Update README.md

README.md

@@ -73,8 +73,6 @@ import gradio as gr
 import random
 from textwrap import wrap
 
-
-# Functions to Wrap the Prompt Correctly
 def wrap_text(text, width=90):
     lines = text.split('\n')
     wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
@@ -82,22 +80,11 @@ 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]"
 
-    # Encode the input text
     encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
     model_inputs = encodeds.to(device)
 
-    # Generate a response using the model
     output = model.generate(
         **model_inputs,
         max_length=max_length,
@@ -110,31 +97,19 @@ def multimodal_prompt(user_input, system_prompt="You are an expert medical analy
         do_sample=True
     )
 
-    # Decode the response
     response_text = tokenizer.decode(output[0], skip_special_tokens=True)
 
     return response_text
 
-# Define the device
 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"
 
-# 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.pad_token = tokenizer.eos_token
 tokenizer.padding_side = 'left'
 
-# Specify the configuration class for the model
-#model_config = AutoConfig.from_pretrained(base_model_id)
-
-# Load the PEFT model with the specified configuration
-#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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")
@@ -144,40 +119,27 @@ class ChatBot:
         self.history = []
 
     def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
-        # Combine user input and system prompt
         formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
 
-        # Encode user input
         user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
 
-        # Concatenate the user input with chat history
-        if len(self.history) > 0:
-            chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1)
-        else:
-            chat_history_ids = user_input_ids
-
-        # Generate a response using the PEFT model
-        response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
-
-        # Update chat history
-        self.history = chat_history_ids
+        response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
 
-        # Decode and return the response
         response_text = tokenizer.decode(response[0], skip_special_tokens=True)
         return response_text
 
 bot = ChatBot()
 
-title = "👋🏻Welcome to Tonic's MistralMed Chat🚀"
-description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together."
-examples = [["What is the proper treatment for buccal herpes?", "Please provide information on the most effective antiviral medications and home remedies for treating buccal herpes."]]
+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)."
+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 complete the answer"]]
 
 iface = gr.Interface(
     fn=bot.predict,
     title=title,
     description=description,
     examples=examples,
-    inputs=["text", "text"],  # Take user input and system prompt separately
+    inputs=["text", "text"],
     outputs="text",
     theme="ParityError/Anime"
 )