Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainer, Seq2SeqTrainingArguments
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("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 = "Once upon a time"
+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)