Add Plant DNAMamba model

README.md

@@ -1,3 +1,75 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: AAAAGCGACATGACCAAACTGCCCCTCACCCGCCGCACTGATGACCGA
+inference: false
+tags:
+- DNA
+- biology
+- genomics
+datasets:
+- zhangtaolab/plant_reference_genomes
+---
+# 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 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.
+
+### How to use
+
+Install the runtime library first:
+```bash
+pip install transformers
+```
+
+Here is a simple code for inference (Note that Mamba model requires NVIDIA GPU for inference):
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = 'plant-dnamamba'
+# load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+
+# example sequence and tokenization
+sequences = ['ATATACGGCCGNC','GGGTATCGCTTCCGAC']
+tokens = tokenizer(sequences,padding="longest")['input_ids']
+print(f"Tokenzied sequence: {tokenizer.batch_decode(tokens)}")
+
+# inference
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+inputs = tokenizer(sequences, truncation=True, padding='max_length', max_length=512, 
+                   return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+outs = model(
+    **inputs,
+    output_hidden_states=True
+)
+
+# get the final layer embeddings and prediction logits
+embeddings = outs['hidden_states'][-1].detach().numpy()
+logits = outs['logits'].detach().numpy()
+```
+
+
+### Training data
+We use CausalLM method to pre-train the model, the tokenized sequence have a maximum length of 512.  
+Detailed training procedure can be found in our manuscript.
+
+
+#### Hardware
+Model was pre-trained on a NVIDIA RTX4090 GPU (24 GB).

config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "Plant_DNAMamba",
+  "architectures": [
+    "MambaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "conv_kernel": 4,
+  "d_inner": 1536,
+  "d_model": 768,
+  "eos_token_id": 0,
+  "expand": 2,
+  "fused_add_norm": true,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.1,
+  "intermediate_size": 1536,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "mamba",
+  "n_layer": 24,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pad_vocab_size_multiple": 8,
+  "rescale_prenorm_residual": false,
+  "residual_in_fp32": true,
+  "rms_norm": true,
+  "ssm_cfg": {},
+  "state_size": 16,
+  "time_step_floor": 0.0001,
+  "time_step_init_scheme": "random",
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "time_step_rank": 48,
+  "time_step_scale": 1.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.1",
+  "use_bias": false,
+  "use_cache": true,
+  "use_conv_bias": true,
+  "vocab_size": 8000
+}

config.json.bak

@@ -0,0 +1,10 @@
+{
+    "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
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "pad_token_id": 0,
+  "transformers_version": "4.39.1"
+}

model.safetensors

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+size 386683304

pytorch_model.bin

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+size 386734482

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "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": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "stride": 0,
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>"
+}