[Bug] 微调后的checkpoint 在lmdeploy 上加载错误，如何解决？

[jimmysue]

Checklist

• 1. I have searched related issues but cannot get the expected help.
• 2. The bug has not been fixed in the latest version.
• 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug

我使用 官方教程 对 internVL 2.5 8B 的模型进行微调， 使用 lmdeploy 进行serve 的时候出错，请问如何解决?, 出错的信息如下：

The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
You are using a model of type internvl_chat to instantiate a model of type . This is not supported for all configurations of models and can yield errors.
Traceback (most recent call last):
  File "/mnt/zjqd/ts/sjz/envs/py311/bin/lmdeploy", line 8, in <module>
    sys.exit(run())
             ^^^^^
  File "/mnt/zjqd/ts/sjz/envs/py311/lib/python3.11/site-packages/lmdeploy/cli/entrypoint.py", line 39, in run
    args.run(args)
  File "/mnt/zjqd/ts/sjz/envs/py311/lib/python3.11/site-packages/lmdeploy/cli/serve.py", line 315, in api_server
    backend = autoget_backend(args.model_path)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/zjqd/ts/sjz/envs/py311/lib/python3.11/site-packages/lmdeploy/archs.py", line 40, in autoget_backend
    turbomind_has = is_supported_turbomind(model_path)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/zjqd/ts/sjz/envs/py311/lib/python3.11/site-packages/lmdeploy/turbomind/supported_models.py", line 113, in is_supported
    llm_arch = cfg.llm_config.architectures[0]
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'dict' object has no attribute 'architectures'

Reproduction

lmdeploy serve api_server internvl2_5_8b_dynamic_res_2nd_finetune_full 

Environment

sys.platform: linux
Python: 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5,6,7: NVIDIA H100 80GB HBM3
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.4, V12.4.131
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.6.0+cu124
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.4
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 90.1
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=2236df1770800ffea5697b11b0bb0d910b2e59e1, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.6.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 

TorchVision: 0.21.0+cu124
LMDeploy: 0.9.0+
transformers: 4.52.4
gradio: Not Found
fastapi: 0.115.12
pydantic: 2.11.7
triton: 3.2.0
NVIDIA Topology: 
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    NIC8    CPU Affinity      NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    PXB     PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-31,64-950               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    PXB     PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-31,64-950               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     0-31,64-950               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     0-31,64-950               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     SYS     SYS     SYS     PXB     PXB     NODE    NODE    32-63,96-127      1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     SYS     SYS     SYS     PXB     PXB     NODE    NODE    32-63,96-127      1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     SYS     NODE    NODE    PXB     PXB     32-63,96-127      1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     SYS     NODE    NODE    PXB     PXB     32-63,96-127      1               N/A
NIC0    PXB     PXB     NODE    NODE    SYS     SYS     SYS     SYS      X      PXB     NODE    NODE    NODE    SYS     SYS     SYS     SYS
NIC1    PXB     PXB     NODE    NODE    SYS     SYS     SYS     SYS     PXB      X      NODE    NODE    NODE    SYS     SYS     SYS     SYS
NIC2    NODE    NODE    NODE    NODE    SYS     SYS     SYS     SYS     NODE    NODE     X      NODE    NODE    SYS     SYS     SYS     SYS
NIC3    NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      PXB     SYS     SYS     SYS     SYS
NIC4    NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     NODE    NODE    NODE    PXB      X      SYS     SYS     SYS     SYS
NIC5    SYS     SYS     SYS     SYS     PXB     PXB     NODE    NODE    SYS     SYS     SYS     SYS     SYS      X      PXB     NODE    NODE
NIC6    SYS     SYS     SYS     SYS     PXB     PXB     NODE    NODE    SYS     SYS     SYS     SYS     SYS     PXB      X      NODE    NODE
NIC7    SYS     SYS     SYS     SYS     NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     SYS     NODE    NODE     X      PXB
NIC8    SYS     SYS     SYS     SYS     NODE    NODE    PXB     PXB     SYS     SYS     SYS     SYS     SYS     NODE    NODE    PXB      X 

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

Error traceback