Add Plant DNAMamba model for lncRNAs prediction

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: ATGCTTTGTGCTGGCATGCCATGTCATGTTGCATCAGCATTTTCTTTATATTTTCTTTCTGATCTTTTCTGTGCTTCAAAACCTCATTCGTCTGTTTCCTTCTTTCCTACCAGTTATCCACAGACACACCCTATTAGAGTACTCCATGCTTGTTTATTTCTTTTGTCAAATAGAAGGGTCTTTTCTCCTCGCTTTAGTAGGGAATGTTGTCTTCCTCATTTGGGAAAAAAAAATTGTTCCTGCAGTTATGCCAGTCATGGGCTCTTTTTGATTGGTTGCATTGATATATTGTCTACCCCGTTTTCTGTAGGAATGATACATATTCCTGATCCTGAGCCTATTTGA
+tags:
+- DNA
+- biology
+- genomics
+---
+# Plant foundation DNA large language models
+
+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.  
+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.  
+
+
+**Developed by:** zhangtaolab
+
+### Model Sources
+
+- **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
+- **Manuscript:** [Versatile applications of foundation DNA large language models in plant genomes]() 
+
+### Architecture
+
+The model is trained based on the State-Space Mamba-130m model with modified tokenizer specific for DNA sequence.
+
+This model is fine-tuned for predicting lncRNAs.
+
+
+### How to use
+
+Install the runtime library first:
+```bash
+pip install transformers
+pip install causal-conv1d<=1.2.0
+pip install mamba-ssm<2.0.0
+```
+
+Since `transformers` library (version < 4.43.0) does not provide a MambaForSequenceClassification function, we wrote a script to train Mamba model for sequence classification.  
+An inference code can be found in our [GitHub](https://github.com/zhangtaolab/plant_DNA_LLMs).  
+Note that Plant DNAMamba model requires NVIDIA GPU to run.
+
+
+### Training data
+We use a custom MambaForSequenceClassification script to fine-tune the model.  
+Detailed training procedure can be found in our manuscript.
+
+
+#### Hardware
+Model was trained on a NVIDIA GTX4090 GPU (24 GB).

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}

pytorch_model.bin

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+size 386756122

special_tokens_map.json

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+  "bos_token": {
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+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "max_length": 1024,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|endoftext|>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "stride": 0,
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>"
+}