T5LAA2

This model is part of the work published in the paper Interactive Text Games: Lookahead Is All You Need!

Four models are introduced in the above paper:

• nanoGPTLA
• nanoGPTLAA
• nanoGPTLAA2
• nanoGPTLAE

These models are implemented in this repository which is a customized version of nanoGPT.

The same variations are also implemented in this fork of Transformers library, on top of Google-t5/T5 implementation. These models are also trained and published as follows:

• T5LA
• T5LAA
• T5LAA2
• T5LAE

All the above models are on the scale of GPT2 (~100M parameters). The work is in progress to train them on larger scales.

Model description

This model is not fine-tuned on any instruction or human feedback datasets. It is just pre-trained on the HuggingFaceFW/fineweb sample-10BT dataset. It achieves the following results on the evaluation set:

• Loss: 4.9746
• Accuracy: 0.0349

Since the above fork is not merged into the main Transformers library yet, if you need to load it with AutoModel.from_pretrained(), you need to first install Transformers from this branch, which contains the code for T5LA models. This can be done by:

pip install git+https://github.com/HRezaei/transformers.git@feature/lookahead_models

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 16
• total_eval_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• training_steps: 200000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.49.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0