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

Note: updated on 2023-11-10 to work with the current version of syncdoth/RetNet.

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
3.6853	10.0	370	3.4459
2.1973	20.0	740	2.0213
1.3819	30.0	1110	1.3017
1.1658	40.0	1480	1.1566

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1