metadata

license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
  - generated_from_trainer
datasets:
  - monsoon-nlp/greenbeing-proteins
  - SciPhi/textbooks-are-all-you-need-lite

tinyllama-mixpretrain-quinoa-sciphi

TinyLLaMA model with continued pretraining / full-model finetuning on amino acids and simulated science textbooks.

The goal is to a create models which understand amino acid sequences and natural language descriptions or Q&A.

Training data was shuffled with:

50% amino acid sequences / proteins from the GreenBeing research dataset (mostly quinoa)
50% textbook content from the SciPhi training dataset

Training procedure

CoLab notebook: https://colab.research.google.com/drive/1dah43byt-T0HQC9eCigNbxSZ8aHu6s-W?usp=sharing

To fit on an L4 GPU, it was necessary to use max_length=400 and train_batch_size=1

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 15000
mixed_precision_training: Native AMP

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.15.2