Update app.py

app.py

@@ -1,3 +1,4 @@
+
 from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
 from peft import PeftModel, PeftConfig
 import torch
@@ -13,14 +14,6 @@ def wrap_text(text, width=90):
     return wrapped_text
 
 def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):
-    """
-    Generates text using a large language model, given a user input and a system prompt.
-    Args:
-        user_input: The user's input text to generate a response for.
-        system_prompt: Optional system prompt.
-    Returns:
-        A string containing the generated text.
-    """
     # Combine user input and system prompt
     formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
 
@@ -50,12 +43,12 @@ def multimodal_prompt(user_input, system_prompt="You are an expert medical analy
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Use the base model's ID
-base_model_id = "mistralai/Mistral-7B-v0.1"
-model_directory = "Tonic/mistralmed"
+base_model_id = "HuggingFaceH4/zephyr-7b-beta"
+model_directory = "pseudolab/K23_MiniMed"
 
 # Instantiate the Tokenizer
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
-# tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", trust_remote_code=True, padding_side="left")
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True, padding_side="left")
+# tokenizer = AutoTokenizer.from_pretrained("pseudolab/K23_MiniMed", trust_remote_code=True, padding_side="left")
 tokenizer.pad_token = tokenizer.eos_token
 tokenizer.padding_side = 'left'
 
@@ -66,9 +59,9 @@ tokenizer.padding_side = 'left'
 #peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config)
 
 # Load the PEFT model
-peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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")
-peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
-peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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")
+peft_config = PeftConfig.from_pretrained("pseudolab/K23_MiniMed")
+peft_model = MistralForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True)
+peft_model = PeftModel.from_pretrained(peft_model, "pseudolab/K23_MiniMed")
 
 class ChatBot:
     def __init__(self):
@@ -97,7 +90,7 @@ class ChatBot:
 bot = ChatBot()
 
 title = "👋🏻토닉의 미스트랄메드 채팅에 오신 것을 환영합니다🚀👋🏻Welcome to Tonic's MistralMed Chat🚀"
-description = "이 공간을 사용하여 현재 모델을 테스트할 수 있습니다. [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) 또는 이 공간을 복제하고 로컬 또는 🤗HuggingFace에서 사용할 수 있습니다. [Discord에서 함께 만들기 위해 Discord에 가입하십시오](https://discord.gg/VqTxc76K3u). You can use this Space to test out the current model [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
+description = "이 공간을 사용하여 현재 모델을 테스트할 수 있습니다. [pseudolab/K23_MiniMed](https://huggingface.co/pseudolab/K23_MiniMed) 또는 이 공간을 복제하고 로컬 또는 🤗HuggingFace에서 사용할 수 있습니다. [Discord에서 함께 만들기 위해 Discord에 가입하십시오](https://discord.gg/VqTxc76K3u). You can use this Space to test out the current model [pseudolab/K23_MiniMed](https://huggingface.co/pseudolab/K23_MiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
 examples = [["[Question:] What is the proper treatment for buccal herpes?", "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]]
 
 iface = gr.Interface(