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from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM | |
from peft import PeftModel, PeftConfig | |
import torch | |
import gradio as gr | |
# Use the base model's ID | |
base_model_id = "mistralai/Mistral-7B-v0.1" | |
model_directory = "Tonic/mistralmed" | |
# Instantiate the Models | |
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True) | |
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 = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") | |
peft_model = PeftModel.from_pretrained(base_model, "Tonic/mistralmed", token="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") | |
class ChatBot: | |
def __init__(self): | |
self.history = [] | |
def predict(self, input): | |
# Encode user input | |
user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt") | |
# Concatenate the user input with chat history | |
if self.history: | |
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(chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) | |
# Update chat history | |
self.history = response | |
# 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 boiling point of nitrogen"]] | |
iface = gr.Interface( | |
fn=bot.predict, | |
title=title, | |
description=description, | |
examples=examples, | |
inputs="text", | |
outputs="text", | |
theme="ParityError/Anime" | |
) | |
iface.launch() | |