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Runtime error
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gen
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app.py
CHANGED
@@ -2,7 +2,6 @@
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import os, logging, torch, streamlit as st
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from transformers import (
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AutoTokenizer, AutoModelForCausalLM)
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st.balloons()
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# --------------------- HELPER --------------------- #
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def C(text, color="yellow"):
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@@ -18,18 +17,31 @@ def C(text, color="yellow"):
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return (
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f"{color_dict.get(color, None)}"
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f"{text}{color_dict[None]}")
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st.balloons()
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# ------------------ ENVIORNMENT ------------------- #
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os.environ["HF_ENDPOINT"] = "https://huggingface.co"
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device = ("cuda"
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if torch.cuda.is_available() else "cpu")
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logging.info(C("[INFO] "f"device = {device}"))
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st.balloons()
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# ------------------ INITITALIZE ------------------- #
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@st.cache
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def model_init():
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tokenizer = AutoTokenizer.from_pretrained(
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"ckip-joint/bloom-1b1-zh")
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model = AutoModelForCausalLM.from_pretrained(
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@@ -44,14 +56,10 @@ def model_init():
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return tokenizer, model
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tokenizer, model = model_init()
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st.balloons()
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try:
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# ===================== INPUT ====================== #
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# prompt = "\u554F\uFF1A\u53F0\u7063\u6700\u9AD8\u7684\u5EFA\u7BC9\u7269\u662F\uFF1F\u7B54\uFF1A" #@param {type:"string"}
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prompt = st.text_input("Prompt: ")
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st.balloons()
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# =================== INFERENCE ==================== #
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if prompt:
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@@ -59,13 +67,13 @@ try:
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with torch.no_grad():
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[texts_out] = model.generate(
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**tokenizer(
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prompt, return_tensors="pt"
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).to(device))
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st.balloons()
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output_text = tokenizer.decode(texts_out)
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st.balloons()
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st.markdown(output_text)
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except Exception as err:
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st.write(str(err))
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st.snow()
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import os, logging, torch, streamlit as st
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from transformers import (
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AutoTokenizer, AutoModelForCausalLM)
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# --------------------- HELPER --------------------- #
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def C(text, color="yellow"):
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return (
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f"{color_dict.get(color, None)}"
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f"{text}{color_dict[None]}")
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# ------------------ ENVIORNMENT ------------------- #
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os.environ["HF_ENDPOINT"] = "https://huggingface.co"
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device = ("cuda"
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if torch.cuda.is_available() else "cpu")
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logging.info(C("[INFO] "f"device = {device}"))
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# ------------------ INITITALIZE ------------------- #
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@st.cache
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def model_init():
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from transformers import GenerationConfig
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# generation_config, unused_kwargs = GenerationConfig.from_pretrained(
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# "ckip-joint/bloom-1b1-zh",
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# max_new_tokens=200,
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# return_unused_kwargs=True)
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tokenizer = AutoTokenizer.from_pretrained(
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"ckip-joint/bloom-1b1-zh")
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model = AutoModelForCausalLM.from_pretrained(
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return tokenizer, model
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tokenizer, model = model_init()
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try:
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# ===================== INPUT ====================== #
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prompt = st.text_input("Prompt: ")
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# =================== INFERENCE ==================== #
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if prompt:
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with torch.no_grad():
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[texts_out] = model.generate(
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**tokenizer(
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prompt, return_tensors="pt",
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max_new_tokens=200,
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).to(device))
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output_text = tokenizer.decode(texts_out)
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st.balloons()
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st.markdown(output_text)
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except Exception as err:
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st.write(str(err))
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st.snow()
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