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import streamlit as st
import time
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("Short Story Transformer Demo")
st.subheader("UCLA DSU Project, Fall 2023")
st.markdown("By Daniel Mendelevitch, Terry Ming, Casey Tattersall, Sean Tjoa")
st.header("Data and Training")
st.markdown("""We used the dataset from Microsoft Research's [TinyStories Paper](https://arxiv.org/pdf/2305.07759.pdf) (Eldan and Li),
which consists of 2.1 million synthetic short children's stories generated by GPT-4, to train a Transformer LLM that we built from scratch in PyTorch.""")
st.markdown("""Our model uses EleutherAI's [gpt-neo-1.3B tokenizer](https://huggingface.co/EleutherAI/gpt-neo-1.3B) (vocab size 50,257) and consists of 8 transformer blocks,
16 attention heads, and an embedding dimension of 768, for a total of ~56M non-embedding parameters. The model was trained on 8 H100 GPUs for 7 hours, achieving a cross-entropy validation loss of 1.16,
which is superior to all models in the TinyStories paper (likely due to a larger vocab size and far more compute).""")
st.markdown("""Despite the simple themes and limited vocabulary present in the training data, the model is
quite effective at generating new short stories. **Try it out below!**""")
st.header("Prompting Tips")
st.markdown(
"The model can struggle with some prompts, especially those outside of its limited domain. If a response isn't satisfactory, try repeating the generation, or make the following modifications:"
)
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 sticking with first names only can help.**
"""
)
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()
## Default values for advanced settings
user_seed = None # Remove if we're not rigging the "random" demo
generation_method = "top-k"
specified_k = 5
specified_nucleus = 0.5
specified_temperature = 0.4
max_tokens = 750
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", "nucleus", "temperature", "multinomial", "greedy"), 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("Nucleus Cutoff:", 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 = 0.4, step = 0.05, min_value = 0.0, max_value = 1.0)
max_tokens = st.slider('Max Tokens Generated:', 50, 750, 750)
# 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)
st.markdown("\n")
st.markdown("\n")
st.markdown("\n")
st.markdown("\n")
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.markdown(f"{streamed_result}")
time.sleep(0.1)
if st.button('Clear Output'):
placeholder = st.empty()
st.markdown('####')
st.caption(r'Data Attribution: Tinystories (License: CDLA-Sharing-1.0) https://arxiv.org/abs/2305.07759')
if __name__ == "__main__":
main()
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