Update app.py

app.py

@@ -5,18 +5,6 @@ import gradio as gr
 import random
 from textwrap import wrap
 
-# Define the PeftConfig
-peft_config = PeftConfig(
-    max_length=500,
-    use_cache=True,
-    early_stopping=False,
-    bos_token_id=peft_model.config.bos_token_id,
-    eos_token_id=peft_model.config.eos_token_id,
-    pad_token_id=peft_model.config.eos_token_id,
-    temperature=0.4,
-    do_sample=True
-)
-
 # Functions to Wrap the Prompt Correctly
 def wrap_text(text, width=90):
     lines = text.split('\n')
@@ -62,21 +50,20 @@ 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"
+base_model_id = "tiiuae/falcon-7b-instruct"
 model_directory = "Tonic/GaiaMiniMed"
 
 # Instantiate the Tokenizer
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct", 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'
 
 # Load the GaiaMiniMed model with the specified configuration
 # Load the Peft model with a specific configuration
-peft_model = PeftModel.from_pretrained("Tonic/GaiaMiniMed", config=peft_config)
+peft_model = PeftModel.from_pretrained("Tonic/GaiaMiniMed")
 peft_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b-instruct")
-# Now you can use peft_model without any NameError
-peft_model = peft_model.to_bettertransformer("tiiuae/falcon-7b-instruct")
+peft_model = PeftModel.from_pretrained(peft_model, "Tonic/GaiaMiniMed")
 
 
 # Specify the configuration class for the model