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README.md CHANGED
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  ---
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  license: apache-2.0
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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  ---
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+
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+ # XGen
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+
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+ Official research release for the family of **XGen** models (`7B`) by Salesforce AI Research:
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+
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+ *Title*: [Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length](https://blog.salesforceairesearch.com/xgen/)
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+
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+ ## Models
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+
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+ * [XGen-7B-4K-Base](https://huggingface.co/Salesforce/xgen-7b-4k-base): Pretrained XGen-7B model trained under 4K sequence length.
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+ * License: Apache-2.0
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+ * [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base): Pretrained XGen-7B model trained under 8K sequence length.
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+ * License: Apache-2.0
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+
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+ The training data for the models are tokenized with OpenAI Tiktoken library.
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+ To use this model, install tiktoken library via `pip`:
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+
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+ ```sh
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+ pip install tiktoken
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+ ```
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+
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+ The models can be used as auto-regressive samplers as follows:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-4k-base", trust_remote_code=True)
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+ model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-4k-base", torch_dtype=torch.bfloat16)
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+ inputs = tokenizer("The world is", return_tensors="pt")
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+ sample = model.generate(**inputs, max_length=128)
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+ print(tokenizer.decode(sample[0]))
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+ ```
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{XGen,
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+ title={Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length},
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+ author={Salesforce AI Research},
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+ howpublished={Salesforce AI Research Blog},
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+ year={2023},
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+ url={https://blog.salesforceairesearch.com/xgen-7b/}
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+ }
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+ ```
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+ "LlamaForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "hidden_act": "silu",
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+ "model_type": "llama",
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+ "num_hidden_layers": 32,
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+ }
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+ }
330
+ }
tokenization_xgen.py ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Tokenization classes for xgen."""
2
+
3
+ from typing import List, Optional
4
+
5
+ import tiktoken
6
+
7
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
8
+ from transformers.utils import logging
9
+
10
+ logger = logging.get_logger(__name__)
11
+
12
+ MAX_MODEL_INPUT_SIZES = {
13
+ "Salesforce/xgen-7b-4k-base": 4096,
14
+ "Salesforce/xgen-7b-8k-base": 8192,
15
+ "Salesforce/xgen-7b-4k-inst": 4096,
16
+ "Salesforce/xgen-7b-8k-inst": 8192
17
+ }
18
+
19
+
20
+ def tiktoken_tokenizer(base="gpt2", add_special=True):
21
+ if not add_special:
22
+ return tiktoken.get_encoding(base)
23
+
24
+ def include_whitespace(n_min=2, n_max=20):
25
+ whitespaces = [" " * n for n in reversed(range(n_min, n_max))]
26
+ return whitespaces
27
+
28
+ def include_tabs(n_min=2, n_max=20):
29
+ tabs = ["\t" * n for n in reversed(range(n_min, n_max))]
30
+ return tabs
31
+
32
+ def include_fim_tokens():
33
+ fim_tokens = [
34
+ "<fim_prefix>",
35
+ "<fim_middle>",
36
+ "<fim_suffix>",
37
+ "<fim_pad>",
38
+ "<filename>",
39
+ "<gh_stars>",
40
+ "<issue_start>",
41
+ "<issue_comment>",
42
+ "<issue_closed>",
43
+ "<jupyter_start>",
44
+ "<jupyter_text>",
45
+ "<jupyter_code>",
46
+ "<jupyter_output>",
47
+ "<empty_output>",
48
+ "<commit_before>",
49
+ "<commit_msg>",
50
+ "<commit_after>",
51
+ "<reponame>"
52
+ ]
53
+ return fim_tokens
54
+
55
+ add_whitespaces = include_whitespace(n_min=2, n_max=32)
56
+ add_tabs = include_tabs(n_min=2, n_max=10)
57
+ fim_tokens = include_fim_tokens()
58
+
59
+ tokenizer = tiktoken.get_encoding(base)
60
+
61
+ idx = tokenizer.n_vocab
62
+
63
+ bpe_ranks = tokenizer._mergeable_ranks
64
+
65
+ for wsp in add_whitespaces:
66
+ bpe_ranks[bytes(wsp, 'ascii')] = idx
67
+ idx += 1
68
+ for t in add_tabs:
69
+ bpe_ranks[bytes(t, 'ascii')] = idx
70
+ idx += 1
71
+
72
+ special_tokens = dict()
73
+
74
+ for sp in fim_tokens:
75
+ special_tokens[sp] = idx
76
+ idx += 1
77
+
78
+ # In production, load the arguments directly instead of accessing private attributes
79
+ # See openai_public.py for examples of arguments for specific encodings
80
+ enc = tiktoken.Encoding(
81
+ # If you're changing the set of special tokens, make sure to use a different name
82
+ # It should be clear from the name what behaviour to expect.
83
+ name=base.replace("base", "im"),
84
+ pat_str=tokenizer._pat_str,
85
+ mergeable_ranks=bpe_ranks,
86
+ special_tokens={
87
+ **tokenizer._special_tokens,
88
+ **special_tokens
89
+ }
90
+ )
91
+ return enc
92
+
93
+
94
+ class XgenTokenizer(PreTrainedTokenizer):
95
+ """
96
+ Construct a Xgen tokenizer. Based on byte-level Byte-Pair-Encoding.
97
+ Args:
98
+ vocab_file (`str`):
99
+ Path to the vocabulary file.
100
+ """
101
+ max_model_input_sizes = MAX_MODEL_INPUT_SIZES
102
+ model_input_names = ["input_ids", "attention_mask"]
103
+
104
+ def __init__(
105
+ self,
106
+ pad_token=None,
107
+ add_eos_token=False,
108
+ add_special_tokens=True,
109
+ **kwargs,
110
+ ):
111
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
112
+ super().__init__(
113
+ pad_token=pad_token,
114
+ add_eos_token=add_eos_token,
115
+ add_special_tokens=add_special_tokens,
116
+ **kwargs,
117
+ )
118
+ self.add_eos_token = add_eos_token
119
+ self.encoder = tiktoken_tokenizer(base="gpt2", add_special=add_special_tokens)
120
+
121
+ @property
122
+ def vocab_size(self):
123
+ """Returns vocab size"""
124
+ return self.encoder.n_vocab
125
+
126
+ def get_vocab(self):
127
+ """Returns vocab as a dict"""
128
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
129
+ return vocab
130
+
131
+ def _tokenize(self, text, **kwargs):
132
+ """Returns a tokenized string."""
133
+ return self.encoder.encode(text, allowed_special="all")
134
+
135
+ def _convert_token_to_id(self, token):
136
+ """Converts a token (str) in an id using the vocab."""
137
+ return token
138
+
139
+ def _convert_id_to_token(self, index):
140
+ """Converts an index (integer) in a token (str) using the vocab."""
141
+ return self.encoder.decode_single_token_bytes(index)
142
+
143
+ def _decode(self, token_ids: List[int], skip_special_tokens: bool = False, **kwargs):
144
+ return self.encoder.decode(token_ids)
145
+
146
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
147
+ """Build model inputs from a sequence by appending eos_token_id."""
148
+ eos_token_id = [50256] if self.add_eos_token else []
149
+
150
+ output = token_ids_0 + eos_token_id
151
+
152
+ if token_ids_1 is not None:
153
+ output = output + token_ids_1 + eos_token_id
154
+
155
+ return output
156
+
157
+ def get_special_tokens_mask(
158
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None,
159
+ already_has_special_tokens: bool = False
160
+ ) -> List[int]:
161
+ """
162
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
163
+ special tokens using the tokenizer `prepare_for_model` method.
164
+ Args:
165
+ token_ids_0 (`List[int]`):
166
+ List of IDs.
167
+ token_ids_1 (`List[int]`, *optional*):
168
+ Optional second list of IDs for sequence pairs.
169
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
170
+ Whether the token list is already formatted with special tokens for the model.
171
+ Returns:
172
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
173
+ """
174
+ if already_has_special_tokens:
175
+ return super().get_special_tokens_mask(
176
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
177
+ )
178
+
179
+ eos_token_id = [1] if self.add_eos_token else []
180
+
181
+ if token_ids_1 is None:
182
+ return ([0] * len(token_ids_0)) + eos_token_id
183
+ return ([0] * len(token_ids_0)) + eos_token_id + ([0] * len(token_ids_1)) + eos_token_id
184
+
185
+ def create_token_type_ids_from_sequences(
186
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
187
+ ) -> List[int]:
188
+ """
189
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
190
+ sequence pair mask has the following format:
191
+ ```
192
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
193
+ | first sequence | second sequence |
194
+ ```
195
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
196
+ Args:
197
+ token_ids_0 (`List[int]`):
198
+ List of ids.
199
+ token_ids_1 (`List[int]`, *optional*):
200
+ Optional second list of IDs for sequence pairs.
201
+ Returns:
202
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
203
+ """
204
+ eos_token_id = [50256] if self.add_eos_token else []
205
+
206
+ output = [0] * len(token_ids_0 + eos_token_id)
207
+
208
+ if token_ids_1 is not None:
209
+ output += [1] * len(token_ids_1 + eos_token_id)
210
+
211
+ return output
tokenizer_config.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_eos_token": false,
3
+ "add_special_tokens": true,
4
+ "clean_up_tokenization_spaces": true,
5
+ "model_max_length": 1000000000000000019884624838656,
6
+ "pad_token": null,
7
+ "tokenizer_class": "XgenTokenizer",
8
+ "auto_map": {
9
+ "AutoTokenizer": ["tokenization_xgen.XgenTokenizer", null]
10
+ }
11
+ }