Delete train.py

train.py

@@ -1,53 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import LoraConfig, get_peft_model
-from datasets import load_dataset
-from transformers import TrainingArguments, Trainer
-
-# Load dataset (StackOverflow Python dataset as an example)
-dataset = load_dataset("stackoverflow", "python")
-
-# Preprocess the dataset
-def format_data(example):
-    return {
-        "text": f"### Question:\n{example['question']}\n### Answer:\n{example['answer']}"
-    }
-
-dataset = dataset.map(format_data)
-
-# Load the Mistral-7B model and tokenizer
-model_name = "mistralai/Mistral-7B-v0.1"  # or use Phi-2
-model = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# LoRA configuration for lightweight fine-tuning
-lora_config = LoraConfig(
-    r=8,
-    lora_alpha=32,
-    lora_dropout=0.1,
-    target_modules=["q_proj", "v_proj"]
-)
-model = get_peft_model(model, lora_config)
-
-# Training arguments
-training_args = TrainingArguments(
-    output_dir="./tuned_model",  # Directory to save the fine-tuned model
-    per_device_train_batch_size=4,
-    num_train_epochs=3,
-    save_strategy="epoch",  # Save model after each epoch
-    save_total_limit=2      # Keep only the last 2 saved models
-)
-
-# Trainer setup
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=dataset["train"]
-)
-
-# Start the fine-tuning process
-trainer.train()
-
-# Save the model and tokenizer
-model.save_pretrained("./tuned_model")
-tokenizer.save_pretrained("./tuned_model")