| | import os |
| | import streamlit as st |
| | from transformers import AutoModel, AutoTokenizer |
| |
|
| | st.title("HuggingFace Model Loader & Saver") |
| | st.write("Load a model from HuggingFace and save it locally. Edit parameters below:") |
| |
|
| | |
| | model_name = st.text_input("Model Name", value="openai-gpt", help="Enter the HuggingFace model name (e.g., openai-gpt)") |
| | save_dir = st.text_input("Save Directory", value="./hugging", help="Local directory to save the model") |
| | additional_models = st.multiselect( |
| | "Additional Models", |
| | options=["bert-base-uncased", "gpt2", "roberta-base"], |
| | help="Select additional models to load and save" |
| | ) |
| |
|
| | if st.button("Load and Save Model"): |
| | st.write("### Processing Primary Model") |
| | try: |
| | st.write(f"Loading **{model_name}** ...") |
| | model = AutoModel.from_pretrained(model_name) |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | |
| | model_save_path = os.path.join(save_dir, model_name.replace("/", "_")) |
| | os.makedirs(model_save_path, exist_ok=True) |
| | model.save_pretrained(model_save_path) |
| | st.success(f"Model **{model_name}** saved to `{model_save_path}`") |
| | except Exception as e: |
| | st.error(f"Error loading/saving model **{model_name}**: {e}") |
| |
|
| | if additional_models: |
| | st.write("### Processing Additional Models") |
| | for m in additional_models: |
| | try: |
| | st.write(f"Loading **{m}** ...") |
| | model = AutoModel.from_pretrained(m) |
| | tokenizer = AutoTokenizer.from_pretrained(m) |
| | model_save_path = os.path.join(save_dir, m.replace("/", "_")) |
| | os.makedirs(model_save_path, exist_ok=True) |
| | model.save_pretrained(model_save_path) |
| | st.success(f"Model **{m}** saved to `{model_save_path}`") |
| | except Exception as e: |
| | st.error(f"Error loading/saving model **{m}**: {e}") |
