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README.md
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license: mit
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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.
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