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| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import streamlit as st | |
| def count_parameters(model,name): | |
| print("Counting parameters of model:{name}") | |
| return sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| m_id = st.text_input("model id", placeholder="K00B404/Merged_Beowolx-CodePro_Medusa2-14X-7B-Mistral-I-v0-2") | |
| with m_id: | |
| tokenizer = AutoTokenizer.from_pretrained(m_id) | |
| model = AutoModelForCausalLM.from_pretrained(m_id) | |
| st.info(f"{count_parameters(model, m_id)} parameters") |