Update app.py

app.py

@@ -1,11 +1,33 @@
-from transformers import pipeline, AutoTokenizer, AutoModel
+import torch
+from transformers import AutoModel, AutoTokenizer
 
-# Authentifizierung mit deinem Token
-hf_token = "HF_TOKEN"
+model_name = "Qwen/Qwen2.5-Math-RM-72B"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Pipeline-Beispiel
-pipe = pipeline("text-classification", model="Qwen/Qwen2.5-Math-RM-72B", trust_remote_code=True, token=hf_token)
+# Modell und Tokenizer laden
+model = AutoModel.from_pretrained(
+    model_name, 
+    device_map="auto", 
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+).eval()
 
-# Direktes Modell-Beispiel
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Math-RM-72B", trust_remote_code=True, token=hf_token)
-model = AutoModel.from_pretrained("Qwen/Qwen2.5-Math-RM-72B", trust_remote_code=True, token=hf_token)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Eingabe für das Gesprächsmodell erstellen
+chat = [
+    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
+    {"role": "user", "content": "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"}
+]
+
+# Vorbereitung des Eingabeformats
+conversation_str = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)
+
+# Tokenisierung der Eingabe
+input_ids = tokenizer.encode(conversation_str, return_tensors="pt", add_special_tokens=False).to(model.device)
+
+# Inferenz durchführen
+with torch.no_grad():
+    outputs = model(input_ids=input_ids)
+
+print(outputs[0])  # Anpassen je nach Ausgabeformat