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--- |
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license: apache-2.0 |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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tags: |
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- generated_from_trainer |
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datasets: |
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- monsoon-nlp/greenbeing-proteins |
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- SciPhi/textbooks-are-all-you-need-lite |
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--- |
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# tinyllama-mixpretrain-quinoa-sciphi |
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TinyLLaMA model with continued pretraining / full-model finetuning on amino acids and simulated science textbooks. |
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The goal is to a create models which understand amino acid sequences and natural language descriptions or Q&A. |
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Training data was shuffled with: |
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- 50% amino acid sequences / proteins from the [GreenBeing](https://huggingface.co/datasets/monsoon-nlp/greenbeing-proteins) research dataset (mostly quinoa) |
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- 50% textbook content from the [SciPhi](https://huggingface.co/datasets/SciPhi/textbooks-are-all-you-need-lite) training dataset |
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## Training procedure |
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CoLab notebook: https://colab.research.google.com/drive/1dah43byt-T0HQC9eCigNbxSZ8aHu6s-W?usp=sharing |
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To fit on an L4 GPU, it was necessary to use max_length=400 and train_batch_size=1 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 15000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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