The trained model is provisionally placed in the following repository: https://huggingface.co/MasaakiKotera/GenerRNA/tree/main
GenerRNA is a generative RNA language model based on a Transformer decoder-only architecture. It was pre-trained on 30M sequences, encompassing 17B nucleotides.
Here, you can find all the relevant scripts for running GenerRNA on your machine. GenerRNA enable you to generate RNA sequences in a zero-shot manner for exploring the RNA space, or to fine-tune the model using a specific dataset for generating RNAs belonging to a particular family or possessing specific characteristics.
A CUDA environment, and a minimum VRAM of 8GB was required.
torch>=2.0
numpy
transformers==4.33.0.dev0
datasets==2.14.4
tqdm
Firstly, combine the split model using the command cat model.pt.part-* > model.pt.recombined
.
├── LICENSE
├── README.md
├── configs
│ ├── example_finetuning.py
│ └── example_pretraining.py
├── experiments_data
├── model.pt.part-aa # splited bin data of pre-trained model
├── model.pt.part-ab
├── model.pt.part-ac
├── model.pt.part-ad
├── model.py # define the architecture
├── sampling.py # script to generate sequences
├── tokenization.py # preparete data
├── tokenizer_bpe_1024
│ ├── tokenizer.json
│ ├── ....
├── train.py # script for training/fine-tuning
Usage example:
python sampling.py \
--out_path {output_file_path} \
--max_new_tokens 256 \
--ckpt_path {model.pt} \
--tokenizer_path {path_to_tokenizer_directory, e.g /tokenizer_bpe_1024}
First, tokenize your sequence data, ensuring each sequence is on a separate line and there is no header.
python tokenization.py \
--data_dir {path_to_the_directory_containing_sequence_data} \
--file_name {file_name_of_sequence_data} \
--tokenizer_path {path_to_tokenizer_directory} \
--out_dir {directory_to_save_tokenized_data} \
--block_size 256
Next, refer to ./configs/example_**.py to create a config file of GPT model.
Lastly, excute following command:
python train.py \
--config {path_to_your_config_file}
Usage example:
python train_BPE.py \
--txt_file_path {path_to_training_file(txt,each sequence is on a separate line)} \
--vocab_size 50256 \
--new_tokenizer_path {directory_to_save_trained_tokenizer} \
The source code is licensed MIT. See LICENSE
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