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import streamlit as st
import time
import torch
from better_transformer import *
def main():
# Enable CUDA if available and load in tokenizer
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer, EMPTY_TOKENS = load_tokenizer(device)
st.title("Scaling Transformers")
st.subheader("UCLA DSU Project, Fall 2023")
st.markdown("Daniel Mendelevitch \n Terry Ming \n Casey Tattersall \n Sean Tjoa")
st.header("What Are Transformers? 🚗🔄🤖")
header_text = """A transformer is a specific type of neural network that uses a mechanism called self-attention to learn the context (and
thus meaning) of sequential data. Transformer-based models can be used in many different domains, such as processing language, predicting
the weather, or even generating images. \n\n You might be familiar with ChatGPT, a Transformer-based model which cost over \$100 million to train. \n In contrast, we spent \$40*.
"""
st.markdown(header_text)
st.header("Let's make some stories! 📖")
# Input from user
user_input = st.text_input("Enter your prompt:", placeholder="Write a prompt to make a story of your own or leave it empty for a random story!").strip()
if st.checkbox("Show Prompting Tips"):
st.markdown("Our model was trained on the TinyStories dataset, a collection of synthetic short stories generated by GPT-4. These stories only contain words and themes that a typical 3-4 year old would understand.")
st.markdown(
"""
- Use simple vocabulary - words and themes that would appear in a children's story
- Avoid using idioms - for example, instead of "hit the gym", say "went to the gym"
- Include plenty of descriptive adjectives
- The model often struggles with names - using common names and only including a person's first name can help
"""
)
## Default values for advanced settings
user_seed = None # Set to a value if we want to rig the "random" demo
generation_method = "top-k"
specified_k = 5
specified_nucleus = 0.5
specified_temperature = 0.9
max_tokens = 500
if st.checkbox("Show Advanced Settings"):
user_seed = st.number_input("Randomness Seed:", value = None, step = 1, placeholder="Use to replicate response", min_value = 1)
generation_method = st.selectbox("Method of Generation:", ("top-k", "multinomial", "temperature", "greedy", "nucleus"), index = 0).strip()
if generation_method == "top-k":
specified_k = st.number_input("Value for k:", value = 5, step = 1)
if generation_method == "nucleus":
specified_nucleus = st.number_input("Value for k:", value = 0.5, step = 0.05, min_value = 0.0, max_value = 1.0)
if generation_method == "temperature":
specified_temperature = st.number_input("Value for temperature:", value = 0.9, step = 0.05, min_value = 0.0, max_value = 1.0)
max_tokens = st.slider('Max Tokens Generated:', 100, 800, 500)
# model_version = st.radio("Which model would you like to use?", ["smoll", "beeg"])
# small_model = load_casey_model(tokenizer, device)
model = load_big_model(tokenizer, device)
model.to('cuda')
model.cuda()
if st.button('Write my story!'):
placeholder = st.empty()
# if model_version == 'smoll':
# model = load_casey_model(tokenizer, device)
# elif model_version == 'beeg':
# model = load_big_model(tokenizer, device)
# with placeholder.container():
# st.write("Model Loaded! Preparing to Generate...")
with st.spinner(""):
result = generate(model, tokenizer, device, method=generation_method, k=specified_k,
p_nucleus=specified_nucleus, temp=specified_temperature, max_new_tokens=max_tokens,
cond=user_input, deterministic=user_seed)
streamed_input = ""
for word in user_input.split(' '):
streamed_input += word
with placeholder.container():
st.markdown(f"**{streamed_input}**")
streamed_input += " "
time.sleep(0.1)
if user_input != "": ##conditional
result = result[len(user_input) + 3 :]
streamed_result = f"**{streamed_input[:-1]}**"
time.sleep(1)
else: ##unconditional
streamed_result = ""
for word in result.split(' '):
streamed_result += word + ' '
with placeholder.container():
st.write(streamed_result)
time.sleep(0.1)
if st.button('Clear Output'):
placeholder = st.empty()
if __name__ == "__main__":
main()
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