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VeoAI / app.py

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Update app.py

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	from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainer, Seq2SeqTrainingArguments
	# Load tokenizer and model
	tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-large")
	model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum", from_tf=True)

	# Assuming you have your own dataset for fine-tuning
	# Replace this with loading your dataset as needed
	# For example, you can use the datasets library for loading datasets
	# See previous responses for an example of how to use datasets

	# Define data collator for sequence-to-sequence modeling
	data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)

	# Define training arguments
	training_args = Seq2SeqTrainingArguments(
	output_dir="./gpt4-text-gen",
	overwrite_output_dir=True,
	per_device_train_batch_size=4,
	save_steps=10_000,
	save_total_limit=2,
	)

	# Create Seq2SeqTrainer
	trainer = Seq2SeqTrainer(
	model=model,
	args=training_args,
	data_collator=data_collator,
	train_dataset=your_training_dataset, # Replace with your training dataset
	)

	# Train the model
	trainer.train()

	# Save the fine-tuned model and tokenizer
	model.save_pretrained("./gpt4-text-gen")
	tokenizer.save_pretrained("./gpt4-text-gen")

	# Generate text using the fine-tuned model
	input_text = "Hello!"
	input_ids = tokenizer.encode(input_text, return_tensors="pt")
	output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95)
	generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
	print("Generated Text: ", generated_text)