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