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import streamlit as st
from guardrails_genie.train.llama_guard import DatasetArgs, LlamaGuardFineTuner
def initialize_session_state():
st.session_state.llama_guard_fine_tuner = LlamaGuardFineTuner(streamlit_mode=True)
if "dataset_address" not in st.session_state:
st.session_state.dataset_address = ""
if "train_dataset_range" not in st.session_state:
st.session_state.train_dataset_range = 0
if "test_dataset_range" not in st.session_state:
st.session_state.test_dataset_range = 0
if "load_fine_tuner_button" not in st.session_state:
st.session_state.load_fine_tuner_button = False
if "is_fine_tuner_loaded" not in st.session_state:
st.session_state.is_fine_tuner_loaded = False
if "model_name" not in st.session_state:
st.session_state.model_name = ""
if "preview_dataset" not in st.session_state:
st.session_state.preview_dataset = False
if "evaluate_model" not in st.session_state:
st.session_state.evaluate_model = False
if "evaluation_batch_size" not in st.session_state:
st.session_state.evaluation_batch_size = None
if "evaluation_temperature" not in st.session_state:
st.session_state.evaluation_temperature = None
initialize_session_state()
st.title(":material/star: Fine-Tune LLama Guard")
dataset_address = st.sidebar.text_input("Dataset Address", value="")
st.session_state.dataset_address = dataset_address
if st.session_state.dataset_address != "":
train_dataset_range = st.sidebar.number_input(
"Train Dataset Range", value=0, min_value=0, max_value=252956
)
test_dataset_range = st.sidebar.number_input(
"Test Dataset Range", value=0, min_value=0, max_value=63240
)
st.session_state.train_dataset_range = train_dataset_range
st.session_state.test_dataset_range = test_dataset_range
model_name = st.sidebar.selectbox(
"Model Name",
["meta-llama/Prompt-Guard-86M"],
)
st.session_state.model_name = model_name
preview_dataset = st.sidebar.toggle("Preview Dataset")
st.session_state.preview_dataset = preview_dataset
evaluate_model = st.sidebar.toggle("Evaluate Model")
st.session_state.evaluate_model = evaluate_model
load_fine_tuner_button = st.sidebar.button("Load Fine-Tuner")
st.session_state.load_fine_tuner_button = load_fine_tuner_button
if st.session_state.load_fine_tuner_button:
with st.status("Loading Fine-Tuner"):
st.session_state.llama_guard_fine_tuner.load_dataset(
DatasetArgs(
dataset_address=st.session_state.dataset_address,
train_dataset_range=st.session_state.train_dataset_range,
test_dataset_range=st.session_state.test_dataset_range,
)
)
st.session_state.llama_guard_fine_tuner.load_model(
model_name=st.session_state.model_name
)
if st.session_state.preview_dataset:
st.session_state.llama_guard_fine_tuner.show_dataset_sample()
if st.session_state.evaluate_model:
st.session_state.llama_guard_fine_tuner.evaluate_model(
batch_size=32,
temperature=3.0,
)
st.session_state.is_fine_tuner_loaded = True
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