+tags:
+- text-generation
+- transformer
+- custom-model
+- pytorch
+datasets:
+- custom
+metrics:
+- perplexity
+widget:
+- text: "artificial intelligence"
+---
+
+# Custom Transformer Text Generation Model
+
+## Model Description
+
+This is a custom-built Transformer model trained from scratch for text generation tasks.
+
+### Model Architecture
+
+- **Model Type**: Transformer (Decoder-only)
+- **Parameters**: 397,572
+- **Embedding Dimension**: 128
+- **Number of Layers**: 2
+- **Attention Heads**: 4
+- **Vocabulary Size**: 4
+- **Context Length**: 128 tokens
+
+### Training Details
+
+- **Framework**: PyTorch
+- **Perplexity**: 3.76
+- **Training Data**: Custom corpus
+- **Optimizer**: Adam
+- **Loss Function**: Cross-Entropy Loss
+
+## Usage
+
+```python
+import torch
+import json
+
+# Load model configuration
+with open('model_config.json', 'r') as f:
+    config = json.load(f)
+
+# Load tokenizer
+with open('tokenizer.json', 'r') as f:
+    tokenizer_data = json.load(f)
+
+# Load model weights
+model = TransformerModel(**config)
+model.load_state_dict(torch.load('model_weights.pt'))
+model.eval()
+
+# Generate text
+def generate(prompt, max_length=50):
+    # Add your generation code here
+    pass
+
+text = generate("artificial intelligence")
+print(text)
+```
+
+## Limitations
+
+- Trained on limited custom data
+- May generate repetitive text
+- Context window limited to 128 tokens
+- Not fine-tuned for specific domains
+
+## Training Procedure
+
+Model was trained using:
+- Custom transformer architecture
+- Gradient clipping for stability
+- Learning rate scheduling
+- Dropout for regularization
+
+## Evaluation
+
+**Perplexity**: 3.76
+
+Lower perplexity indicates better performance. This model achieved a perplexity of 3.76 on the validation set.
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{custom-transformer-4,
+  author = {Your Name},
+  title = {Custom Transformer Model},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/YOUR-USERNAME/YOUR-MODEL-NAME}}
+}
+```
+
+## Contact
+
+For questions or feedback, please open an issue on the model repository.

example_usage.py

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+
+import torch
+import json
+
+# Load configuration
+with open('model_config.json', 'r') as f:
+    config = json.load(f)
+
+# Load tokenizer  
+with open('tokenizer.json', 'r') as f:
+    tokenizer_data = json.load(f)
+
+print("Model loaded successfully!")
+print(f"Vocabulary size: {config['vocab_size']}")
+print(f"Model dimensions: {config['d_model']}")

model_config.json

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+{
+  "vocab_size": 4,
+  "d_model": 128,
+  "num_heads": 4,
+  "num_layers": 2,
+  "d_ff": 1024,
+  "dropout": 0.1,
+  "max_len": 512
+}

model_weights.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf352bac08def50c4d2ff83b116b73b5f2750845f189cb6e6507e8f698f2191b
+size 1866227

requirements.txt

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+torch>=2.0.0
+numpy>=1.24.0

tokenizer.json

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+{
+  "word2idx": {
+    "<PAD>": 0,
+    "<UNK>": 1,
+    "<SOS>": 2,
+    "<EOS>": 3
+  },
+  "idx2word": {
+    "0": "<PAD>",
+    "1": "<UNK>",
+    "2": "<SOS>",
+    "3": "<EOS>"
+  },
+  "vocab_size": 10000,
+  "special_tokens": [
+    "<PAD>",
+    "<UNK>",
+    "<SOS>",
+    "<EOS>"
+  ]
+}