feat: change model

Dockerfile

@@ -10,16 +10,11 @@ RUN apt-get update && apt-get install -y \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-# ENV PYTHONUNBUFFERED=1 \
-#     PORT=8000 \
-#     HF_HOME=/home/user/huggingface
-
 COPY src/ ./src/
-# RUN pip3 install poetry==2.1.3
-# RUN pip install -U "huggingface_hub"
 COPY requirements.txt ./
 RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
 
+# for cache permission
 RUN useradd -m -u 1000 user
 USER user
 ENV HOME=/home/user \
@@ -27,15 +22,6 @@ ENV HOME=/home/user \
 WORKDIR $HOME/app
 COPY --chown=user . $HOME/app
 
-# COPY pyproject.toml README.md /app
-# RUN poetry install
-# RUN poetry config virtualenvs.in-project true
-
-# ENV VIRTUAL_ENV=/app/.venv
-# ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
 EXPOSE 8501
-
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
 ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

src/streamlit_app.py

@@ -1,26 +1,12 @@
-import os
 import streamlit as st
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-# from huggingface_hub import notebook_login
-# from unsloth import FastLanguageModel, is_bfloat16_supported
 
 # model_id = "sentence-transformers/all-MiniLM-L6-v2"
 # model_id = "sentence-transformers/xlm-r-base-en-ko-nli-ststb"
-
 # model_id = "mistralai/Mistral-7B-Instruct-v0.1"
-# model_id = "meta-llama/Llama-3.2-1B"
-model_id = "choco-conoz/TwinLlama-3.1-8B"
-
-# processor = pipeline(
-#     "text-generation",
-#     model=model_id,
-#     model_kwargs={
-#         "torch_dtype": torch.float16,
-#         "quantization_config": {"load_in_4bit": True},
-#         "low_cpu_mem_usage": True,
-#     },
-# )
+model_id = "meta-llama/Llama-3.2-1B"
+# model_id = "choco-conoz/TwinLlama-3.1-8B"
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
@@ -67,29 +53,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-# >>> old
-# num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-# num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-# indices = np.linspace(0, 1, num_points)
-# theta = 2 * np.pi * num_turns * indices
-# radius = indices
-
-# x = radius * np.cos(theta)
-# y = radius * np.sin(theta)
-
-# df = pd.DataFrame({
-#     "x": x,
-#     "y": y,
-#     "idx": indices,
-#     "rand": np.random.randn(num_points),
-# })
-
-# st.altair_chart(alt.Chart(df, height=700, width=700)
-#     .mark_point(filled=True)
-#     .encode(
-#         x=alt.X("x", axis=None),
-#         y=alt.Y("y", axis=None),
-#         color=alt.Color("idx", legend=None, scale=alt.Scale()),
-#         size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-#     ))