The result obtained is garbled

[Michelangelo-Y]

I test the finetuned model below：
python generate/adapter_robot.py
--prompt "Can you prepare me a sandwich?"
--quantize llm.int8
--max_new_tokens 512
--input "[Cabinet, PaperTowelRoll, Cup, ButterKnife, Shelf, Bowl, Fridge, CounterTop, Drawer, Potato, DishSponge, Bread, Statue, Spoon, SoapBottle, ShelvingUnit, HousePlant, Sink, Fork, Spatula, GarbageCan, Plate, Pot, Blinds, Kettle, Lettuce,Stool, Vase, Tomato, Mug, StoveBurner, StoveKnob, CoffeeMachine, LightSwitch, Toaster, Microwave, Ladle, SaltShaker, Apple, PepperShaker]"

but the result is garbled：

Response:

===================
token shape: torch.Size([211])
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Time for inference: 46.05 sec total, 11.12 tokens/sec
Memory used: 7.42 GB

[Gary3410]

I'm sorry for not getting back to you sooner.
This might be due to a problem with the lit-llama version, you can try to finetune the model on the latest version.