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from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
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 = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
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 = 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 = [["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()
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