diff --git a/examples/low_level_api/common.py b/examples/low_level_api/common.py
index 55d08db..1a51525 100644
--- a/examples/low_level_api/common.py
+++ b/examples/low_level_api/common.py
@@ -106,7 +106,7 @@ def gpt_params_parse(argv = None):
     parser.add_argument("--mirostat_lr", type=float, default=0.1, help="Mirostat learning rate, parameter eta",dest="mirostat_eta")
 
     parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
-    parser.add_argument("-p", "--prompt", type=str, default="", help="initial prompt",dest="prompt")
+    parser.add_argument("-p", "--prompt", type=str, default=None, help="initial prompt",dest="prompt")
     parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
     parser.add_argument("--session", type=str, default=None, help="file to cache model state in (may be large!)",dest="path_session")
     parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
diff --git a/examples/low_level_api/low_level_api_chat_cpp.py b/examples/low_level_api/low_level_api_chat_cpp.py
index 44b6d4a..02c09af 100644
--- a/examples/low_level_api/low_level_api_chat_cpp.py
+++ b/examples/low_level_api/low_level_api_chat_cpp.py
@@ -62,7 +62,7 @@ specified) expect poor results""", file=sys.stderr)
 		self.multibyte_fix = []
 
 		# model load
-		self.lparams = llama_cpp.llama_context_default_params()
+		self.lparams = llama_cpp.llama_model_default_params()
 		self.lparams.n_ctx = self.params.n_ctx
 		self.lparams.n_parts = self.params.n_parts
 		self.lparams.seed = self.params.seed
@@ -72,7 +72,11 @@ specified) expect poor results""", file=sys.stderr)
 
 		self.model = llama_cpp.llama_load_model_from_file(
 			self.params.model.encode("utf8"), self.lparams)
-		self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.lparams)
+
+		# Context Params.
+		self.cparams = llama_cpp.llama_context_default_params()
+
+		self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams)
 		if (not self.ctx):
 			raise RuntimeError(f"error: failed to load model '{self.params.model}'")
 
@@ -244,7 +248,7 @@ n_keep = {self.params.n_keep}
 	# tokenize a prompt
 	def _tokenize(self, prompt, bos=True):
 		_arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))()
-		_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8", errors="ignore"), _arr, len(_arr), bos)
+		_n = llama_cpp.llama_tokenize(self.model, prompt.encode("utf8", errors="ignore"), len(prompt), _arr, len(_arr), bos, False)
 		return _arr[:_n]
 
 	def set_color(self, c):
@@ -304,7 +308,7 @@ n_keep = {self.params.n_keep}
 					self.n_past += n_eval"""
 
 				if (llama_cpp.llama_eval(
-					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.params.n_threads
+					self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past
 				) != 0):
 					raise Exception("Failed to llama_eval!")
 
@@ -332,7 +336,7 @@ n_keep = {self.params.n_keep}
 				id = 0
 
 				logits = llama_cpp.llama_get_logits(self.ctx)
-				n_vocab = llama_cpp.llama_n_vocab(self.ctx)
+				n_vocab = llama_cpp.llama_n_vocab(self.model)
 
 				# Apply params.logit_bias map
 				for key, value in self.params.logit_bias.items():
@@ -349,12 +353,20 @@ n_keep = {self.params.n_keep}
 				last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx)
 
 				_arr = (llama_cpp.llama_token * last_n_repeat)(*self.last_n_tokens[len(self.last_n_tokens) - last_n_repeat:])
-				llama_cpp.llama_sample_repetition_penalty(self.ctx, candidates_p,
-					_arr,
-					last_n_repeat, llama_cpp.c_float(self.params.repeat_penalty))
-				llama_cpp.llama_sample_frequency_and_presence_penalties(self.ctx, candidates_p,
-					_arr,
-					last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
+				llama_cpp.llama_sample_repetition_penalties(
+					ctx=self.ctx,
+					candidates=candidates_p,
+					last_tokens_data = _arr,
+					penalty_last_n = last_n_repeat,
+					penalty_repeat = llama_cpp.c_float(self.params.repeat_penalty),
+					penalty_freq = llama_cpp.c_float(self.params.frequency_penalty),
+					penalty_present = llama_cpp.c_float(self.params.presence_penalty),
+				)
+				
+				# NOT PRESENT IN CURRENT VERSION ?
+				# llama_cpp.llama_sample_frequency_and_presence_penalti(self.ctx, candidates_p,
+				# 	_arr,
+				# 	last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
 
 				if not self.params.penalize_nl:
 					logits[llama_cpp.llama_token_nl()] = nl_logit
@@ -473,7 +485,7 @@ n_keep = {self.params.n_keep}
 	def token_to_str(self, token_id: int) -> bytes:
 		size = 32
 		buffer = (ctypes.c_char * size)()
-		n = llama_cpp.llama_token_to_piece_with_model(
+		n = llama_cpp.llama_token_to_piece(
 			self.model, llama_cpp.llama_token(token_id), buffer, size)
 		assert n <= size
 		return bytes(buffer[:n])
@@ -532,6 +544,9 @@ n_keep = {self.params.n_keep}
 			print(i,end="",flush=True)
 		self.params.input_echo = False
 
+        # Using string instead of tokens to check for antiprompt,
+		# It is more reliable than tokens for interactive mode.
+		generated_str = ""
 		while self.params.interactive:
 			self.set_color(util.CONSOLE_COLOR_USER_INPUT)
 			if (self.params.instruct):
@@ -546,6 +561,10 @@ n_keep = {self.params.n_keep}
 			try:
 				for i in self.output():
 					print(i,end="",flush=True)
+					generated_str += i
+					for ap in self.params.antiprompt:
+						if generated_str.endswith(ap):							
+							raise KeyboardInterrupt
 			except KeyboardInterrupt:
 				self.set_color(util.CONSOLE_COLOR_DEFAULT)
 				if not self.params.instruct:
@@ -561,7 +580,7 @@ if __name__ == "__main__":
 	time_now = datetime.now()
 	prompt = f"""Text transcript of a never ending dialog, where {USER_NAME} interacts with an AI assistant named {AI_NAME}.
 {AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer {USER_NAME}’s requests immediately and with details and precision.
-There are no annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
+Transcript below contains only the recorded dialog between two, without any annotations like (30 seconds passed...) or (to himself), just what {USER_NAME} and {AI_NAME} say aloud to each other.
 The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
 The transcript only includes text, it does not include markup like HTML and Markdown.
 
@@ -575,8 +594,11 @@ The transcript only includes text, it does not include markup like HTML and Mark
 {AI_NAME}: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
 {USER_NAME}: Name a color.
 {AI_NAME}: Blue
-{USER_NAME}:"""
+{USER_NAME}:   """
+	
 	params = gpt_params_parse()
+	if params.prompt is None and params.file is None:
+		params.prompt = prompt
 
 	with LLaMAInteract(params) as m:
 		m.interact()