Add Plant DNAGPT model

README.md

@@ -1,3 +1,74 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: AAAAGCGACATGACCAAACTGCCCCTCACCCGCCGCACTGATGACCGA
+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 OpenAI GPT-2 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:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = 'plant-dnagpt'
+# 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).

added_tokens.json

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+{
+  "<|endoftext|>": 8000,
+  "[PAD]": 8001
+}

config.json

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+{
+  "_name_or_path": "plantdna_gpt2v1",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 8000,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 8000,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 512,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 8002
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 8000,
+  "eos_token_id": 8000,
+  "transformers_version": "4.35.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0c0dbfd78ba232831d71fa7f41e809d10eb452e4a5a0297eb8064cfe7475187
+size 367966848

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "8000": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8001": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "max_length": 512,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "stride": 0,
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>"
+}