[Usage]: Serve From Hard disk and folder Path issue

[abbas381366]

Your current environment

INFO 03-25 08:55:22 [__init__.py:256] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Feb  4 2025, 14:57:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 566.03
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:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               32
On-line CPU(s) list:                  0-31
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Core(TM) i9-14900K
CPU family:                           6
Model:                                183
Thread(s) per core:                   2
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             1
BogoMIPS:                             6374.40
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize flush_l1d arch_capabilities
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            768 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             32 MiB (16 instances)
L3 cache:                             36 MiB (1 instance)
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:        Unknown: No mitigations
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed:               Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[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-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.3.0
[pip3] torch==2.6.0
[pip3] torchaudio==2.6.0
[pip3] torchvision==0.21.0
[pip3] transformers==4.50.0
[pip3] triton==3.2.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.8.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 				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

NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

How would you like to use vllm

I want to run inference of a mistralai/Mistral-Small-3.1-24B-Instruct-2503.
i downloaded the model in my disk and folder path in WSL2 is "/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503"....
when i run command
vllm serve --model "/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 1

get me error usage: vllm serve <model_tag> [options] vllm serve: error: the following arguments are required: model_tag

i test it for llama model and same error for it ?
how run model from my disk ?

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[DarkLight1337]

When using vllm serve, you should pass "/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503" as a positional argument rather than as an option.

[noooop]

@DarkLight1337

PTAL:
#14116

Use model_overwrite to redirect the model name to a local folder

[abbas381366]

@noooop
i change my command to vllm serve --model mistralai/Mistral-Small-3.1-24B-Instruct-2503 --model-overwrite "/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 1.
but get me error : vllm: error: unrecognized arguments: --model-overwrite ...
please rewrite my fixed command again . i am noob . thanks

[abbas381366]

@DarkLight1337
how ?
please rewrite my Fixed command . thanks

[noooop]

try remove --model

vllm serve /mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 1.

[abbas381366]

@noooop
with that command give me error : vllm: error: unrecognized arguments: Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503 .
i think this error fot space in path . so i change command to this : vllm serve '/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503' --tokenizer_mode mistral --config_format mistral --load_format mistral - -tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 1
but again Error : huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503'. Use repo_type argument if needed.

[noooop]

Wait a minute, please check if the folder path is correct.

ll "/mnt/f/AI/AI Models/mistralai__Mistral-Small-3.1-24B-Instruct-2503"

Please submit the complete error stack. I can't locate where the error is reported.

https://github.com/vllm-project/vllm/blob/cf5c8f1686d810883f27974fa4433f0f95c94cbe/vllm/transformers_utils/config.py#L257C27-L257C46

vllm/vllm/transformers_utils/config.py

Line 167 in cf5c8f1

# Offline mode support: Check if config file is cached already

Is try_to_load_from_cache reporting an error?

[abbas381366]

@noooop
thanks man ...
your link show me that for mistral model need param.json ...
but for some reason my downloaded change name param.json to param_2.json and Error for that ...
very thanks .. man