Update app.py

app.py

@@ -18,55 +18,56 @@ print(zero.device) # <-- 'cpu' 🤔
 @spaces.GPU
 def greet(n):
     print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
-
-print(zero.device) # <-- 'cuda:0' 🤗
-
-
+        
+    
+    print(zero.device) # <-- 'cuda:0' 🤗
+    
+    
+    
+    torch_dtype = torch.float32  # float32 seçtik çünkü CPU'da bf16 genelde yok
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype)
+    
+    # === 3️⃣ LoRA AYARLARI ===
+    lora_config = LoraConfig(
+        r=8,              
+        lora_alpha=32,    
+        lora_dropout=0.1, 
+        bias="none",
+        target_modules=["q_proj", "v_proj"], 
+    )
+    model = get_peft_model(model, lora_config)
+    
+    # === 4️⃣ VERİ SETİ ===
+    dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", trust_remote_code=True)  # 🔥 ÇÖZÜM: trust_remote_code=True
+    train_data = dataset["train"].shuffle(seed=42).select(range(10000))  # Küçük subset
+    
+    # === 5️⃣ TOKENLEŞTİRME FONKSİYONU ===
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], truncation=True, max_length=512)
+    
+    tokenized_datasets = train_data.map(tokenize_function, batched=True)
+    
+    # === 6️⃣ EĞİTİM AYARLARI ===
+    training_args = TrainingArguments(
+        output_dir="./mistral_lora_cpu",
+        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",  # 🔥 ÇÖZÜM: bitsandbytes yerine adamw_torch
+    )
+    
+    # === 7️⃣ MODEL EĞİTİMİ ===
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets,
+    )
+    
+    trainer.train()
 demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-demo.launch()
-torch_dtype = torch.float32  # float32 seçtik çünkü CPU'da bf16 genelde yok
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype)
-
-# === 3️⃣ LoRA AYARLARI ===
-lora_config = LoraConfig(
-    r=8,              
-    lora_alpha=32,    
-    lora_dropout=0.1, 
-    bias="none",
-    target_modules=["q_proj", "v_proj"], 
-)
-model = get_peft_model(model, lora_config)
-
-# === 4️⃣ VERİ SETİ ===
-dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", trust_remote_code=True)  # 🔥 ÇÖZÜM: trust_remote_code=True
-train_data = dataset["train"].shuffle(seed=42).select(range(10000))  # Küçük subset
-
-# === 5️⃣ TOKENLEŞTİRME FONKSİYONU ===
-def tokenize_function(examples):
-    return tokenizer(examples["text"], truncation=True, max_length=512)
-
-tokenized_datasets = train_data.map(tokenize_function, batched=True)
-
-# === 6️⃣ EĞİTİM AYARLARI ===
-training_args = TrainingArguments(
-    output_dir="./mistral_lora_cpu",
-    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",  # 🔥 ÇÖZÜM: bitsandbytes yerine adamw_torch
-)
-
-# === 7️⃣ MODEL EĞİTİMİ ===
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets,
-)
-
-trainer.train()
+demo.launch()