Update app.py

app.py

@@ -1,84 +1,113 @@
-import os
-import math
-import transformers
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+from peft import PeftModel, PeftConfig
 import torch
 import gradio as gr
-import sentencepiece
-
-title = "Welcome to Tonic's 🐋🐳Orca-2-13B!"
-description = "You can use [🐋🐳microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/Tonic1/TonicsOrca2?duplicate=true) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Big thanks to the HuggingFace Organisation for the Community Grant."
-
-# os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50'
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model_name = "microsoft/Orca-2-13b"
-# offload_folder = './model_weights'
-
-# if not os.path.exists(offload_folder):
-#     os.makedirs(offload_folder)
-
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-model = model.to(torch.bfloat16)
-model = model.to(device)
-
-
-class OrcaChatBot:
-    def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.system_message = system_message
-        self.conversation_history = None
-
-    def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9):
-        # Prepare the prompt
-        prompt = f"<|im_start|>system\n{self.system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" if self.conversation_history is None else self.conversation_history + f"<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
-
-        # Encode the prompt
-        inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
-        input_ids = inputs["input_ids"].to(self.model.device)
-
-        # Generate a response
-        output_ids = self.model.generate(
-            input_ids,
-            max_length=input_ids.shape[1] + max_new_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-            pad_token_id=self.tokenizer.eos_token_id,
-            do_sample=True  # Enable sampling-based generation
-        )
+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]
+    wrapped_text = '\n'.join(wrapped_lines)
+    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,
+        use_cache=True,
+        early_stopping=True,
+        bos_token_id=model.config.bos_token_id,
+        eos_token_id=model.config.eos_token_id,
+        pad_token_id=model.config.eos_token_id,
+        temperature=0.1,
+        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")
+peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
+peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed")
+
+class ChatBot:
+    def __init__(self):
+        self.history = []
+
+class ChatBot:
+    def __init__(self):
+        # Initialize the ChatBot class with an empty history
+        self.history = []
+
+    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
+        # Combine the user's input with the system prompt
+        formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"
+
+        # Encode the formatted input using the tokenizer
+        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")
+
+        # Generate a response using the PEFT model
+        response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)
+
+        # Decode the generated response to text
+        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
         
+        return response_text  # Return the generated response
 
-        # Decode the generated response
-        response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+bot = ChatBot()
 
-        # Update conversation history
-        self.conversation_history = self.tokenizer.decode(output_ids[0], skip_special_tokens=False)
-
-        return response
-
-Orca_bot = OrcaChatBot(model, tokenizer)
-
-def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty):
-    full_message = f"{system_message}\n{user_message}" if system_message else user_message
-    return Orca_bot.predict(full_message, temperature, max_new_tokens, top_p, repetition_penalty)
+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 provide a complete answer"]]
 
 iface = gr.Interface(
-    fn=gradio_predict,
+    fn=bot.predict,
     title=title,
     description=description,
-    inputs=[
-        gr.Textbox(label="Your Message", type="text", lines=3),
-        gr.Textbox(label="Introduce a Character Here or Set a Scene (system prompt)", type="text", lines=2),
-        gr.Slider(label="Max new tokens", value=1200, minimum=25, maximum=4096, step=1),
-        gr.Slider(label="Temperature", value=0.7, minimum=0.05, maximum=1.0, step=0.05),
-        gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05),
-        gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05)
-    ],
+    examples=examples,
+    inputs=["text", "text"],  # Take user input and system prompt separately
     outputs="text",
     theme="ParityError/Anime"
 )
 
-# Launch the Gradio interface
 iface.launch()