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--- |
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license: mit |
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datasets: |
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- wikimedia/wikipedia |
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language: |
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- en |
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metrics: |
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- bleu |
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- rouge |
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library_name: adapter-transformers |
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pipeline_tag: reinforcement-learning |
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tags: |
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- code |
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--- |
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# Super Large Language Model |
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This project implements a super-large language model using PyTorch. The model architecture is based on the Transformer model. |
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## Files |
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- `super_large_language_model.py`: Contains the model architecture. |
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- `train.py`: Contains the training script. |
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## Requirements |
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- Python 3.7+ |
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- PyTorch 1.6+ |
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- NumPy |
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## Installation |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/yourusername/super-large-language-model.git |
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cd super-large-language-model |
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``` |
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2. Install the required packages: |
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```bash |
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pip install torch numpy |
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``` |
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## Usage |
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1. Prepare your dataset and vocabulary. |
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2. Run the training script: |
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```bash |
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python train.py |
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``` |
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## Model Architecture |
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**Type**: Transformer |
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**Style**: Encoder-Decoder |
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The model is a Transformer-based language model. It consists of: |
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- An embedding layer for converting input tokens to vectors. |
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- Positional encoding to inject information about the position of tokens. |
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- A series of Transformer layers. |
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- A final linear layer for outputting the predictions. |
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## Training |
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The training script trains the model on a dataset of texts. The dataset should be a list of strings, and the vocabulary should be a dictionary mapping characters to indices. |
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## License |
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This project is licensed under the MIT License. |
