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@@ -68,7 +68,7 @@ def generate_prompt(data_point):
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{data_point["output"]}"""
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{data_point["output"]}"""
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-data = data.map(
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+data = data.shuffle().map(
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lambda data_point: tokenizer(
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lambda data_point: tokenizer(
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generate_prompt(data_point),
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generate_prompt(data_point),
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truncation=True,
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truncation=True,
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@@ -77,17 +77,9 @@ data = data.map(
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)
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)
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)
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)
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-
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-train_testvalid = data.train_test_split(test_size=2000, shuffle=True, seed=42)
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-test_valid = train_testvalid["test"].train_test_split(test_size=1000)
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-train_data = train_testvalid["train"]
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-valid_data = test_valid["train"]
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-test_data = test_valid["test"]
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-
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trainer = transformers.Trainer(
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trainer = transformers.Trainer(
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model=model,
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model=model,
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- train_dataset=train_data,
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- eval_dataset=valid_data,
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+ train_dataset=data["train"],
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args=transformers.TrainingArguments(
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args=transformers.TrainingArguments(
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per_device_train_batch_size=MICRO_BATCH_SIZE,
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per_device_train_batch_size=MICRO_BATCH_SIZE,
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gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
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gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
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@@ -95,7 +87,7 @@ trainer = transformers.Trainer(
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num_train_epochs=EPOCHS,
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num_train_epochs=EPOCHS,
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learning_rate=LEARNING_RATE,
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learning_rate=LEARNING_RATE,
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fp16=True,
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fp16=True,
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- logging_steps=10,
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+ logging_steps=1,
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output_dir="lora-alpaca",
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output_dir="lora-alpaca",
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save_total_limit=3,
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save_total_limit=3,
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),
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),
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