app.py

import torch
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# === 1️⃣ MODEL VE TOKENIZER YÜKLEME ===
MODEL_NAME = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

# === 2️⃣ LoRA AYARLARI ===
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    lora_dropout=0.1,
    bias="none",
    target_modules=["q_proj", "v_proj"],
)

# === 3️⃣ VERİ SETİ ===
dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", split="train", streaming=True, trust_remote_code=True)
dataset = dataset.shuffle(seed=42).take(10000)

def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, max_length=512)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# === 4️⃣ EĞİTİM AYARLARI ===
# === 4️⃣ EĞİTİM AYARLARI ===
training_args = TrainingArguments(
    output_dir="./mistral_lora",
    per_device_train_batch_size=1,
    gradient_accumulation_steps=16,
    learning_rate=5e-4,
    num_train_epochs=1,
    save_steps=500,
    save_total_limit=2,
    logging_dir="./logs",
    logging_steps=10,
    optim="adamw_torch",
    no_cuda=True,  # 🔥 ÇÖZÜM: Ana süreçte GPU'yu başlatmayı engelle
)


# === 5️⃣ GPU BAŞLATMA VE EĞİTİM ===
@spaces.GPU
def train_model():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # Modeli burada yükle
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float32).to(device)
    model = get_peft_model(model, lora_config)

    # TrainingArguments burada tanımlandı!
    training_args = TrainingArguments(
        output_dir="./mistral_lora",
        per_device_train_batch_size=1,
        gradient_accumulation_steps=16,
        learning_rate=5e-4,
        num_train_epochs=1,
        save_steps=500,
        save_total_limit=2,
        logging_dir="./logs",
        logging_steps=10,
        optim="adamw_torch",
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_datasets,
    )
    trainer.train()
    return "✅ Model Eğitimi Tamamlandı!"


# === 6️⃣ BAŞLATMA ===
if __name__ == "__main__":
    train_model()  # Eğitimi başlat