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