change llm to lm

s2s_pipeline.py

@@ -445,19 +445,19 @@ class WhisperSTTHandler(BaseHandler):
 
 @dataclass
 class LanguageModelHandlerArguments:
-    llm_model_name: str = field(
+    lm_model_name: str = field(
         default="microsoft/Phi-3-mini-4k-instruct",
         metadata={
             "help": "The pretrained language model to use. Default is 'microsoft/Phi-3-mini-4k-instruct'."
         }
     )
-    llm_device: str = field(
+    lm_device: str = field(
         default="cuda",
         metadata={
             "help": "The device type on which the model will run. Default is 'cuda' for GPU acceleration."
         }
     )
-    llm_torch_dtype: str = field(
+    lm_torch_dtype: str = field(
         default="float16",
         metadata={
             "help": "The PyTorch data type for the model and input tensors. One of `float32` (full-precision), `float16` or `bfloat16` (both half-precision)."
@@ -481,15 +481,15 @@ class LanguageModelHandlerArguments:
             "help": "The initial chat prompt to establish context for the language model. Default is 'You are a helpful AI assistant.'"
         }
     )
-    llm_gen_max_new_tokens: int = field(
+    lm_gen_max_new_tokens: int = field(
         default=128,
         metadata={"help": "Maximum number of new tokens to generate in a single completion. Default is 128."}
     )
-    llm_gen_temperature: float = field(
+    lm_gen_temperature: float = field(
         default=0.0,
         metadata={"help": "Controls the randomness of the output. Set to 0.0 for deterministic (repeatable) outputs. Default is 0.0."}
     )
-    llm_gen_do_sample: bool = field(
+    lm_gen_do_sample: bool = field(
         default=False,
         metadata={"help": "Whether to use sampling; set this to False for deterministic outputs. Default is False."}
     )
@@ -635,9 +635,9 @@ class ParlerTTSHandler(BaseHandler):
         framerate = self.model.audio_encoder.config.frame_rate
         self.play_steps = int(framerate * play_steps_s)
 
-    def process(self, llm_sentence):
-        console.print(f"[green]ASSISTANT: {llm_sentence}")
-        tokenized_prompt = self.prompt_tokenizer(llm_sentence, return_tensors="pt")
+    def process(self, lm_sentence):
+        console.print(f"[green]ASSISTANT: {lm_sentence}")
+        tokenized_prompt = self.prompt_tokenizer(lm_sentence, return_tensors="pt")
         prompt_input_ids = tokenized_prompt.input_ids.to(self.device)
         prompt_attention_mask = tokenized_prompt.attention_mask.to(self.device)
 
@@ -723,7 +723,7 @@ def main():
         torch._logging.set_logs(graph_breaks=True, recompiles=True, cudagraphs=True)
 
     prepare_args(whisper_stt_handler_kwargs, "stt")
-    prepare_args(language_model_handler_kwargs, "llm")
+    prepare_args(language_model_handler_kwargs, "lm")
     prepare_args(parler_tts_handler_kwargs, "tts") 
 
     stop_event = Event()
@@ -732,7 +732,7 @@ def main():
     send_audio_chunks_queue = Queue()
     spoken_prompt_queue = Queue() 
     text_prompt_queue = Queue()
-    llm_response_queue = Queue()
+    lm_response_queue = Queue()
     
     vad = VADHandler(
         stop_event,
@@ -747,15 +747,15 @@ def main():
         queue_out=text_prompt_queue,
         setup_kwargs=vars(whisper_stt_handler_kwargs),
     )
-    llm = LanguageModelHandler(
+    lm = LanguageModelHandler(
         stop_event,
         queue_in=text_prompt_queue,
-        queue_out=llm_response_queue,
+        queue_out=lm_response_queue,
         setup_kwargs=vars(language_model_handler_kwargs),
     )
     tts = ParlerTTSHandler(
         stop_event,
-        queue_in=llm_response_queue,
+        queue_in=lm_response_queue,
         queue_out=send_audio_chunks_queue,
         setup_args=(should_listen,),
         setup_kwargs=vars(parler_tts_handler_kwargs),
@@ -778,7 +778,7 @@ def main():
         )
 
     try:
-        pipeline_manager = ThreadManager([vad, tts, llm, stt, recv_handler, send_handler])
+        pipeline_manager = ThreadManager([vad, tts, lm, stt, recv_handler, send_handler])
         pipeline_manager.start()
 
     except KeyboardInterrupt: