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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
st.write("runningg")
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct",trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
text = st.text_area("Enter text....")
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
if text:
st.write("calculating")
out = pipe(messages, **generation_args)
st.write(out) |