Update app.py

app.py

@@ -25,7 +25,7 @@ import re
 
 
 # default_persist_directory = './chroma_HF/'
-list_llm = ["HuggingFaceH4/zephyr-7b-beta", "mistralai/Mistral-7B-Instruct-v0.2"]
+list_llm = ["HuggingFaceH4/zephyr-7b-beta"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
 
@@ -97,16 +97,7 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
     # Use of trust_remote_code as model_kwargs
     # Warning: langchain issue
     # URL: https://github.com/langchain-ai/langchain/issues/6080
-    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # 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 in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
+    if llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
         raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
@@ -114,34 +105,7 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
             max_new_tokens = max_tokens,
             top_k = top_k,
         )
-    elif llm_model == "microsoft/phi-2":
-        raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain 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"}
-            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 = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # 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 = HuggingFaceEndpoint(
-            repo_id=llm_model, 
-            # 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 = HuggingFaceEndpoint(
             repo_id=llm_model,