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tangled-llama-l-128k-v0.1

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mtasic85 commited on
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c1e1c58
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1 Parent(s): 9eb0b88

tokenizer training

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Files changed (6) hide show
  1. merges.txt +0 -0
  2. scripts/train_tokenizer.py +18 -154
  3. special_tokens_map.json +0 -4
  4. tokenizer.json +0 -3
  5. tokenizer_config.json +0 -3706
  6. vocab.json +0 -0
merges.txt DELETED
The diff for this file is too large to render. See raw diff
 
scripts/train_tokenizer.py CHANGED
@@ -1,130 +1,11 @@
1
- import gc
2
- import re
3
-
4
- from datasets import load_dataset
5
  from transformers import PreTrainedTokenizerFast
6
  from tokenizers import Tokenizer, normalizers, pre_tokenizers, processors, decoders
7
  from tokenizers.models import BPE
8
  from tokenizers.trainers import BpeTrainer
9
 
 
10
  from pretrain_datasets import pretrain_datasets
11
 
12
- #
13
- # datasets
14
- #
15
- def batch_iterator():
16
-
17
-
18
- # text
19
- dataset = load_dataset('JeanKaddour/minipile', split='train')
20
-
21
- for text in dataset['text']:
22
- yield text
23
-
24
- del dataset
25
- gc.collect()
26
-
27
- #
28
- #
29
- #
30
-
31
- # code
32
- dataset = load_dataset('bigcode/programming-languages-keywords', split='train')
33
-
34
- for row in dataset:
35
- for n in row['keywords']:
36
- yield n
37
-
38
- del dataset
39
- gc.collect()
40
-
41
- # code
42
- dataset = (
43
- load_dataset('bigcode/the-stack-smol-xs', data_dir=f'data/{name}', split='train', trust_remote_code=True)
44
- for name in [
45
- 'batchfile',
46
- 'powershell',
47
- 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly',
48
- 'augeas', 'awk', 'bison', 'bluespec', 'c',
49
- 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp',
50
- 'css', 'cuda', 'dart', 'dockerfile', 'elixir',
51
- 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go',
52
- 'groovy', 'haskell','html', 'idris', 'isabelle', 'java',
53
- 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean',
54
- 'literate-agda', 'literate-coffeescript', 'literate-haskell',
55
- 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab',
56
- 'ocaml', 'pascal', 'perl', 'php', 'prolog',
57
- 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext',
58
- 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme',
59
- 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan',
60
- 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex',
61
- 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt',
62
- 'yacc', 'zig',
63
- ]
64
- )
65
-
66
- for d in dataset:
67
- for text in d['content']:
68
- yield text
69
-
70
- del dataset
71
- gc.collect()
72
-
73
- # math
74
- dataset = load_dataset('OleehyO/latex-formulas', 'cleaned_formulas', split='train')
75
-
76
- for text in dataset['latex_formula']:
77
- yield text
78
-
79
- del dataset
80
- gc.collect()
81
-
82
- # text
83
- dataset = (
84
- load_dataset('saillab/taco-datasets', data_dir=data_dir, split='train')
85
- for data_dir in [
86
- 'multilingual-instruction-tuning-dataset /multilingual-alpaca-52k-gpt-4',
87
- 'multilingual-instruction-tuning-dataset /multilinugal-dolly-15k',
88
- ]
89
- )
90
-
91
- for d in dataset:
92
- for row in d:
93
- for n in row:
94
- yield row['instruction'] + '\n' + row['input'] + '\n' + row['output']
95
-
96
- del dataset
97
- gc.collect()
98
-
99
- # text
100
- dataset = (
101
- load_dataset('xu-song/cc100-samples', lang, split='train')
102
- for lang in [
103
- 'en', 'hr', 'sr', 'ru',
104
- 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'bn_rom', 'br',
105
- 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'eo', 'es',
106
- 'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl',
107
- 'gn', 'gu', 'ha', 'he', 'hi', 'hi_rom', 'ht', 'hu',
108
- 'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km',
109
- 'kn', 'ko', 'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt',
110
- 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'my_zaw',
111
- 'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt',
112
- 'qu', 'rm', 'ro', 'sa', 'si', 'sc', 'sd', 'sk', 'sl',
113
- 'so', 'sq', 'ss', 'su', 'sv', 'sw', 'ta', 'ta_rom',
114
- 'te', 'te_rom', 'th', 'tl', 'tn', 'tr', 'ug', 'uk', 'ur',
115
- 'ur_rom', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo',
116
- 'zh-Hans', 'zh-Hant', 'zu',
117
- ]
118
- )
119
-
120
- for d in dataset:
121
- for text in d['text']:
122
- yield text
123
-
124
- del dataset
125
- gc.collect()
126
-
127
-
128
  #
129
  # special_tokens
130
  #
@@ -132,7 +13,6 @@ bos_token = '<|begin_of_text|>'
132
  eos_token = '<|end_of_text|>'
133
 
134
  special_tokens = [
135
- # '<unk>',
136
  bos_token,
137
  eos_token,
138
  '<|start_header_id|>',
@@ -142,9 +22,6 @@ special_tokens = [
142
  'system',
143
  'user',
144
  'assistant',
145
- 'tool',
146
- 'agent',
147
- 'internal', # thinking
148
 
149
  # tool/function calling
150
  '<tools>',
@@ -305,11 +182,11 @@ special_tokens = [
305
  # '</value>',
306
  ]
307
 
308
- # for i in range(2, 25):
309
- # special_tokens.append(' ' * i)
310
 
311
- # for i in range(2, 25):
312
- # special_tokens.append('\t' * i)
313
 
314
  # for i in range(2, 25):
315
  # special_tokens.append('\n' * i)
@@ -326,11 +203,9 @@ for i in range(256):
326
  for i in range(64):
327
  special_tokens.append(f'<|reserved_special_token_{i}|>')
328
 
329
-
330
  #
331
- # train tokenizer
332
  #
333
- # bpe = BPE(unk_token=None, dropout=None, fuse_unk=False, byte_fallback=False)
334
  bpe = BPE(unk_token=None, byte_fallback=True)
335
  tokenizer = Tokenizer(bpe)
336
 
@@ -338,45 +213,35 @@ tokenizer = Tokenizer(bpe)
338
  tokenizer.normalizer = None
339
 
340
  # pre-tokenizer
341
- # tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
342
- # pre_tokenizers.Split(r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+", behavior='isolated'),
343
- # pre_tokenizers.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=False)
344
- # ])
345
- # tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
346
  tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=True)
347
 
348
  # post-processor
349
- # tokenizer.post_processor = processors.Sequence([
350
- # processors.ByteLevel(add_prefix_space=True, trim_offsets=False, use_regex=True),
351
- # processors.TemplateProcessing(
352
- # single=f"{bos_token} $A",
353
- # pair=f"{bos_token} $A {bos_token} $B",
354
- # special_tokens=[
355
- # (bos_token, 0),
356
- # ],
357
- # ),
358
- # ])
359
- # tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
360
  tokenizer.post_processor = processors.ByteLevel(add_prefix_space=True, trim_offsets=False, use_regex=True)
361
 
362
  # decoder
363
- # tokenizer.decoder = decoders.ByteLevel(add_prefix_space=True, trim_offsets=True, use_regex=True)
364
- # tokenizer.decoder = decoders.ByteLevel()
365
  tokenizer.decoder = decoders.ByteLevel(add_prefix_space=True, trim_offsets=True, use_regex=True)
366
 
 
 
 
367
  trainer = BpeTrainer(
368
- vocab_size=49152, # 1.5 * 32 * 1024
369
  min_frequency=3,
370
  special_tokens=special_tokens,
371
  max_token_length=24,
372
- # continuing_subword_prefix=None,
373
- # end_of_word_suffix=None,
374
  )
375
 
376
- tokenizer.train_from_iterator(batch_iterator(), trainer)
 
 
 
 
377
  tokenizer.save('../tokenizer.json')
378
  tokenizer.model.save('../')
379
 
 
 
 
380
  CHAT_TEMPLATE = (
381
  "{{ bos_token }}"
382
 
@@ -396,7 +261,6 @@ fast_tokenizer = PreTrainedTokenizerFast(
396
  chat_template=CHAT_TEMPLATE,
397
  bos_token=bos_token,
398
  eos_token=eos_token,
399
- # unk_token='<unk>',
400
  clean_up_tokenization_spaces=False,
401
  )
402
 
 
 
 
 
 
1
  from transformers import PreTrainedTokenizerFast
2
  from tokenizers import Tokenizer, normalizers, pre_tokenizers, processors, decoders
3
  from tokenizers.models import BPE
4
  from tokenizers.trainers import BpeTrainer
5
 
6
+ from utils import batch_text_iterator
7
  from pretrain_datasets import pretrain_datasets
8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  #
10
  # special_tokens
11
  #
 
13
  eos_token = '<|end_of_text|>'
14
 
15
  special_tokens = [
 
16
  bos_token,
17
  eos_token,
18
  '<|start_header_id|>',
 
22
  'system',
23
  'user',
24
  'assistant',
 
 
 
25
 
26
  # tool/function calling
27
  '<tools>',
 
182
  # '</value>',
183
  ]
184
 
185
+ for i in range(2, 25):
186
+ special_tokens.append(' ' * i)
187
 
188
+ for i in range(2, 25):
189
+ special_tokens.append('\t' * i)
190
 
191
  # for i in range(2, 25):
192
  # special_tokens.append('\n' * i)
 
203
  for i in range(64):
204
  special_tokens.append(f'<|reserved_special_token_{i}|>')
205
 
 
206
  #
207
+ # BPE Tokenizer
208
  #
 
209
  bpe = BPE(unk_token=None, byte_fallback=True)
210
  tokenizer = Tokenizer(bpe)
211
 
 
213
  tokenizer.normalizer = None
214
 
215
  # pre-tokenizer
 
 
 
 
 
216
  tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=True)
217
 
218
  # post-processor
 
 
 
 
 
 
 
 
 
 
 
219
  tokenizer.post_processor = processors.ByteLevel(add_prefix_space=True, trim_offsets=False, use_regex=True)
220
 
221
  # decoder
 
 
222
  tokenizer.decoder = decoders.ByteLevel(add_prefix_space=True, trim_offsets=True, use_regex=True)
223
 
224
+ #
225
+ # BPE Trainer
226
+ #
227
  trainer = BpeTrainer(
228
+ vocab_size=65536, # 64 * 1024
229
  min_frequency=3,
230
  special_tokens=special_tokens,
231
  max_token_length=24,
 
 
232
  )
233
 
234
+ tokenizer.train_from_iterator(
235
+ batch_text_iterator(pretrain_datasets),
236
+ trainer,
237
+ )
238
+
239
  tokenizer.save('../tokenizer.json')
240
  tokenizer.model.save('../')
241
 
242
+ #
243
+ # PreTrainedTokenizerFast
244
+ #
245
  CHAT_TEMPLATE = (
246
  "{{ bos_token }}"
247
 
 
261
  chat_template=CHAT_TEMPLATE,
262
  bos_token=bos_token,
263
  eos_token=eos_token,
 
264
  clean_up_tokenization_spaces=False,
265
  )
266
 
special_tokens_map.json DELETED
@@ -1,4 +0,0 @@
1
- {
2
- "bos_token": "<|begin_of_text|>",
3
- "eos_token": "<|end_of_text|>"
4
- }
 
 
 
 
 
tokenizer.json DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:1cd4138339706633e3ce4f58c71e2b9e29b9bf1176f7b8af81285543248f541d
3
- size 3677337
 
 
 
 
tokenizer_config.json DELETED
@@ -1,3706 +0,0 @@
1
- {
2
- "added_tokens_decoder": {
3
- "0": {
4
- "content": "<|begin_of_text|>",
5
- "lstrip": false,
6
- "normalized": false,
7
- "rstrip": false,
8
- "single_word": false,
9
- "special": true
10
- },
11
- "1": {
12
- "content": "<|end_of_text|>",
13
- "lstrip": false,
14
- "normalized": false,
15
- "rstrip": false,
16
- "single_word": false,
17
- "special": true
18
- },
19
- "2": {
20
- "content": "<|start_header_id|>",
21
- "lstrip": false,
22
- "normalized": false,
23
- "rstrip": false,
24
- "single_word": false,
25
- "special": true
26
- },
27
- "3": {
28
- "content": "<|end_header_id|>",
29
- "lstrip": false,
30
- "normalized": false,
31
- "rstrip": false,
32
- "single_word": false,
33
- "special": true
34
- },
35
- "4": {
36
- "content": "<|eom_id|>",
37
- "lstrip": false,
38
- "normalized": false,
39
- "rstrip": false,
40
- "single_word": false,
41
- "special": true
42
- },
43
- "5": {
44
- "content": "<|eot_id|>",
45
- "lstrip": false,
46
- "normalized": false,
47
- "rstrip": false,
48
- "single_word": false,
49
- "special": true
50
- },
51
- "6": {
52
- "content": "system",
53
- "lstrip": false,
54
- "normalized": false,
55
- "rstrip": false,
56
- "single_word": false,
57
- "special": true
58
- },
59
- "7": {
60
- "content": "user",
61
- "lstrip": false,
62
- "normalized": false,
63
- "rstrip": false,
64
- "single_word": false,
65
- "special": true
66
- },
67
- "8": {
68
- "content": "assistant",
69
- "lstrip": false,
70
- "normalized": false,
71
- "rstrip": false,
72
- "single_word": false,
73
- "special": true
74
- },
75
- "9": {
76
- "content": "tool",
77
- "lstrip": false,
78
- "normalized": false,
79
- "rstrip": false,
80
- "single_word": false,
81
- "special": true
82
- },
83
- "10": {
84
- "content": "agent",
85
- "lstrip": false,
86
- "normalized": false,
87
- "rstrip": false,
88
- "single_word": false,
89
- "special": true
90
- },
91
- "11": {
92
- "content": "internal",
93
- "lstrip": false,
94
- "normalized": false,
95
- "rstrip": false,
96
- "single_word": false,
97
- "special": true
98
- },
99
- "12": {
100
- "content": "<tools>",
101
- "lstrip": false,
102
- "normalized": false,
103
- "rstrip": false,
104
- "single_word": false,
105
- "special": true
106
- },
107
- "13": {
108
- "content": "</tools>",
109
- "lstrip": false,
110
- "normalized": false,
111
- "rstrip": false,
112
- "single_word": false,
113
- "special": true
114
- },
115
- "14": {
116
- "content": "<tool>",
117
- "lstrip": false,
118
- "normalized": false,
119
- "rstrip": false,
120
- "single_word": false,
121
- "special": true
122
- },
123
- "15": {
124
- "content": "</tool>",
125
- "lstrip": false,
126
- "normalized": false,
127
- "rstrip": false,
128
- "single_word": false,
129
- "special": true
130
- },
131
- "16": {
132
- "content": "<tool_call>",
133
- "lstrip": false,
134
- "normalized": false,
135
- "rstrip": false,
136
- "single_word": false,
137
- "special": true
138
- },
139
- "17": {
140
- "content": "</tool_call>",
141
- "lstrip": false,
142
- "normalized": false,
143
- "rstrip": false,
144
- "single_word": false,
145
- "special": true
146
- },
147
- "18": {
148
- "content": "<tool_response>",
149
- "lstrip": false,
150
- "normalized": false,
151
- "rstrip": false,
152
- "single_word": false,
153
- "special": true
154
- },
155
- "19": {
156
- "content": "</tool_response>",
157
- "lstrip": false,
158
- "normalized": false,
159
- "rstrip": false,
160
- "single_word": false,
161
- "special": true
162
- },
163
- "20": {
164
- "content": "\"name\"",
165
- "lstrip": false,
166
- "normalized": false,
167
- "rstrip": false,
168
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169
- "special": true
170
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171
- "21": {
172
- "content": "\"arguments\"",
173
- "lstrip": false,
174
- "normalized": false,
175
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176
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177
- "special": true
178
- },
179
- "22": {
180
- "content": "\"$schema\"",
181
- "lstrip": false,
182
- "normalized": false,
183
- "rstrip": false,
184
- "single_word": false,
185
- "special": true
186
- },
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- "23": {
188
- "content": "\"$id\"",
189
- "lstrip": false,
190
- "normalized": false,
191
- "rstrip": false,
192
- "single_word": false,
193
- "special": true
194
- },
195
- "24": {
196
- "content": "\"$ref\"",
197
- "lstrip": false,
198
- "normalized": false,
199
- "rstrip": false,
200
- "single_word": false,
201
- "special": true
202
- },
203
- "25": {
204
- "content": "\"$defs\"",
205
- "lstrip": false,
206
- "normalized": false,
207
- "rstrip": false,
208
- "single_word": false,
209
- "special": true
210
- },
211
- "26": {
212
- "content": "\"$anchor\"",
213
- "lstrip": false,
214
- "normalized": false,
215
- "rstrip": false,
216
- "single_word": false,
217
- "special": true
218
- },
219
- "27": {
220
- "content": "\"$dynamicAnchor\"",
221
- "lstrip": false,
222
- "normalized": false,
223
- "rstrip": false,
224
- "single_word": false,
225
- "special": true
226
- },
227
- "28": {
228
- "content": "\"$dynamicRef\"",
229
- "lstrip": false,
230
- "normalized": false,
231
- "rstrip": false,
232
- "single_word": false,
233
- "special": true
234
- },
235
- "29": {
236
- "content": "\"$vocabulary\"",
237
- "lstrip": false,
238
- "normalized": false,
239
- "rstrip": false,
240
- "single_word": false,
241
- "special": true
242
- },
243
- "30": {
244
- "content": "\"$comment\"",
245
- "lstrip": false,
246
- "normalized": false,
247
- "rstrip": false,
248
- "single_word": false,
249
- "special": true
250
- },
251
- "31": {
252
- "content": "\"null\"",
253
- "lstrip": false,
254
- "normalized": false,
255
- "rstrip": false,
256
- "single_word": false,
257
- "special": true
258
- },
259
- "32": {
260
- "content": "\"boolean\"",
261
- "lstrip": false,
262
- "normalized": false,
263
- "rstrip": false,
264
- "single_word": false,
265
- "special": true
266
- },
267
- "33": {
268
- "content": "\"object\"",
269
- "lstrip": false,
270
- "normalized": false,
271
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