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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 = 1.0
top_k = 1
result = await st.session_state.together_web3.language_model_inference(
from_dict(
data_class=LanguageModelInferenceRequest,
data={
"model": model_name_map[model_name],
"max_tokens": max_new_tokens,
"prompt": prompt,
"n": num_completions,
"temperature": temperature,
"top_k": top_k,
"top_p": top_p,
"stop": stop,
"seed": seed,
"echo": False,
}
),
)
generated_text = result.choices[0].text
for stop_word in stop:
if stop_word in result:
generated_text = generated_text[:generated_text.find(stop_word)]
return generated_text
def set_preset():
if st.session_state.preset == "Classification":
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"
elif st.session_state.preset == "Generation":
st.session_state.prompt = '''Please write a story given keywords.
Input: bear, honey
Story:'''
st.session_state.temperature = "1.0"
st.session_state.top_p = "0.5"
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: Where is Zurich?\nAnswer:"
if 'temperature' not in st.session_state:
st.session_state.temperature = "0.0"
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 'together_web3' not in st.session_state:
st.session_state.together_web3 = TogetherWeb3()
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', "10")
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 Configurations",
('Classification', 'Generation'),
on_change=set_preset,
key="preset",
horizontal=True
)
prompt = st.text_area(
"Prompt",
value=st.session_state.prompt,
max_chars=4096,
height=400,
)
generated_area = st.empty()
generated_area.text("(Generate here)")
button_submit = st.button("Submit")
if button_submit:
generated_area.text(prompt)
report_text = infer(
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() |