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import gc
import torch
from torch.optim import AdamW
import bitsandbytes as bnb
from datasets import load_dataset, Dataset
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelForCausalLM,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling,
)
def _batch_iterator():
## code
# dataset = load_dataset('bigcode/programming-languages-keywords', split='train')
# for row in dataset:
# for n in row['keywords']:
# yield n
# del dataset
# gc.collect()
# return
# code
dataset = (
load_dataset('bigcode/the-stack-smol-xs', lang, split='train', trust_remote_code=True)
for lang in [
'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c',
'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir',
'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java',
'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell',
'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog',
'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme',
'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex',
'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig'
]
)
for d in dataset:
for row in d:
yield row['content']
del dataset
gc.collect()
# text
dataset = load_dataset('nampdn-ai/tiny-textbooks', split='train')
for row in dataset:
yield row['text']
del dataset
gc.collect()
## text
# dataset = (
# 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']
# )
#
# for d in dataset:
# for row in d['text']:
# yield row
#
# del dataset
# gc.collect()
# text
dataset = (
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']
)
for d in dataset:
for row in d['text']:
yield row
del dataset
gc.collect()
## text
# dataset = (
# 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']
# )
#
# for d in dataset:
# for row in d['text']:
# yield row
#
# del dataset
# gc.collect()
## text
# dataset = load_dataset('recursal/SuperWikiNEXT-32B', split='train')
#
# for row in dataset['text']:
# yield row
#
# del dataset
# gc.collect()
# code
dataset = load_dataset('m-a-p/CodeFeedback-Filtered-Instruction', split='train')
for row in dataset:
yield row['query'] + '\n' + row['answer']
del dataset
gc.collect()
# code
dataset = load_dataset('nampdn-ai/tiny-codes', split='train')
for row in dataset:
yield row['prompt'] + '\n' + row['response']
del dataset
gc.collect()
# math
dataset = load_dataset('ajibawa-2023/Maths-College', split='train')
for row in dataset:
yield row['instruction'] + '\n' + row['output']
del dataset
gc.collect()
# math
dataset = load_dataset('microsoft/orca-math-word-problems-200k', split='train')
for row in dataset:
yield row['question'] + '\n' + row['answer']
del dataset
gc.collect()
# text
dataset = load_dataset('mlabonne/FineTome-100k', split='train')
for row in dataset['conversations']:
yield '\n'.join(n['value'] for n in row)
del dataset
gc.collect()
# instruction
dataset = load_dataset('arcee-ai/agent-data', split='train')
for row in dataset['conversations']:
yield '\n'.join(n['value'] for n in row)
del dataset
gc.collect()
# instruction
dataset = (
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'),
)
for d in dataset:
for row in d['messages']:
yield '\n'.join(n['content'] for n in row)
del dataset
gc.collect()
# emoji
dataset = load_dataset('badrex/llm-emoji-dataset', split='train')
for row in dataset:
yield f'{row["character"]}\n{row["unicode"]}\n{row["short description"]}\n{row["tags"]}\n{row["LLM description"]}'
del dataset
gc.collect()
def batch_iterator():
for text in _batch_iterator():
for i in range(0, len(text), 2048):
chunk = text[i:i + 2048]
tokenized = tokenize_function(chunk)
yield tokenized
def tokenize_function(text):
outputs = tokenizer(text, truncation=True, padding='max_length', max_length=2048)
outputs['labels'] = outputs['input_ids'].copy()
return outputs
tokenizer = AutoTokenizer.from_pretrained('../')
print(tokenizer)
config = AutoConfig.from_pretrained('mistralai/Mistral-7B-Instruct-v0.3')
config.bos_token_id = tokenizer.bos_token_id
config.eos_token_id = tokenizer.eos_token_id
config.unk_token_id = tokenizer.unk_token_id
config.pad_token_id = tokenizer.pad_token_id
config.hidden_size = 512
config.intermediate_size = 1792 # int(512 * 3.5)
config.max_position_embeddings = 32768 # 32 * 1024
config.num_attention_heads = 12
config.num_hidden_layers = 10
config.num_key_value_heads = 4
config.rope_theta = 1_000_000.0
config.sliding_window = 4096
config.torch_dtype = torch.bfloat16
config.use_cache = False
print(config)
model = AutoModelForCausalLM.from_config(config)
print(model)
dataset = Dataset.from_generator(batch_iterator)
print(dataset)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
print(data_collator)
optimizer = bnb.optim.AdamW8bit(
model.parameters(),
lr=1e-5,
betas=(0.9, 0.95),
weight_decay=0.1,
)
print(optimizer)
training_args = TrainingArguments(
output_dir='./mistral-custom',
num_train_epochs=3,
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
warmup_steps=500,
learning_rate=1e-5,
fp16=False,
bf16=True,
logging_dir='./logs',
logging_steps=10,
evaluation_strategy='no',
save_strategy='epoch',
torch_compile=True,
)
print(training_args)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
optimizers=(optimizer, None)
)
print(trainer)
trainer.train()
trainer.save_model('./mistral-custom-final')
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