Create fine_tune_model.py

fine_tune_model.py

@@ -0,0 +1,47 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
+
+# Load custom dataset
+dataset = load_dataset('json', data_files='path_to_your/shell_commands_mock_data.json')
+
+# Load tokenizer and model for Repl.it LLM
+model_name = "Repl.it/llama-2-13b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Tokenization function
+def tokenize_function(examples):
+    return tokenizer(examples['prompt'], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=1,
+    per_device_eval_batch_size=1,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=10,
+    save_steps=100,
+)
+
+# Trainer setup
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'],
+    eval_dataset=tokenized_datasets['test'] if 'test' in tokenized_datasets else None,
+)
+
+# Start training
+trainer.train()
+
+# Save fine-tuned model
+trainer.save_model("./fine_tuned_model")
+
+# Evaluate the model
+trainer.evaluate()