---
base_model: ''
tags:
- generated_from_trainer
model-index:
- name: retnet-mini-shakespeare
  results: []
pipeline_tag: text-generation
---

# retnet-mini-shakespeare

This model was trained from scratch on "tinyshakespeare" text file.

## Model description

A tiny model similar to jploski/falcon-mini-shakespeare, to demonstrate training and recurrent inference using a retentive network (https://arxiv.org/pdf/2307.08621.pdf).
The code utilizes Sehyun Choi's implementation of retentive network (https://github.com/syncdoth/RetNet) with configuration parameters changed to make it a very tiny model.

- **License:** Apache 2.0.

## Intended uses & limitations

Intended to demonstrate training and (recurrent O(1)) inference using a retentive network

## Training and evaluation data

https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt

## Training procedure

Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:

https://colab.research.google.com/drive/1wZnM7FCe4TsQpoamJ7NDAuQfA3DYiwHi?usp=sharing

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3901        | 9.93  | 370  | 4.1523          |
| 3.8122        | 19.87 | 740  | 3.3425          |
| 3.1609        | 29.8  | 1110 | 2.8916          |
| 2.8352        | 39.73 | 1480 | 2.7718          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3