import reflex as rx
p2 = '''
# Steps
### Dataset Selection
We begin with the layoric/labeled-multiple-choice-explained dataset, which includes reasoning provided by GPT-3.5-turbo. reasoning explanations serve as a starting point but may differ from Falcon's reasoning style.
0. 00-poe-generate-falcon-reasoning.ipynb: To align with falcon, we need to create a refined dataset: derek-thomas/labeled-multiple-choice-explained-falcon-reasoning.
1. 01-poe-dataset-creation.ipynb: Then we need to create our prompt experiments.
2. 02-autotrain.ipynb: We generate autotrain jobs on spaces to train our models.
3. 03-poe-token-count-exploration.ipynb: We do some quick analysis so we can optimize our TGI settings.
4. 04-poe-eval.ipynb: We finally evaluate our trained models.
**The flowchart is _Clickable_**
'''
def mermaid_svg():
with open('assets/prompt-order-experiment.svg', 'r') as file:
svg_content = file.read()
return rx.html(
f'{svg_content}
'
)
def page():
return rx.vstack(
rx.markdown(p2),
mermaid_svg(),
)