change model file and using ctransformers directly instead of langchain.

app.py

@@ -8,8 +8,8 @@ from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 # from langchain.llms import LlamaCpp
 from langchain.vectorstores import Qdrant
 from qdrant_client.http import models
-from langchain.llms import CTransformers
-# from ctransformers import AutoModelForCausalLM
+# from langchain.llms import CTransformers
+from ctransformers import AutoModelForCausalLM
 
 
 
@@ -36,13 +36,13 @@ print("loading the LLM......................................")
 #     verbose=True,
 # )
 
-llm = CTransformers(
-    model="TheBloke/Llama-2-7B-Chat-GGUF/llama-2-7b-chat.Q8_0.gguf", 
-    callbacks=[StreamingStdOutCallbackHandler()],
-    temperature = 0.2,
-    max_new_tokens = 1000,
-    
-)
+llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGUF", 
+                                           model_file="llama-2-7b-chat.Q8_0.gguf", 
+                                           model_type="llama", 
+                                          #  config = ctransformers.hub.AutoConfig,
+                                           hf = True
+                                           )
+
 
 
 print("LLM loaded........................................")