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from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset, load_from_disk
dataset = load_from_disk('finn_wake_dataset')
tokenizer = AutoTokenizer.from_pretrained("tinyllama/tinyllama-1.1b-chat-v1.0")
tokenizer.save_pretrained(".results/checkpoint-12000/")
model = AutoModelForCausalLM.from_pretrained("tinyllama/tinyllama-1.1b-chat-v1.0")
if tokenizer.pad_token is None:
print("Tokenizer does not have a pad token set. Setting pad_token to eos_token.")
tokenizer.pad_token = tokenizer.eos_token
def tokenize_function(examples):
tokenized_inputs = tokenizer(examples['text'], padding="max_length", truncation=True, max_length=128)
tokenized_inputs["labels"] = tokenized_inputs["input_ids"].copy()
return tokenized_inputs
tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"])
train_test_split = tokenized_dataset.train_test_split(test_size=0.1)
train_dataset = train_test_split['train']
eval_dataset = train_test_split['test']
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=1,
warmup_steps=500,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=10,
save_strategy="steps",
save_steps=500,
save_total_limit=2,
use_cpu=True)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
#below has been modified because i ran out of disk storage initially so had to resume and adjust the save_strategy above.
trainer.train(resume_from_checkpoint="./results/checkpoint-10000")
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