train tokenizer

.gitignore

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+# ---> Python
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+develop-eggs/
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+share/python-wheels/
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+
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+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+
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+.pdm.toml
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+/site
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+.DS_Store
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requirements.in

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+tqdm
+datasets
+jinja2
+transformers
+jsonlines

scripts/TRAIN.md

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+# Train
+
+## Environment
+
+```bash
+python -m venv venv
+source venv/bin/activate
+pip install -U -r requirements.in
+```
+
+## Tokenizer
+
+```bash
+python -B train_tokenizer.py
+```

scripts/train_tokenizer.py

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+import string
+
+from datasets import load_dataset
+from tokenizers import ByteLevelBPETokenizer
+from transformers import PreTrainedTokenizerFast
+
+
+# dataset_0 = (
+#     load_dataset('wikimedia/wikisource', lang, split='train')
+#     for lang in ['20231201.ar', '20231201.as', '20231201.az', '20231201.ban', '20231201.be', '20231201.bg', '20231201.bn', '20231201.br', '20231201.bs', '20231201.ca', '20231201.cs', '20231201.cy', '20231201.da', '20231201.de', '20231201.el', '20231201.en', '20231201.eo', '20231201.es', '20231201.et', '20231201.eu', '20231201.fa', '20231201.fi', '20231201.fo', '20231201.fr', '20231201.gl', '20231201.gu', '20231201.he', '20231201.hi', '20231201.hr', '20231201.hu', '20231201.hy', '20231201.id', '20231201.is', '20231201.it', '20231201.ja', '20231201.jv', '20231201.kn', '20231201.ko', '20231201.la', '20231201.li', '20231201.lij', '20231201.lt', '20231201.mk', '20231201.ml', '20231201.mr', '20231201.nap', '20231201.nl', '20231201.no', '20231201.or', '20231201.pa', '20231201.pl', '20231201.pms', '20231201.pt', '20231201.ro', '20231201.ru', '20231201.sa', '20231201.sah', '20231201.sk', '20231201.sl', '20231201.sr', '20231201.su', '20231201.sv', '20231201.ta', '20231201.te', '20231201.th', '20231201.tr', '20231201.uk', '20231201.vec', '20231201.vi', '20231201.wa', '20231201.yi', '20231201.zh', '20231201.zh-min-nan']
+# )
+
+dataset_1 = (
+    load_dataset('xu-song/cc100-samples', lang, split='train')
+    for lang in ['am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'bn_rom', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gn', 'gu', 'ha', 'he', 'hi', 'hi_rom', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'my_zaw', 'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'qu', 'rm', 'ro', 'ru', 'sa', 'si', 'sc', 'sd', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'ta_rom', 'te', 'te_rom', 'th', 'tl', 'tn', 'tr', 'ug', 'uk', 'ur', 'ur_rom', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh-Hans', 'zh-Hant', 'zu']
+)
+
+dataset_2 = (
+    load_dataset('csebuetnlp/xlsum', lang, split='train')
+    for lang in ['amharic', 'arabic', 'azerbaijani', 'bengali', 'burmese', 'chinese_simplified', 'chinese_traditional', 'english', 'french', 'gujarati', 'hausa', 'hindi', 'igbo', 'indonesian', 'japanese', 'kirundi', 'korean', 'kyrgyz', 'marathi', 'nepali', 'oromo', 'pashto', 'persian', 'pidgin', 'portuguese', 'punjabi', 'russian', 'scottish_gaelic', 'serbian_cyrillic', 'serbian_latin', 'sinhala', 'somali', 'spanish', 'swahili', 'tamil', 'telugu', 'thai', 'tigrinya', 'turkish', 'ukrainian', 'urdu', 'uzbek', 'vietnamese', 'welsh', 'yoruba']
+)
+
+# dataset_3 = load_dataset('recursal/SuperWikiNEXT-32B', split='train')
+dataset_4 = load_dataset('m-a-p/CodeFeedback-Filtered-Instruction', split='train')
+dataset_5 = load_dataset('nampdn-ai/tiny-codes', split='train')
+# dataset_6 = load_dataset('ajibawa-2023/Maths-College', split='train')
+dataset_7 = load_dataset('microsoft/orca-math-word-problems-200k', split='train')
+dataset_8 = load_dataset('mlabonne/FineTome-100k', split='train')
+dataset_9 = load_dataset('arcee-ai/agent-data', split='train')
+dataset_10 = [
+    load_dataset('cognitivecomputations/SystemChat-2.0', data_files='SystemChat_filtered.jsonl', split='train'),
+    load_dataset('cognitivecomputations/SystemChat-2.0', data_files='SystemChat_multilingual.jsonl', split='train'),
+]
+dataset_11 = load_dataset('badrex/llm-emoji-dataset', split='train')
+
+
+def batch_iterator():
+    # for d in dataset_0:
+    #     for row in d['text']:
+    #         yield row
+    #         break
+    #
+    #     break
+
+    for d in dataset_1:
+        for row in d['text']:
+            yield row
+            # break
+
+        # break
+
+    for d in dataset_2:
+        for row in d['text']:
+            yield row
+            # break
+
+        # break
+
+    # for row in dataset_3['text']:
+    #     yield row
+    #     break
+
+    for row in dataset_4:
+        yield row['query'] + '\n' + row['answer']
+        # break
+
+    for row in dataset_5:
+        yield row['prompt'] + '\n' + row['response']
+        # break
+
+    # for row in dataset_6:
+    #     yield row['instruction'] + '\n' + row['output']
+    #     break
+
+    for row in dataset_7:
+        yield row['question'] + '\n' + row['answer']
+        # break
+
+    for row in dataset_8['conversations']:
+        yield '\n'.join(n['value'] for n in row)
+        # break
+
+    for row in dataset_9['conversations']:
+        yield '\n'.join(n['value'] for n in row)
+        # break
+
+    for d in dataset_10:
+        for row in d['messages']:
+            yield '\n'.join(n['content'] for n in row)
+            # break
+
+    for row in dataset_11:
+        yield f'{row["character"]}\n{row["unicode"]}\n{row["short description"]}\n{row["tags"]}\n{row["LLM description"]}'
+        # break
+
+
+# for row in batch_iterator():
+#     print(f'{row = }')
+
+
+special_tokens = [
+    '<s>',
+    '</s>',
+    '<pad>',
+    '<unk>',
+    '<mask>',
+    '<|im_start|>',
+    '<|im_end|>',
+    '<tools>',
+    '</tools>',
+    '<tool_call>',
+    '</tool_call>',
+    '<tool_response>',
+    '</tool_response>',
+    'system',
+    'user',
+    'assistant',
+    *list(string.printable),
+]
+
+for i in range(64 - len(special_tokens)):
+    special_tokens.append(f'<|reserved_{i}|>')
+
+ascii_chars = string.ascii_letters + string.ascii_lowercase + string.ascii_uppercase + string.digits + string.punctuation
+
+tokenizer = ByteLevelBPETokenizer()
+
+tokenizer.train_from_iterator(
+    [ascii_chars],
+    vocab_size=len(ascii_chars),
+    min_frequency=1,
+    special_tokens=[],
+)
+
+tokenizer.train_from_iterator(
+    batch_iterator(),
+    vocab_size=32064,
+    min_frequency=2,
+    special_tokens=special_tokens,
+)
+
+tokenizer.save_model('..')
+
+CHATML_CHAT_TEMPLATE = (
+    "{% for message in messages %}"
+        "{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}"
+        "{{ '<|im_start|>assistant\n' }}"
+    "{% endif %}"
+)
+
+fast_tokenizer = PreTrainedTokenizerFast(
+    tokenizer_object=tokenizer,
+    chat_template=CHATML_CHAT_TEMPLATE,
+    bos_token='<s>',
+    eos_token='</s>',
+    unk_token='<unk>',
+    pad_token='<pad>',
+    mask_token='<mask>',
+)
+
+fast_tokenizer.save_pretrained('..')