Create app.py

app.py

@@ -0,0 +1,41 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+
+# Load the dataset
+dataset = load_dataset("json", data_files="dataset.jsonl")
+
+# Load the model and tokenizer
+model_name = "Salesforce/codegen-2B-multi"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Tokenize the dataset
+def tokenize_function(examples):
+    return tokenizer(examples["input"], text_target=examples["output"], truncation=True)
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    overwrite_output_dir=True,
+    evaluation_strategy="epoch",
+    learning_rate=5e-5,
+    per_device_train_batch_size=4,
+    num_train_epochs=3,
+    save_strategy="epoch",
+    logging_dir="./logs",
+)
+
+# Train the model
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["train"],
+)
+
+trainer.train()
+trainer.save_model("./fine_tuned_codegen")
+tokenizer.save_pretrained("./fine_tuned_codegen")
+print("Training complete. Model saved.")