update

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-MODEL_NAME = "osmankoc/llama-2-7b-zoa"
+MODEL_NAME = "Qwen/Qwen2.5-Coder-32B-Instruct"
 
 # Model ve tokenizer'ı önceden yükle
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
@@ -14,14 +14,30 @@ model = AutoModelForCausalLM.from_pretrained(
 # ZeroGPU için model GPU'ya sadece gerektiğinde yüklenecek
 @spaces.GPU
 def generate(prompt):
-    system_prompt = (
-        "Generate HTML code using Tailwind CSS framework and Shadcn UI components. Add HTML tags to the code. Don't forget to use the correct classes. Don't write inline styles and descriptions. "
-        "Here is the user prompt: "
+    messages = [
+        {"role": "system", "content": "You are HTML Web Developer. enerate HTML code using Tailwind CSS framework and Shadcn UI components. Add HTML tags to the code. Don't forget to use the correct classes. Don't write inline styles and descriptions."},
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
     )
-    full_prompt = system_prompt + prompt
-    inputs = tokenizer(full_prompt, return_tensors="pt").to("cuda")
-    output = model.generate(**inputs, max_length=2500)
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
+    
+    # output = model.generate(**inputs, max_length=2500)
+    # response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
     return response
 
 # Gradio UI (Basit bir API arayüzü gibi çalışacak)