diff --git a/backend/python/transformers/backend.py b/backend/python/transformers/backend.py
old mode 100755
new mode 100644
index b1e0d559..10603d2e
--- a/backend/python/transformers/backend.py
+++ b/backend/python/transformers/backend.py
@@ -22,9 +22,9 @@ import torch.cuda
 
 XPU=os.environ.get("XPU", "0") == "1"
 if XPU:
-    from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer
+    from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria
 else:
-    from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig, TextIteratorStreamer
+    from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria
 
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
@@ -246,28 +246,28 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 
         # Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
         sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-#        print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
-#        print("Embeddings:", sentence_embeddings, file=sys.stderr)
         return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
 
     async def _predict(self, request, context, streaming=False): 
         set_seed(request.Seed)
-        if request.TopP == 0:
-            request.TopP = 0.9
+        if request.TopP < 0 or request.TopP > 1:
+            request.TopP = 1
         
-        if request.TopK == 0:
-            request.TopK = 40
+        if request.TopK <= 0:
+            request.TopK = 50
+
+        if request.Temperature > 0 :
+            sample=True
+        else:
+            sample=False
+            request.TopP == None
+            request.TopK == None
+            request.Temperature == None
 
         prompt = request.Prompt
         if not request.Prompt and request.UseTokenizerTemplate and request.Messages:    
             prompt = self.tokenizer.apply_chat_template(request.Messages, tokenize=False, add_generation_prompt=True)
 
-        eos_token_id = self.tokenizer.eos_token_id
-        if request.StopPrompts:
-            eos_token_id = []
-            for word in request.StopPrompts:
-                eos_token_id.append(self.tokenizer.convert_tokens_to_ids(word))
-
         inputs = self.tokenizer(prompt, return_tensors="pt")
 
         if request.Tokens > 0:
@@ -281,6 +281,14 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             inputs = inputs.to("xpu")
             streaming = False
 
+        criteria=[]
+        if request.StopPrompts:
+            criteria = StoppingCriteriaList(
+                [
+                    StopStringCriteria(tokenizer=self.tokenizer, stop_strings=request.StopPrompts),
+                ]
+            )
+
         if streaming:
             streamer=TextIteratorStreamer(self.tokenizer,
                                         skip_prompt=True,
@@ -290,11 +298,14 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                         temperature=request.Temperature, 
                         top_p=request.TopP,
                         top_k=request.TopK, 
-                        do_sample=True,
+                        do_sample=sample,
                         attention_mask=inputs["attention_mask"],
-                        eos_token_id=eos_token_id,
+                        eos_token_id=self.tokenizer.eos_token_id,
                         pad_token_id=self.tokenizer.eos_token_id,
-                        streamer=streamer)
+                        streamer=streamer,
+                        stopping_criteria=criteria,
+                        use_cache=True,
+                        )
             thread=Thread(target=self.model.generate, kwargs=config)
             thread.start()
             generated_text = ""
@@ -311,18 +322,20 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                                     temperature=request.Temperature, 
                                     top_p=request.TopP,
                                     top_k=request.TopK, 
-                                    do_sample=True,
+                                    do_sample=sample,
                                     pad_token=self.tokenizer.eos_token_id)
             else:
-                outputs = self.model.generate(inputs["input_ids"],
+                outputs = self.model.generate(**inputs,
                         max_new_tokens=max_tokens, 
                         temperature=request.Temperature, 
                         top_p=request.TopP,
                         top_k=request.TopK, 
-                        do_sample=True,
-                        attention_mask=inputs["attention_mask"],
-                        eos_token_id=eos_token_id,
-                        pad_token_id=self.tokenizer.eos_token_id)
+                        do_sample=sample,
+                        eos_token_id=self.tokenizer.eos_token_id,
+                        pad_token_id=self.tokenizer.eos_token_id,
+                        stopping_criteria=criteria,
+                        use_cache=True,
+                        )
             generated_text = self.tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]
 
         if streaming: