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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| MODEL_NAME = "Ronaldodev/llama" | |
| # Charger modèle et tokenizer UNE SEULE FOIS | |
| print("Chargement du modèle...") | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| def chat(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=200, | |
| do_sample=True, | |
| temperature=0.7 | |
| ) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| iface = gr.Interface( | |
| fn=chat, | |
| inputs=gr.Textbox(lines=5, placeholder="Écris ton message ici..."), | |
| outputs=gr.Textbox(), | |
| title="Assistant IA (Llama 3.2 1B CPU)", | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |