请问5090D有什么办法能使用呢？

这是我使用118安装后运行的结果，但是我的cuda是13.0，我安装了cuda130的pytorch后，程序中会有的模块找不到。
python run_dpsk_ocr_pdf.py
The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
0it [00:00, ?it/s]
INFO 10-30 00:35:19 [__init__.py:239] Automatically detected platform cuda.
INFO 10-30 00:35:23 [config.py:456] Overriding HF config with {'architectures': ['DeepseekOCRForCausalLM']}
INFO 10-30 00:35:24 [config.py:717] This model supports multiple tasks: {'classify', 'score', 'generate', 'reward', 'embed'}. Defaulting to 'generate'.
INFO 10-30 00:35:24 [llm_engine.py:240] Initializing a V0 LLM engine (v0.8.5) with config: model='deepseek-ai/DeepSeek-OCR', speculative_config=None, tokenizer='deepseek-ai/DeepSeek-OCR', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=deepseek-ai/DeepSeek-OCR, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=True, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":104}, use_cached_outputs=False,
WARNING 10-30 00:35:26 [interface.py:314] Using 'pin_memory=False' as WSL is detected. This may slow down the performance.
INFO 10-30 00:35:26 [cuda.py:292] Using Flash Attention backend.
/home/eashion/.pyenv/versions/dsk-ocr/lib/python3.12/site-packages/torch/cuda/__init__.py:235: UserWarning:
NVIDIA GeForce RTX 5090 D with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_37 sm_90.
If you want to use the NVIDIA GeForce RTX 5090 D GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/