text: Title: [Bug]: vLLM ModelConfig doesn't pass hf_overrides to get_hf_image_processor_config, which could contain auth token for hugging face (not in ENV)
  Body: ### Your current environment
  
  <details>
  <summary>The output of `python collect_env.py`</summary>
  
  ```text
  INFO 03-14 23:03:57 __init__.py:183] Automatically detected platform cuda.
  Collecting environment information...
  PyTorch version: 2.5.1+cu124
  Is debug build: False
  CUDA used to build PyTorch: 12.4
  ROCM used to build PyTorch: N/A
  
  OS: Linux Mint 21.3 (x86_64)
  GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
  Clang version: Could not collect
  CMake version: version 3.22.1
  Libc version: glibc-2.35
  
  Python version: 3.12.8 | packaged by Anaconda, Inc. | (main, Dec 11 2024, 16:31:09) [GCC 11.2.0] (64-bit runtime)
  Python platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
  Is CUDA available: True
  CUDA runtime version: 12.4.131
  CUDA_MODULE_LOADING set to: LAZY
  GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
  Nvidia driver version: 550.144.03
  cuDNN version: Could not collect
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  Is XNNPACK available: True
  
  CPU:
  Architecture:                         x86_64
  CPU op-mode(s):                       32-bit, 64-bit
  Address sizes:                        48 bits physical, 48 bits virtual
  Byte Order:                           Little Endian
  CPU(s):                               32
  On-line CPU(s) list:                  0-31
  Vendor ID:                            AuthenticAMD
  Model name:                           AMD Ryzen 9 7950X3D 16-Core Processor
  CPU family:                           25
  Model:                                97
  Thread(s) per core:                   2
  Core(s) per socket:                   16
  Socket(s):                            1
  Stepping:                             2
  CPU max MHz:                          5858.0000
  CPU min MHz:                          545.0000
  BogoMIPS:                             8399.98
  Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d
  Virtualization:                       AMD-V
  L1d cache:                            512 KiB (16 instances)
  L1i cache:                            512 KiB (16 instances)
  L2 cache:                             16 MiB (16 instances)
  L3 cache:                             128 MiB (2 instances)
  NUMA node(s):                         1
  NUMA node0 CPU(s):                    0-31
  Vulnerability Gather data sampling:   Not affected
  Vulnerability Itlb multihit:          Not affected
  Vulnerability L1tf:                   Not affected
  Vulnerability Mds:                    Not affected
  Vulnerability Meltdown:               Not affected
  Vulnerability Mmio stale data:        Not affected
  Vulnerability Reg file data sampling: Not affected
  Vulnerability Retbleed:               Not affected
  Vulnerability Spec rstack overflow:   Mitigation; Safe RET
  Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
  Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
  Vulnerability Srbds:                  Not affected
  Vulnerability Tsx async abort:        Not affected
  
  Versions of relevant libraries:
  [pip3] flake8==7.1.1
  [pip3] mypy-extensions==1.0.0
  [pip3] numpy==1.26.4
  [pip3] numpydoc==1.7.0
  [pip3] nvidia-cublas-cu12==12.4.5.8
  [pip3] nvidia-cuda-cupti-cu12==12.4.127
  [pip3] nvidia-cuda-nvrtc-cu12==12.4.127
  [pip3] nvidia-cuda-runtime-cu12==12.4.127
  [pip3] nvidia-cudnn-cu12==9.1.0.70
  [pip3] nvidia-cufft-cu12==11.2.1.3
  [pip3] nvidia-curand-cu12==10.3.5.147
  [pip3] nvidia-cusolver-cu12==11.6.1.9
  [pip3] nvidia-cusparse-cu12==12.3.1.170
  [pip3] nvidia-cusparselt-cu12==0.6.2
  [pip3] nvidia-ml-py==12.570.86
  [pip3] nvidia-nccl-cu12==2.21.5
  [pip3] nvidia-nvjitlink-cu12==12.4.127
  [pip3] nvidia-nvtx-cu12==12.4.127
  [pip3] onnxruntime==1.20.1
  [pip3] pyzmq==26.2.0
  [pip3] rapidocr-onnxruntime==1.3.24
  [pip3] sentence-transformers==3.3.1
  [pip3] torch==2.5.1
  [pip3] torchvision==0.20.1
  [pip3] transformers==4.46.3
  [pip3] triton==3.1.0
  [conda] numpy                     1.26.4                   pypi_0    pypi
  [conda] numpydoc                  1.7.0           py312h06a4308_0  
  [conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
  [conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
  [conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
  [conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
  [conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
  [conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
  [conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
  [conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
  [conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
  [conda] nvidia-cusparselt-cu12    0.6.2                    pypi_0    pypi
  [conda] nvidia-ml-py              12.570.86                pypi_0    pypi
  [conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
  [conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
  [conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
  [conda] pyzmq                     26.2.1                   pypi_0    pypi
  [conda] sentence-transformers     3.3.1                    pypi_0    pypi
  [conda] torch                     2.5.1                    pypi_0    pypi
  [conda] torchvision               0.20.1                   pypi_0    pypi
  [conda] transformers              4.46.3                   pypi_0    pypi
  [conda] triton                    3.1.0                    pypi_0    pypi
  ROCM Version: Could not collect
  Neuron SDK Version: N/A
  vLLM Version: 0.7.0
  vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
  GPU Topology:
  GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
  GPU0	 X 	0-31	0		N/A
  
  Legend:
  
    X    = Self
    SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
    NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
    PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
    PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
    PIX  = Connection traversing at most a single PCIe bridge
    NV#  = Connection traversing a bonded set of # NVLinks
  
  LD_LIBRARY_PATH=/home/mukmckenzie/anaconda3/envs/llm/lib/python3.12/site-packages/cv2/../../lib64:/usr/lib/x86_64-linux-gnu:
  NCCL_CUMEM_ENABLE=0
  TORCHINDUCTOR_COMPILE_THREADS=1
  CUDA_MODULE_LOADING=LAZY
  
  ```
  
  </details>
  
  
  ### 🐛 Describe the bug
  
  vLLM ModelConfig in config.py doesn't pass hf_overrides to  get_hf_image_processor_config. 
  
  ```python
  if hf_overrides_kw:
      logger.info("Overriding HF config with %s", hf_overrides_kw)
      hf_config.update(hf_overrides_kw)
  if hf_overrides_fn:
      logger.info("Overriding HF config with %s", hf_overrides_fn)
      hf_config = hf_overrides_fn(hf_config)
  
  self.hf_config = hf_config
  
  self.hf_text_config = get_hf_text_config(self.hf_config)
  self.encoder_config = self._get_encoder_config()
  self.hf_image_processor_config = get_hf_image_processor_config(
      self.model, revision)
  
  ####################
  
  def get_hf_image_processor_config(
      model: Union[str, Path],
      revision: Optional[str] = None,
      **kwargs,
  ) -> Dict[str, Any]:
      # ModelScope does not provide an interface for image_processor
      if VLLM_USE_MODELSCOPE:
          return dict()
      # Separate model folder from file path for GGUF models
      if check_gguf_file(model):
          model = Path(model).parent
      return get_image_processor_config(model, revision=revision, **kwargs)
  
  ####################
  #transformers/models/auto/image_processing_auto.py
  def get_image_processor_config(
      pretrained_model_name_or_path: Union[str, os.PathLike],
      cache_dir: Optional[Union[str, os.PathLike]] = None,
      force_download: bool = False,
      resume_download: Optional[bool] = None,
      proxies: Optional[Dict[str, str]] = None,
      token: Optional[Union[bool, str]] = None,
      revision: Optional[str] = None,
      local_files_only: bool = False,
      **kwargs,
  ):
  use_auth_token = kwargs.pop("use_auth_token", None)
      if use_auth_token is not None:
          warnings.warn(
              "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
              FutureWarning,
          )
          if token is not None:
              raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
          token = use_auth_token
  ......
  ```
  
  This hf_overrides might contain the auth token from huggingface. From unsloth, the only way to pass the auth_token to vllm (not in env), is through hf_overrides. When pulling custom models from hugging face through vllm, we face this error:
  ```
  RuntimeError: void-mckenzie/krikri-sft_compound_instruct is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
  If this is a private repository, make sure to pass a token having permission to this repo either by logging in with `huggingface-cli login` or by passing `token=<your_token>`
  ```
  
  This limits the usage of multiple HF Tokens with vLLM. As you can see from the above snippet, get_image_processor_config in transformers accepts token. We just need to pass it when calling get_hf_image_processor_config from vllm/config.py
  
  ### Before submitting a new issue...
  
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