| import streamlit as st | |
| from ctransformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("Mykes/med_gemma7b_gguf", model_file="unsloth.Q4_K_M.gguf") | |
| tokenizer = AutoTokenizer.from_pretrained(model) | |
| input_text = st.textarea('text') | |
| if text: | |
| input_ids = tokenizer(input_text, return_tensors="pt") | |
| outputs = model.generate(**input_ids) | |
| st.write(outputs) | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # model_id = "Mykes/med_gemma7b_gguf" | |
| # filename = "unsloth.Q4_K_M.gguf" | |
| # tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename) | |
| # model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename) | |
| # input_text = st.textarea('text') | |
| # if text: | |
| # input_ids = tokenizer(input_text, return_tensors="pt") | |
| # outputs = model.generate(**input_ids) | |
| # st.write(outputs) |