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import streamlit as st | |
import requests | |
import time | |
from ast import literal_eval | |
def infer( | |
prompt, | |
model_name, | |
max_new_tokens=10, | |
temperature=0.0, | |
top_p=1.0, | |
top_k=40, | |
num_completions=1, | |
seed=42, | |
stop="\n" | |
): | |
model_name_map = { | |
"GPT-JT-6B-v1": "Together-gpt-JT-6B-v1", | |
} | |
max_new_tokens = int(max_new_tokens) | |
num_completions = int(num_completions) | |
temperature = float(temperature) | |
top_p = float(top_p) | |
stop = stop.split(";") | |
seed = seed | |
assert 0 <= max_new_tokens <= 256 | |
assert 1 <= num_completions <= 5 | |
assert 0.0 <= temperature <= 10.0 | |
assert 0.0 <= top_p <= 1.0 | |
if temperature == 0.0: | |
temperature = 0.01 | |
my_post_dict = { | |
"model": "Together-gpt-JT-6B-v1", | |
"prompt": prompt, | |
"top_p": top_p, | |
"top_k": top_k, | |
"temperature": temperature, | |
"max_tokens": max_new_tokens, | |
"stop": stop, | |
} | |
response = requests.get("https://staging.together.xyz/api/inference", params=my_post_dict).json() | |
generated_text = response['output']['choices'][0]['text'] | |
for stop_word in stop: | |
if stop_word in generated_text: | |
generated_text = generated_text[:generated_text.find(stop_word)] | |
st.session_state.updated = True | |
return generated_text | |
def set_preset(): | |
if st.session_state.preset == "Classification": | |
if not st.session_state.updated: | |
st.session_state.prompt = '''Please classify the given sentence. | |
Possible labels: | |
1. <label_0> | |
2. <label_1> | |
Input: <sentence_0> | |
Label: <label_0> | |
Input: <sentence_1> | |
Label:''' | |
st.session_state.temperature = "0.0" | |
st.session_state.top_p = "1.0" | |
st.session_state.max_new_tokens = "10" | |
elif st.session_state.preset == "Generation": | |
if not st.session_state.updated: | |
st.session_state.prompt = '''Please write a story given keywords. | |
Input: bear, honey | |
Story: Once upon a time,''' | |
st.session_state.temperature = "0.8" | |
st.session_state.top_p = "0.9" | |
st.session_state.max_new_tokens = "100" | |
else: | |
pass | |
def main(): | |
if 'preset' not in st.session_state: | |
st.session_state.preset = "Classification" | |
if 'prompt' not in st.session_state: | |
st.session_state.prompt = "Please answer the following question:\n\nQuestion: In which country is Zurich located?\nAnswer:" | |
if 'temperature' not in st.session_state: | |
st.session_state.temperature = "0.8" | |
if 'top_p' not in st.session_state: | |
st.session_state.top_p = "1.0" | |
if 'top_k' not in st.session_state: | |
st.session_state.top_k = "40" | |
if 'max_new_tokens' not in st.session_state: | |
st.session_state.max_new_tokens = "10" | |
if 'updated' not in st.session_state: | |
st.session_state.updated = False | |
st.title("GPT-JT") | |
col1, col2 = st.columns([1, 3]) | |
with col1: | |
model_name = st.selectbox("Model", ["GPT-JT-6B-v1"]) | |
max_new_tokens = st.text_input('Max new tokens', st.session_state.max_new_tokens) | |
temperature = st.text_input('temperature', st.session_state.temperature) | |
top_k = st.text_input('top_k', st.session_state.top_k) | |
top_p = st.text_input('top_p', st.session_state.top_p) | |
# num_completions = st.text_input('num_completions (only the best one will be returend)', "1") | |
num_completions = "1" | |
stop = st.text_input('stop, split by;', r'\n') | |
# seed = st.text_input('seed', "42") | |
seed = "42" | |
with col2: | |
# preset = st.radio( | |
# "Recommended Templates", | |
# ('Classification', 'Generation'), | |
# on_change=set_preset, | |
# key="preset", | |
# horizontal=True | |
# ) | |
prompt_area = st.empty() | |
prompt = prompt_area.text_area( | |
"Prompt", | |
value=st.session_state.prompt, | |
max_chars=4096, | |
height=300, | |
) | |
generated_area = st.empty() | |
generated_area.text("(Generate here)") | |
button_submit = st.button("Submit") | |
if button_submit: | |
generated_area.text(prompt) | |
report_text = infer( | |
st.session_state.prompt, model_name=model_name, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, | |
num_completions=num_completions, seed=seed, stop=literal_eval("'''"+stop+"'''"), | |
) | |
generated_area.text(prompt + report_text) | |
if __name__ == '__main__': | |
main() | |