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README.md

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----
-license: mit
----
+# GENERator-eukaryote-3b-base model
+
+## Abouts
+In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 3B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. The extensive and diverse pre-training data endow the GENERator with enhanced understanding and generation capabilities across various organisms.
+
+For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://huggingface.co/GenerTeam).
+
+## How to use
+### Simple example1:  generation
+
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model.
+tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-3b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-3b-base")
+config = model.config
+
+max_length = config.max_position_embeddings
+
+# Define input sequences.
+sequences = [
+    "ATGAGGTGGCAAGAAATGGGCTAC",
+    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
+]
+
+# Process the sequences
+sequences = [tokenizer.bos_token + sequence for sequence in sequences]
+
+# Tokenize the sequences
+tokenizer.padding_side = "left"
+inputs = tokenizer(
+    sequences,
+    add_special_tokens=False,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=max_length
+)
+
+# Generate the sequences
+with torch.inference_mode():
+    outputs = model.generate(**inputs, max_new_tokens=32)
+
+# Decode the generated sequences
+decoded_sequences = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+# Print the decoded sequences
+print(decoded_sequences)
+```
+
+### Simple example2: embedding
+
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model.
+tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-3b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-3b-base")
+
+config = model.config
+max_length = config.max_position_embeddings
+
+# Define input sequences.
+sequences = [
+    "ATGAGGTGGCAAGAAATGGGCTAC",
+    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
+]
+
+# Tokenize the sequences with add_special_tokens=True to automatically add special tokens,
+# such as the BOS EOS token, at the appropriate positions.
+tokenizer.padding_side = "right"
+inputs = tokenizer(
+    sequences,
+    add_special_tokens=True,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=max_length
+)
+
+# Perform a forward pass through the model to obtain the outputs, including hidden states.
+with torch.inference_mode():
+    outputs = model(**inputs, output_hidden_states=True)
+
+# Retrieve the hidden states from the last layer.
+hidden_states = outputs.hidden_states[-1]  # Shape: (batch_size, sequence_length, hidden_size)
+
+# Use the attention_mask to determine the index of the last token in each sequence.
+# Since add_special_tokens=True is used, the last token is typically the EOS token.
+attention_mask = inputs["attention_mask"]
+last_token_indices = attention_mask.sum(dim=1) - 1  # Index of the last token for each sequence
+
+# Extract the embedding corresponding to the EOS token for each sequence.
+seq_embeddings = []
+for i, token_index in enumerate(last_token_indices):
+    # Fetch the embedding for the last token (EOS token).
+    seq_embedding = hidden_states[i, token_index, :]
+    seq_embeddings.append(seq_embedding)
+
+# Stack the embeddings into a tensor with shape (batch_size, hidden_size)
+seq_embeddings = torch.stack(seq_embeddings)
+
+print("Sequence Embeddings:", seq_embeddings)
+
+```
+
+## Citation
+TBD

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8448,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "vocab_size": 4128
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.44.0"
+}

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+  }
+}

special_tokens_map.json

@@ -0,0 +1,12 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": {
+    "content": "<oov>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.py

@@ -0,0 +1,137 @@
+import itertools
+import re
+from typing import List, Optional, Tuple
+
+from transformers import PreTrainedTokenizer
+
+
+class DNAKmerTokenizer(PreTrainedTokenizer):
+    def __init__(self, k, **kwargs):
+        self.k = k
+        self.special_tokens = [
+            "<oov>",
+            "<s>",
+            "</s>",
+            "<pad>",
+            "<mask>",
+            "<bog>",
+            "<eog>",
+            "<bok>",
+            "<eok>",
+            "<+>",
+            "<->",
+            "<mam>",
+            "<vrt>",
+            "<inv>",
+            "<pln>",
+            "<fng>",
+            "<prt>",
+            "<cds>",
+            "<pseudo>",
+            "<tRNA>",
+            "<rRNA>",
+            "<ncRNA>",
+            "<misc_RNA>",
+            "<sp0>",
+            "<sp1>",
+            "<sp2>",
+            "<sp3>",
+            "<sp4>",
+            "<sp5>",
+            "<sp6>",
+            "<sp7>",
+            "<sp8>",
+        ]
+        self.kmers = [
+            "".join(kmer) for kmer in itertools.product("ATCG", repeat=self.k)
+        ]
+        self.vocab = {
+            token: i for i, token in enumerate(self.special_tokens + self.kmers)
+        }
+        self.ids_to_tokens = {v: k for k, v in self.vocab.items()}
+        self.special_token_pattern = re.compile(
+            "|".join(re.escape(token) for token in self.special_tokens)
+        )
+        self.dna_pattern = re.compile(f"[A-Z]{{{self.k}}}|[A-Z]+")
+        self.bos_token = "<s>"
+        self.eos_token = "</s>"
+        self.bos_token_id = self._convert_token_to_id(self.bos_token)
+        self.eos_token_id = self._convert_token_to_id(self.eos_token)
+        super().__init__(**kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab)
+
+    def _tokenize(self, text, **kwargs) -> List[str]:
+        tokens = []
+        pos = 0
+        while pos < len(text):
+            special_match = self.special_token_pattern.match(text, pos)
+            if special_match:
+                tokens.append(special_match.group())
+                pos = special_match.end()
+            else:
+                dna_match = self.dna_pattern.match(text, pos)
+                if dna_match:
+                    dna_seq = dna_match.group()
+                    tokens.append(dna_seq)
+                    pos = dna_match.end()
+                else:
+                    tokens.append(text[pos])
+                    pos += 1
+        return tokens
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self.vocab.get(token, self.vocab["<oov>"])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.ids_to_tokens.get(index, "<oov>")
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return "".join(tokens)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if token_ids_1 is None:
+            return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+        return (
+                [self.bos_token_id]
+                + token_ids_0
+                + [self.eos_token_id]
+                + token_ids_1
+                + [self.eos_token_id]
+        )
+
+    def get_special_tokens_mask(
+            self, token_ids_0, token_ids_1=None, already_has_special_tokens=False
+    ):
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0, token_ids_1, already_has_special_tokens=True
+            )
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+    def prepare_for_model(self, *args, **kwargs):
+        encoding = super().prepare_for_model(*args, **kwargs)
+        if "token_type_ids" in encoding:
+            del encoding["token_type_ids"]
+        return encoding
+
+    def save_vocabulary(
+            self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        import os
+
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
+        )
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                writer.write(token + "\n")
+        return (vocab_file,)

tokenizer_config.json

@@ -0,0 +1,50 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<oov>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "DNAKmerTokenizer",
+  "use_fast": false,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.DNAKmerTokenizer",
+      null
+    ]
+  },
+  "unk_token": "<oov>",
+  "use_default_system_prompt": false,
+  "k": 6
+}

vocab.txt

@@ -0,0 +1,4128 @@
+<oov>
+<s>
+</s>
+<pad>
+<mask>
+<bog>
+<eog>
+<bok>
+<eok>
+<+>
+<->
+<mam>
+<vrt>
+<inv>
+<pln>
+<fng>
+<prt>
+<cds>
+<pseudo>
+<tRNA>
+<rRNA>
+<ncRNA>
+<misc_RNA>
+<sp0>
+<sp1>
+<sp2>
+<sp3>
+<sp4>
+<sp5>
+<sp6>
+<sp7>
+<sp8>
+AAAAAA
+AAAAAT
+AAAAAC
+AAAAAG
+AAAATA
+AAAATT
+AAAATC
+AAAATG
+AAAACA
+AAAACT
+AAAACC
+AAAACG
+AAAAGA
+AAAAGT
+AAAAGC
+AAAAGG
+AAATAA
+AAATAT
+AAATAC
+AAATAG
+AAATTA
+AAATTT
+AAATTC
+AAATTG
+AAATCA
+AAATCT
+AAATCC
+AAATCG
+AAATGA
+AAATGT
+AAATGC
+AAATGG
+AAACAA
+AAACAT
+AAACAC
+AAACAG
+AAACTA
+AAACTT
+AAACTC
+AAACTG
+AAACCA
+AAACCT
+AAACCC
+AAACCG
+AAACGA
+AAACGT
+AAACGC
+AAACGG
+AAAGAA
+AAAGAT
+AAAGAC
+AAAGAG
+AAAGTA
+AAAGTT
+AAAGTC
+AAAGTG
+AAAGCA
+AAAGCT
+AAAGCC
+AAAGCG
+AAAGGA
+AAAGGT
+AAAGGC
+AAAGGG
+AATAAA
+AATAAT
+AATAAC
+AATAAG
+AATATA
+AATATT
+AATATC
+AATATG
+AATACA
+AATACT
+AATACC
+AATACG
+AATAGA
+AATAGT
+AATAGC
+AATAGG
+AATTAA
+AATTAT
+AATTAC
+AATTAG
+AATTTA
+AATTTT
+AATTTC
+AATTTG
+AATTCA
+AATTCT
+AATTCC
+AATTCG
+AATTGA
+AATTGT
+AATTGC
+AATTGG
+AATCAA
+AATCAT
+AATCAC
+AATCAG
+AATCTA
+AATCTT
+AATCTC
+AATCTG
+AATCCA
+AATCCT
+AATCCC
+AATCCG
+AATCGA
+AATCGT
+AATCGC
+AATCGG
+AATGAA
+AATGAT
+AATGAC
+AATGAG
+AATGTA
+AATGTT
+AATGTC
+AATGTG
+AATGCA
+AATGCT
+AATGCC
+AATGCG
+AATGGA
+AATGGT
+AATGGC
+AATGGG
+AACAAA
+AACAAT
+AACAAC
+AACAAG
+AACATA
+AACATT
+AACATC
+AACATG
+AACACA
+AACACT
+AACACC
+AACACG
+AACAGA
+AACAGT
+AACAGC
+AACAGG
+AACTAA
+AACTAT
+AACTAC
+AACTAG
+AACTTA
+AACTTT
+AACTTC
+AACTTG
+AACTCA
+AACTCT
+AACTCC
+AACTCG
+AACTGA
+AACTGT
+AACTGC
+AACTGG
+AACCAA
+AACCAT
+AACCAC
+AACCAG
+AACCTA
+AACCTT
+AACCTC
+AACCTG
+AACCCA
+AACCCT
+AACCCC
+AACCCG
+AACCGA
+AACCGT
+AACCGC
+AACCGG
+AACGAA
+AACGAT
+AACGAC
+AACGAG
+AACGTA
+AACGTT
+AACGTC
+AACGTG
+AACGCA
+AACGCT
+AACGCC
+AACGCG
+AACGGA
+AACGGT
+AACGGC
+AACGGG
+AAGAAA
+AAGAAT
+AAGAAC
+AAGAAG
+AAGATA
+AAGATT
+AAGATC
+AAGATG
+AAGACA
+AAGACT
+AAGACC
+AAGACG
+AAGAGA
+AAGAGT
+AAGAGC
+AAGAGG
+AAGTAA
+AAGTAT
+AAGTAC
+AAGTAG
+AAGTTA
+AAGTTT
+AAGTTC
+AAGTTG
+AAGTCA
+AAGTCT
+AAGTCC
+AAGTCG
+AAGTGA
+AAGTGT
+AAGTGC
+AAGTGG
+AAGCAA
+AAGCAT
+AAGCAC
+AAGCAG
+AAGCTA
+AAGCTT
+AAGCTC
+AAGCTG
+AAGCCA
+AAGCCT
+AAGCCC
+AAGCCG
+AAGCGA
+AAGCGT
+AAGCGC
+AAGCGG
+AAGGAA
+AAGGAT
+AAGGAC
+AAGGAG
+AAGGTA
+AAGGTT
+AAGGTC
+AAGGTG
+AAGGCA
+AAGGCT
+AAGGCC
+AAGGCG
+AAGGGA
+AAGGGT
+AAGGGC
+AAGGGG
+ATAAAA
+ATAAAT
+ATAAAC
+ATAAAG
+ATAATA
+ATAATT
+ATAATC
+ATAATG
+ATAACA
+ATAACT
+ATAACC
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+TCTTCT
+TCTTCC
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+CGCCTT
+CGCCTC
+CGCCTG
+CGCCCA
+CGCCCT
+CGCCCC
+CGCCCG
+CGCCGA
+CGCCGT
+CGCCGC
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