| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load model and tokenizer | |
| model_name = "mistralai/Codestral-22B-v0.1" | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # No need to move model to GPU, default is CPU | |
| # model.to("cpu") # This line can be omitted since it's already on CPU by default | |
| # Encode input tokens | |
| input_text = "Your input text here" | |
| tokens = tokenizer(input_text, return_tensors="pt").input_ids | |
| # Generate output | |
| generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True) | |
| # Decode generated tokens | |
| result = tokenizer.decode(generated_ids[0].tolist(), skip_special_tokens=True) | |
| print(result) | |