Add Plant DNAmamba model for H3K27ac prediction
Browse files- README.md +50 -3
- config.json +1 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +35 -0
README.md
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---
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license: cc-by-nc-sa-4.0
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---
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license: cc-by-nc-sa-4.0
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widget:
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- text: AGTCGCCGCAACCCACACACGGACGGCTCGACGTGGCGATCTTAGCGGCTCATCCGCCCGGCCTCCCTCGCGCTCGATCGCTACGCAGCCTACGCTCGTTTCGCTCGGTTCGGTGGGTCGCCGATCTGGCGCCACGGCGGCTACCAACGACACCGCGATTGAGAAGGGTGCGTGGCCGTGGAGTCGTGGAGAAACGCCCGCGCGCGCGGGTGCGGCGAGGGACGACGACCGCGTCGTGCGGATCGATTGGCGGGGCAGCTCGGCGCCCCG
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tags:
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- DNA
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- biology
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- genomics
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---
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# Plant foundation DNA large language models
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The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
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All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.
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**Developed by:** zhangtaolab
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### Model Sources
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- **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
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- **Manuscript:** [Versatile applications of foundation DNA language models in plant genomes]()
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### Architecture
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The model is trained based on the State-Space Mamba-130m model with modified tokenizer specific for DNA sequence.
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This model is fine-tuned for predicting H3K27ac histone modification.
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### How to use
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Install the runtime library first:
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```bash
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pip install transformers
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pip install causal-conv1d<=1.2.0
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pip install mamba-ssm<2.0.0
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```
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Since `transformers` library (version < 4.43.0) does not provide a MambaForSequenceClassification function, we wrote a script to train Mamba model for sequence classification.
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An inference code can be found in our [GitHub](https://github.com/zhangtaolab/plant_DNA_LLMs).
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Note that Plant DNAMamba model requires NVIDIA GPU to run.
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### Training data
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We use a custom MambaForSequenceClassification script to fine-tune the model.
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Detailed training procedure can be found in our manuscript.
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#### Hardware
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Model was trained on a NVIDIA GTX4090 GPU (24 GB).
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config.json
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{"d_model": 768, "n_layer": 24, "vocab_size": 8000, "ssm_cfg": {}, "rms_norm": true, "residual_in_fp32": true, "fused_add_norm": true, "pad_vocab_size_multiple": 8, "tie_embeddings": true}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:cf3fd2bd5ef71267e59511155ea3b43ccd134764632054b1542a3365cdea8d0a
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size 386756122
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"0": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"max_length": 1024,
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"model_max_length": 512,
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"pad_to_multiple_of": null,
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"pad_token": "<|endoftext|>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"stride": 0,
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"tokenizer_class": "GPTNeoXTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<|endoftext|>"
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}
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