| import streamlit as st |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| |
| model_name = "Rimyy/MISTRAL-finetuneGSMdata1exp" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
|
|
| |
| def predict(prompt): |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(inputs.input_ids, max_length=256) |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
| |
| st.title("Modèle de génération de texte") |
| prompt = st.text_area("Entrez votre texte:") |
| if st.button("Générer"): |
| result = predict(prompt) |
| st.text_area("Résultat", value=result, height=200) |
|
|
