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import streamlit as st |
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from transformers import AutoModelForCausalLM, LlamaTokenizer |
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@st.cache_resource |
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def load(): |
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model = AutoModelForCausalLM.from_pretrained( |
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"stabilityai/japanese-stablelm-instruct-alpha-7b", |
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trust_remote_code=True, |
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) |
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tokenizer = LlamaTokenizer.from_pretrained( |
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"novelai/nerdstash-tokenizer-v1", |
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additional_special_tokens=['▁▁'], |
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) |
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return model, tokenizer |
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def generate(): |
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pass |
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st.header(":dna: 遺伝カウンセリング対話AI") |
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st.sidebar.header("Options") |
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st.session_state["options"]["temperature"] = st.sidebar.slider("temperature", min_value=0.0, max_value=2.0, step=0.1, value=st.session_state["options"]["temperature"]) |
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st.session_state["options"]["top_k"] = st.sidebar.slider("top_k", min_value=0, max_value=100, step=1, value=st.session_state["options"]["top_k"]) |
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st.session_state["options"]["top_p"] = st.sidebar.slider("top_p", min_value=0.0, max_value=1.0, step=0.1, value=st.session_state["options"]["top_p"]) |
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st.session_state["options"]["repetition_penalty"] = st.sidebar.slider("repetition_penalty", min_value=1.0, max_value=2.0, step=0.01, value=st.session_state["options"]["repetition_penalty"]) |
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st.session_state["options"]["system_prompt"] = st.sidebar.text_area("System Prompt", value=st.session_state["options"]["system_prompt"]) |
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model, tokenizer = load() |
