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from transformers import AutoTokenizer, MistralForCausalLM | |
import torch | |
import gradio as gr | |
import random | |
from textwrap import wrap | |
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM | |
from peft import PeftModel, PeftConfig | |
import torch | |
import gradio as gr | |
# 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(input_text, system_prompt="", max_length=512): | |
""" | |
Generates text using a large language model, given a prompt and a device. | |
Args: | |
input_text: The input text to generate a response for. | |
system_prompt: Optional system prompt. | |
max_length: Maximum length of the generated text. | |
Returns: | |
A string containing the generated text. | |
""" | |
# Modify the input text to include the desired format | |
formatted_input = f"""<s>[INST]{input_text}[/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", 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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") | |
class ChatBot: | |
def __init__(self): | |
self.history = [] | |
def predict(self, input_text): | |
# Encode user input | |
user_input_ids = tokenizer.encode(input_text, 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 | |
# 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 = [["<s>[INST] What is the proper treatment for bucal herpes?[/INST]"]] | |
iface = gr.Interface( | |
fn=bot.predict, | |
title=title, | |
description=description, | |
examples=examples, | |
inputs="text", | |
outputs="text", | |
theme="ParityError/Anime" | |
) | |
iface.launch() | |