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from typing import Optional |
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from functools import partial |
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from datasets import load_dataset |
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from litdata import optimize, TokensLoader |
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from litgpt.tokenizer import Tokenizer |
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def batch_iterator(path: str, |
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name: Optional[str]=None, |
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data_dir: Optional[str]=None, |
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data_files: Optional[str]=None, |
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revision: Optional[str]=None, |
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split: str='train', |
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format: Optional[str]=None): |
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assert format is not None |
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dataset = load_dataset(path=path, |
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name=name, |
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data_dir=data_dir, |
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data_files=data_files, |
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revision=revision, |
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split=split, |
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trust_remote_code=True) |
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for row in dataset: |
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text = format.format(**row) |
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yield text |
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def tokenize_fn(datasets_config, tokenizer=None): |
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for text in batch_iterator(**datasets_config): |
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text_ids = tokenizer.encode(text, bos=False, eos=True) |
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yield text_ids |
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datasets_configs = [ |
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{'path': 'yahma/alpaca-cleaned', 'format': '{instruction} {input} {output}'}, |
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{'path': 'gbharti/wealth-alpaca_lora', 'format': '{instruction} {input} {output}'}, |
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*[ |
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{'path': 'saillab/taco-datasets', 'data_dir': data_dir, 'split': 'train[:10%]', 'format': '{instruction} {input} {output}'} |
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for data_dir in [ |
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'multilingual-instruction-tuning-dataset /multilingual-alpaca-52k-gpt-4', |
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'multilingual-instruction-tuning-dataset /multilinugal-dolly-15k', |
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] |
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], |
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*[ |
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{'path': 'xu-song/cc100-samples', 'name': name, 'split': 'train[:10%]', 'format': '{text}'} |
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for name in [ |
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'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'bn_rom', 'br', |
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'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', |
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'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', |
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'gn', 'gu', 'ha', 'he', 'hi', 'hi_rom', 'hr', 'ht', 'hu', |
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'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', |
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'kn', 'ko', 'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt', |
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'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'my_zaw', |
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'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt', |
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'qu', 'rm', 'ro', 'ru', 'sa', 'si', 'sc', 'sd', 'sk', 'sl', |
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'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'ta_rom', |
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'te', 'te_rom', 'th', 'tl', 'tn', 'tr', 'ug', 'uk', 'ur', |
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'ur_rom', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', |
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'zh-Hans', 'zh-Hant', 'zu', |
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] |
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], |
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{'path': 'ontocord/fineweb-permissive-multilingual-2m', 'split': 'train[:5%]', 'format': '{text}'}, |
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{'path': 'MuskumPillerum/General-Knowledge', 'format': '{Question} {Answer}'}, |
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{'path': 'yirenc/general_knowledge_boolean', 'split': 'train+validation', 'format': '{question}? {answer}. {passage}'}, |
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{'path': 'nampdn-ai/tiny-textbooks', 'split': 'train+test', 'format': '{textbook}'}, |
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{'path': 'nampdn-ai/tiny-codes', 'split': 'train[:5%]', 'format': '{prompt} {response}'}, |
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*[ |
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{'path': 'bigcode/the-stack-smol-xs', 'name': name, 'format': '{content}'} |
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for name in [ |
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'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', |
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'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', |
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'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', |
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'css', 'cuda', 'dart', 'dockerfile', 'elixir', |
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'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', |
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'groovy', 'haskell','html', 'idris', 'isabelle', 'java', |
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'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', |
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'literate-agda', 'literate-coffeescript', 'literate-haskell', |
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'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', |
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'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', |
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'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', |
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'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', |
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'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', |
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'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', |
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'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', |
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'yacc', 'zig', |
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] |
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], |
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{'path': 'm-a-p/CodeFeedback-Filtered-Instruction', 'split': 'train', 'format': '{query} {answer}'}, |
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{'path': 'jtatman/python-code-dataset-500k', 'format': '{instruction} {output}'}, |
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{'path': 'iamtarun/python_code_instructions_18k_alpaca', 'format': '{instruction} {input} {output}'}, |
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{'path': 'HuggingFaceH4/CodeAlpaca_20K', 'split': 'train+test', 'format': '{prompt} {completion}'}, |
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{'path': 'gair-prox/open-web-math-pro', 'split': 'train[:5%]', 'format': '{text}'}, |
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{'path': 'rvv-karma/Math-QA', 'split': 'train+val+test', 'format': '{question} {answer}'}, |
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{'path': 'ajibawa-2023/Maths-College', 'split': 'train[:10%]', 'format': '{instruction} {output}'}, |
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{'path': 'microsoft/orca-math-word-problems-200k', 'format': '{question} {answer}'}, |
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{'path': 'fblgit/simple-math', 'revision': 'refs/convert/parquet', 'split': 'train+test', 'format': '{instruction} = {output}'}, |
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{'path': 'SkunkworksAI/reasoning-0.01', 'format': '{instruction} {reasoning} {output}'}, |
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{'path': 'badrex/llm-emoji-dataset', 'format': '{character} {unicode} {short description} {tags} {LLM description}'}, |
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] |
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outputs = optimize( |
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fn=partial(tokenize_fn, tokenizer=Tokenizer('..')), |
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inputs=datasets_configs, |
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output_dir='../pretrain-data/', |
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chunk_size=(2049 * 8012), |
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num_workers=32, |
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) |
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