Update app.py

app.py

@@ -24,15 +24,8 @@ import accelerate
 # default_persist_directory = './chroma_HF/'
 
 llm_name0 = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-llm_name1 = "mistralai/Mistral-7B-Instruct-v0.2"
-llm_name2 = "mistralai/Mistral-7B-Instruct-v0.1"
-llm_name3 = "meta-llama/Llama-2-7b-chat-hf"
-llm_name4 = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-llm_name5 = "microsoft/phi-2"
-llm_name6 = "mosaicml/mpt-7b-instruct"
-llm_name7 = "tiiuae/falcon-7b-instruct"
-llm_name8 = "google/flan-t5-xxl"
-list_llm = [llm_name0, llm_name1, llm_name2, llm_name3, llm_name4, llm_name5, llm_name6, llm_name7, llm_name8]
+
+list_llm = [llm_name0]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
 # Load PDF document and create doc splits
@@ -78,25 +71,6 @@ def load_db():
 # Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     progress(0.1, desc="Initializing HF tokenizer...")
-    # HuggingFacePipeline uses local model
-    # Note: it will download model locally...
-    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
-    # progress(0.5, desc="Initializing HF pipeline...")
-    # pipeline=transformers.pipeline(
-    #     "text-generation",
-    #     model=llm_model,
-    #     tokenizer=tokenizer,
-    #     torch_dtype=torch.bfloat16,
-    #     trust_remote_code=True,
-    #     device_map="auto",
-    #     # max_length=1024,
-    #     max_new_tokens=max_tokens,
-    #     do_sample=True,
-    #     top_k=top_k,
-    #     num_return_sequences=1,
-    #     eos_token_id=tokenizer.eos_token_id
-    #     )
-    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
     
     # HuggingFaceHub uses HF inference endpoints
     progress(0.5, desc="Initializing HF Hub...")