.to on a PreTrainedModel throws a Pyright type check error. What is the correct way to put a model to the device that does not throw type check errors?

[nickeisenberg]

System Info

(venv) nicholas@B367309:tmp(master)$ transformers-cli env

Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.

• transformers version: 4.51.1
• Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.39
• Python version: 3.12.3
• Huggingface_hub version: 0.30.2
• Safetensors version: 0.5.3
• Accelerate version: 1.6.0
• Accelerate config: not found
• DeepSpeed version: not installed
• PyTorch version (GPU?): 2.6.0+cu126 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?:
• Using GPU in script?:
• GPU type: NVIDIA RTX 2000 Ada Generation Laptop GPU

Who can help?

No response

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

Here is a small snippet

from transformers.models.auto.modeling_auto import AutoModelForCausalLM
from transformers.models.llama.modeling_llama import LlamaForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/deepseek-coder-1.3b-instruct", torch_dtype=torch.float16
)
assert isinstance(model, LlamaForCausalLM)
model.to("cuda:0")

This code runs fine and correctly puts the model to the device, however, Pyright throws a pre-runtime type check error on the `model.to("cuda:0") call. This is the error,

Pyright: Argument of type "Literal['cuda:0']" cannot be assigned to parameter "self" of 
type "LlamaForCausalLM" in function "__call__".
"Literal['cuda:0']" is not assignable to "LlamaForCausalLM" [reportArgumentType]  

What is the correct way to put a model to the device that will satisfy the type checker?

Expected behavior

There should be know static type check error when doing model.to(<device>)

[Rocketknight1]

I'm afraid we don't have complete type hint support! If you run with strict type checking, you will almost certainly encounter these errors. We recommend relaxing your typing requirements.