# ------------------- LIBRARIES -------------------- # import os, logging, torch, streamlit as st from transformers import ( AutoTokenizer, AutoModelForCausalLM) st.balloons() # --------------------- HELPER --------------------- # def C(text, color="yellow"): color_dict: dict = dict( red="\033[01;31m", green="\033[01;32m", yellow="\033[01;33m", blue="\033[01;34m", magenta="\033[01;35m", cyan="\033[01;36m", ) color_dict[None] = "\033[0m" return ( f"{color_dict.get(color, None)}" f"{text}{color_dict[None]}") st.balloons() # ------------------ ENVIORNMENT ------------------- # os.environ["HF_ENDPOINT"] = "https://huggingface.co" device = ("cuda" if torch.cuda.is_available() else "cpu") logging.info(C("[INFO] "f"device = {device}")) st.balloons() # ------------------ INITITALIZE ------------------- # @st.cache def model_init(): tokenizer = AutoTokenizer.from_pretrained( "ckip-joint/bloom-1b1-zh") model = AutoModelForCausalLM.from_pretrained( "ckip-joint/bloom-1b1-zh", # Ref.: Eric, Thanks! # torch_dtype="auto", # device_map="auto", # Ref. for `half`: Chan-Jan, Thanks! ).eval().to(device) st.balloons() logging.info(C("[INFO] "f"Model init success!")) return tokenizer, model tokenizer, model = model_init() st.balloons() try: # ===================== INPUT ====================== # # prompt = "\u554F\uFF1A\u53F0\u7063\u6700\u9AD8\u7684\u5EFA\u7BC9\u7269\u662F\uFF1F\u7B54\uFF1A" #@param {type:"string"} prompt = st.text_input("Prompt: ") st.balloons() # =================== INFERENCE ==================== # if prompt: st.balloons() with torch.no_grad(): [texts_out] = model.generate( **tokenizer( prompt, return_tensors="pt" ).to(device)) st.balloons() output_text = tokenizer.decode(texts_out) st.balloons() st.markdown(output_text) st.balloons() except Exception as err: st.write(str(err)) st.snow()