Use HuggingFaceEndpoint

app.py

@@ -9,7 +9,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
-from langchain.llms import HuggingFaceHub
+from langchain_community.llms import HuggingFaceEndpoint
 
 from pathlib import Path
 import chromadb
@@ -101,32 +101,50 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
     # Warning: langchain issue
     # URL: https://github.com/langchain-ai/langchain/issues/6080
     if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-        llm = HuggingFaceHub(
+        llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+            load_in_8bit = True,
         )
     elif llm_model == "microsoft/phi-2":
         raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
-        llm = HuggingFaceHub(
+        llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
+            trust_remote_code = True,
+            torch_dtype = "auto",
         )
     elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
-        llm = HuggingFaceHub(
+        llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
+            temperature = temperature,
+            max_new_tokens = 250,
+            top_k = top_k,
         )
     elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
         raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
-        llm = HuggingFaceHub(
+        llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
         )
     else:
-        llm = HuggingFaceHub(
+        llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
             # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+            temperature = temperature,
+            max_new_tokens = max_tokens,
+            top_k = top_k,
         )
     
     progress(0.75, desc="Defining buffer memory...")