Solshine Tonic commited on
Commit
130318f
1 Parent(s): ebfcee5

Update app.py (#2)

Browse files

- Update app.py (8a1d1fcc38c182381df6e3ae64c149247786ddb5)


Co-authored-by: Joseph Pollack <[email protected]>

Files changed (1) hide show
  1. app.py +10 -5
app.py CHANGED
@@ -1,13 +1,18 @@
1
  import streamlit as st
2
  import pandas as pd
3
- from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
4
 
5
  #Note this should be used always in compliance with applicable laws and regulations if used with real patient data.
6
 
7
- # Load the tokenizer and model: pseudolab/K23_MiniMed by Tonic (Note: This is a large model and will take a while to download)
8
- # Config issues persist with this model, unfortunately. It may not be ready for use.
9
- tokenizer = AutoTokenizer.from_pretrained("pseudolab/K23_MiniMed")
10
- model = AutoModelForCausalLM.from_pretrained("pseudolab/K23_MiniMed")
 
 
 
 
11
 
12
  #Upload Patient Data
13
  uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
 
1
  import streamlit as st
2
  import pandas as pd
3
+ from transformers import pipeline, AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
4
+ from peft import PeftModel, PeftConfig
5
 
6
  #Note this should be used always in compliance with applicable laws and regulations if used with real patient data.
7
 
8
+ # Instantiate the Tokenizer
9
+ tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
10
+ tokenizer.pad_token = tokenizer.eos_token
11
+ tokenizer.padding_side = 'left'
12
+ # Load the PEFT model
13
+ peft_config = PeftConfig.from_pretrained("pseudolab/K23_MiniMed")
14
+ peft_model = MistralForCausalLM.from_pretrained("https://huggingface.co/HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True)
15
+ peft_model = PeftModel.from_pretrained(peft_model, "pseudolab/K23_MiniMed")
16
 
17
  #Upload Patient Data
18
  uploaded_file = st.file_uploader("Choose a CSV file", type="csv")