Model name id returned is weird specially when using Docker [Bug]

Checklist

[X] 1. I have searched related issues but cannot get the expected help.
[X] 2. The bug has not been fixed in the latest version.

Describe the bug

Hi! When using the cli command: lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5-AWQ --backend turbomind --model-format awq the model name returned by the API (v1/models) is: internvl-internlm2

Shouldn't it be: OpenGVLab/InternVL-Chat-V1-5-AWQ ???

But when I run the same service via docker with the following Docker command, the ID returned gets weirder:

dockerfile:

FROM openmmlab/lmdeploy:latest

RUN apt-get update && apt-get install -y python3 python3-pip git WORKDIR /app

RUN pip3 install --upgrade pip RUN pip3 install timm RUN pip3 install flash-attn --no-build-isolation

CMD ["lmdeploy", "serve", "api_server", "OpenGVLab/InternVL-Chat-V1-5-AWQ", "--backend", "turbomind", "--model-format", "awq"]

docker build --tag 'lmdeploy' .

docker run --privileged --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=" -p 23333:23333 --ipc=host lmdeploy lmdeploy serve api_server OpenGVLab/InternVL-Chat-V1-5-AWQ

then when I try the API, the ID returned is the following: /root/.cache/huggingface/hub/models--OpenGVLab--InternVL-Chat-V1-5-AWQ/snapshots/5ce4e49fe4e5d960b62a619b268113e40943c57f

As this Id is used in the web UI I'm using to show the model name, I'd rather have the normal short name, but while using Docker.

Reproduction

1.start lmdeploy using Docker recipe above 2.open browser to check fast api web page (http://{serverIP}:23333) 3.test the GET v1/models endpoint 4.Notice that the object.data[0].id is not the normal short name

Environment

sys.platform: linux
Python: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA RTX A6000
CUDA_HOME: /usr
NVCC: Cuda compilation tools, release 11.5, V11.5.119
GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.1.0+cu121
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 12.1
  - 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 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, 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 -DUSE_FBGEMM -DUSE_QNNPACK -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-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.16.0+cu121
LMDeploy: 0.4.2+
transformers: 4.36.2
gradio: 4.36.1
fastapi: 0.111.0
pydantic: 2.7.4
triton: 2.1.0

Error traceback

No response

InternLM / lmdeploy