#22762[Bug]: Internal Server Error 'Encoding' object has no attribute 'items' When Using Mistral Tokenizer
Issue Details
mskoubaAuthor
Your current environment
The output of python collect_env.py
Collecting environment information... ============================== System Info ============================== OS : Ubuntu 22.04.5 LTS (x86_64) GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version : Could not collect CMake version : version 4.0.3 Libc version : glibc-2.35 ============================== PyTorch Info ============================== PyTorch version : 2.7.1+cu128 Is debug build : False CUDA used to build PyTorch : 12.8 ROCM used to build PyTorch : N/A ============================== Python Environment ============================== Python version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime) Python platform : Linux-6.1.141-165.249.amzn2023.x86_64-x86_64-with-glibc2.35 ============================== CUDA / GPU Info ============================== Is CUDA available : True CUDA runtime version : 12.8.93 CUDA_MODULE_LOADING set to : LAZY GPU models and configuration : GPU 0: NVIDIA L40S Nvidia driver version : 575.57.08 cuDNN version : Could not collect HIP runtime version : N/A MIOpen runtime version : N/A Is XNNPACK available : True ============================== CPU Info ============================== 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): 4 On-line CPU(s) list: 0-3 Vendor ID: AuthenticAMD Model name: AMD EPYC 7R13 Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 2 Socket(s): 1 Stepping: 1 BogoMIPS: 5299.99 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 nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid Hypervisor vendor: KVM Virtualization type: full L1d cache: 64 KiB (2 instances) L1i cache: 64 KiB (2 instances) L2 cache: 1 MiB (2 instances) L3 cache: 8 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-3 Vulnerability Gather data sampling: Not affected Vulnerability Indirect target selection: 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; Retpolines; IBPB disabled; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected ============================== Versions of relevant libraries ============================== [pip3] numpy==2.2.6 [pip3] nvidia-cublas-cu12==12.8.3.14 [pip3] nvidia-cuda-cupti-cu12==12.8.57 [pip3] nvidia-cuda-nvrtc-cu12==12.8.61 [pip3] nvidia-cuda-runtime-cu12==12.8.57 [pip3] nvidia-cudnn-cu12==9.7.1.26 [pip3] nvidia-cufft-cu12==11.3.3.41 [pip3] nvidia-cufile-cu12==1.13.0.11 [pip3] nvidia-curand-cu12==10.3.9.55 [pip3] nvidia-cusolver-cu12==11.7.2.55 [pip3] nvidia-cusparse-cu12==12.5.7.53 [pip3] nvidia-cusparselt-cu12==0.6.3 [pip3] nvidia-ml-py==12.575.51 [pip3] nvidia-nccl-cu12==2.26.2 [pip3] nvidia-nvjitlink-cu12==12.8.61 [pip3] nvidia-nvshmem-cu12==3.3.9 [pip3] nvidia-nvtx-cu12==12.8.55 [pip3] pynvml==12.0.0 [pip3] pyzmq==27.0.0 [pip3] torch==2.7.1+cu128 [pip3] torchaudio==2.7.1+cu128 [pip3] torchvision==0.22.1+cu128 [pip3] transformers==4.54.1 [pip3] triton==3.3.1 [conda] Could not collect ============================== vLLM Info ============================== ROCM Version : Could not collect Neuron SDK Version : N/A vLLM Version : 0.10.1.dev1+gbcc0a3cbe (git sha: bcc0a3cbe) vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X 0-3 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 ============================== Environment Variables ============================== NVIDIA_VISIBLE_DEVICES=all NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566 NCCL_VERSION=2.25.1-1 NVIDIA_DRIVER_CAPABILITIES=compute,utility NVIDIA_PRODUCT_NAME=CUDA VLLM_USAGE_SOURCE=production-docker-image CUDA_VERSION=12.8.1 LD_LIBRARY_PATH=/usr/local/cuda/lib64 NCCL_CUMEM_ENABLE=0 PYTORCH_NVML_BASED_CUDA_CHECK=1 TORCHINDUCTOR_COMPILE_THREADS=1 CUDA_MODULE_LOADING=LAZY
This can be reproduced with the vllm/vllm-openai:v0.10.0 image using the below docker compose (This was run on an L40s GPU (g6e.xlarge instance in AWS):
services: vllm: container_name: vllm restart: unless-stopped networks: - 'vllm' image: vllm/vllm-openai:v0.10.0 deploy: resources: reservations: devices: - driver: nvidia count: 1 capabilities: [gpu] ports: - 8000:8000 command: - "--model" - "mistralai/Mistral-Nemo-Instruct-2407" - "--max-model-len" - "30000" - "--tokenizer-mode" - "mistral" environment: HF_TOKEN: <HF Token Here (Will need to be granted access to this gated model)> networks: vllm:
🐛 Describe the bug
When sending multiple concurrent requests to a vLLM instance running a Mistral model such as mistralai/Mistral-Nemo-Instruct-2407 and specifying the mistral tokenizer, vLLM is returning a 500 Internal Server Error with the message "'Encoding' object has no attribute 'items'".
This error does not appear when running vLLM v0.9.1, but does occur when running v0.10.0. The error also only occurs when "--tokenizer mistral" is passed, omitting this input prevents the error from occurring.
Example script to reproduce error:
import json import os import time import threading import urllib.error import urllib.request BASE_URL = os.environ.get("BASE_URL", "localhost:8000") URL = f"http://{BASE_URL.rstrip('/')}/v1/completions" PROMPT = os.environ.get("PROMPT", "hello") CONCURRENCY = int(os.environ.get("CONCURRENCY", "5")) HEADERS = {"Content-Type": "application/json"} PAYLOAD = { "model": "mistralai/Mistral-Nemo-Instruct-2407", "prompt": "hello", } DATA = json.dumps(PAYLOAD).encode("utf-8") def send_once() -> None: request = urllib.request.Request(url=URL, data=DATA, headers=HEADERS, method="POST") try: with urllib.request.urlopen(request, timeout=30) as response: status = response.getcode() body_text = response.read().decode("utf-8", errors="replace") if status != 200: print(f"Non-200 response: {status}") print(dict(response.headers)) print(body_text) except urllib.error.HTTPError as e: body = e.read().decode("utf-8", errors="replace") print(f"HTTPError: status={e.code}, reason={e.reason}") print(body) except Exception as e: print(f"Error: {e}") def main() -> None: def worker() -> None: while True: send_once() time.sleep(0.2) threads: list[threading.Thread] = [] for _ in range(CONCURRENCY): t = threading.Thread(target=worker, daemon=True) t.start() threads.append(t) # Keep main thread alive while True: time.sleep(3600) if __name__ == "__main__": main()
Full Error Returned:
{"object":"error","message":"'Encoding' object has no attribute 'items'","type":"Internal Server Error","param":null,"code":500}
Debug logs: mistral-no-items-attr-debug.log
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