Create app.py

app.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
+from datasets import load_dataset
+
+tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+
+tokenizer.pad_token = tokenizer.eos_token
+
+dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
+dataset = dataset['train_sft'].select(range(100))
+
+def tokenize_function(examples):
+    return tokenizer(examples["prompt"], padding="max_length", truncation=True)
+
+td = dataset.map(tokenize_function, batched=True)
+
+training_args = TrainingArguments(
+    output_dir="./output",
+    per_device_train_batch_size=4,
+    num_train_epochs=3,
+    logging_dir="./logs",
+)
+
+data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+
+
+"""
+
+dataloader_config = DataLoaderConfiguration(
+    dispatch_batches=None,
+    split_batches=False,
+    even_batches=True,
+    use_seedable_sampler=True
+)
+
+
+accelerator = Accelerator(dataloader_config=dataloader_config)
+
+with accelerator.prepare():
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        data_collator=data_collator,
+        train_dataset=td,
+    )
+
+    trainer.train()
+    trainer.save_model("fine_tuned_gpt2")
+
+"""
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    data_collator=data_collator,
+    train_dataset=td,
+)
+
+trainer.train()
+trainer.save_model("fine_tuned_gpt2")