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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Model ve tokenizer'ı yükle
+model_name = "your_llama_3_1_8b_model_name"  # Doğru model adını buraya girin
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+def generate_text(prompt, max_length=100, temperature=0.7):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Parametrelerle birlikte çıktı üret
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        temperature=temperature,
+        do_sample=True,
+        top_p=0.95,
+        top_k=50,
+        num_return_sequences=1
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio arayüzünü oluştur
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=3, label="Prompt"),
+        gr.Slider(minimum=10, maximum=500, value=100, step=1, label="Maksimum Uzunluk"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Sıcaklık")
+    ],
+    outputs=gr.Textbox(label="Üretilen Metin"),
+    title="LLaMA 3.1 8B Metin Üreteci",
+    description="LLaMA 3.1 8B modeli kullanarak metin üretin."
+)
+
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch
+accelerate