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import gc |
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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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from functools import partial |
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def batch_iterator(name=None): |
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if name in (None, 'Replete-AI/Everything_Instruct_Multilingual'): |
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dataset = load_dataset('Replete-AI/Everything_Instruct_Multilingual', split='train') |
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for row in dataset: |
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text = [] |
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if row['instruction']: |
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text.append( |
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'<|im_start|>system\n' |
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f"{row['instruction']}<|im_end|>" |
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) |
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if row['input']: |
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text.append( |
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'<|im_start|>user\n' |
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f"{row['input']}<|im_end|>" |
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) |
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if row['output']: |
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text.append( |
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'<|im_start|>assistant\n' |
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f"{row['output']}<|im_end|>" |
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) |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'HuggingFaceH4/ultrachat_200k'): |
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dataset = load_dataset('HuggingFaceH4/ultrachat_200k', split='train_sft') |
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for row in dataset: |
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text = [ |
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f"<|im_start|>{n['role']}\n{n['content']}<|im_end|>" |
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for n in row['messages'] |
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] |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'HuggingFaceH4/no_robots'): |
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dataset = load_dataset('HuggingFaceH4/no_robots', split='train') |
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for row in dataset: |
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text = [ |
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f"<|im_start|>{n['role']}\n{n['content']}<|im_end|>" |
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for n in row['messages'] |
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] |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/ultrachat_200k_serbian'): |
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dataset = load_dataset('datatab/ultrachat_200k_serbian', split='train') |
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for row in dataset: |
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text = [ |
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f"<|im_start|>{n['role']}\n{n['content']}<|im_end|>" |
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for n in row['messages_srb'] |
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] |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/ultrafeedback_binarized_serbian'): |
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dataset = load_dataset('datatab/ultrafeedback_binarized_serbian', split='train_sft') |
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for row in dataset: |
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text = [ |
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f"<|im_start|>{n['role']}\n{n['content']}<|im_end|>" |
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for n in row['chosen'] |
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] |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/alpaca-cleaned-serbian-full'): |
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dataset = load_dataset('datatab/alpaca-cleaned-serbian-full', split='train') |
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for row in dataset: |
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text = [] |
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if row['instruction']: |
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text.append( |
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'<|im_start|>system\n' |
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f"{row['instruction']}<|im_end|>" |
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) |
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if row['input']: |
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text.append( |
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'<|im_start|>user\n' |
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f"{row['input']}<|im_end|>" |
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) |
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if row['output']: |
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text.append( |
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'<|im_start|>assistant\n' |
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f"{row['output']}<|im_end|>" |
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) |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/orca_math_world_problem_200k_serbian'): |
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dataset = load_dataset('datatab/orca_math_world_problem_200k_serbian', split='train') |
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for row in dataset: |
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text = [] |
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text.append( |
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'<|im_start|>user\n' |
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f"{row['question_translated_srb']}<|im_end|>" |
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) |
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text.append( |
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'<|im_start|>assistant\n' |
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f"{row['answer_translated_srb']}<|im_end|>" |
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) |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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if name in (None, 'datatab/open-orca-slim-serbian'): |
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dataset = load_dataset('datatab/open-orca-slim-serbian', split='train') |
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role_map = {'system': 'system', 'human': 'user', 'gpt': 'assistant'} |
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for row in dataset['conversations']: |
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text = [ |
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f"<|im_start|>{role_map[n['from']]}\n{n['value']}<|im_end|>" |
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for n in row |
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if n |
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] |
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text = '\n'.join(text) + '\n' |
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yield text |
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del dataset |
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gc.collect() |
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def tokenize_fn(dataset_name, tokenizer=None): |
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for text in batch_iterator(dataset_name): |
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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_names = [ |
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'Replete-AI/Everything_Instruct_Multilingual', |
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'HuggingFaceH4/ultrachat_200k', |
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'HuggingFaceH4/no_robots', |
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'datatab/ultrachat_200k_serbian', |
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'datatab/ultrafeedback_binarized_serbian', |
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'datatab/alpaca-cleaned-serbian-full', |
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'datatab/orca_math_world_problem_200k_serbian', |
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'datatab/open-orca-slim-serbian', |
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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_names, |
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output_dir='../data/', |
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chunk_size=((32768 + 1) * 500), |
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num_workers=16, |
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) |
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