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