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README.md

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----
-license: cc-by-nc-sa-4.0
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+---
+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": 11, "ssm_cfg": {}, "rms_norm": true, "residual_in_fp32": true, "fused_add_norm": true, "pad_vocab_size_multiple": 1, "tie_embeddings": true}

pytorch_model.bin

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+oid sha256:505cb1e38bea388b5058d80886521c47a10cfd77501ca845240fbafbfec25867
+size 362213914

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "<cls>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test_metrics.json

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+{'test_loss': 0.439256876707077, 'test_accuracy': 0.8060413354531002, 'test_f1': 0.8361316319677636, 'test_precision': 0.7962903741605373, 'test_recall': 0.88016967126193, 'test_matthews_correlation': 0.6045237630866616, 'test_runtime': 15.8565, 'test_samples_per_second': 317.345, 'test_steps_per_second': 19.866}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<cls>",
+  "eos_token": null,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "tokenizer_class": "EsmTokenizer",
+  "unk_token": "<unk>"
+}

vocab.txt

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+<unk>
+<pad>
+<mask>
+<cls>
+A
+T
+C
+G
+N
+<eos>
+<bos>