Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 24.04.4 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version : Could not collect
CMake version : version 3.28.3
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0+cu130
Is debug build : False
CUDA used to build PyTorch : 13.0
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.13.6 (main, Aug 6 2025, 22:57:45) [Clang 20.1.4 ] (64-bit runtime)
Python platform : Linux-6.8.0-106-generic-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 13.2.51
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA RTX PRO 4000 Blackwell
GPU 1: NVIDIA RTX PRO 4000 Blackwell
GPU 2: NVIDIA RTX PRO 4000 Blackwell
Nvidia driver version : 595.45.04
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: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9274F 24-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
Frequency boost: enabled
CPU(s) scaling MHz: 57%
CPU max MHz: 4304.1870
CPU min MHz: 1500.0000
BogoMIPS: 8088.21
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 pcid sse4_1 sse4_2 x2apic 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 amd_ppin cppc amd_ibpb_ret 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 la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap ibpb_exit_to_user
Virtualization: AMD-V
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 24 MiB (24 instances)
L3 cache: 256 MiB (8 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-47
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; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsa: Mitigation; Clear CPU buffers
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Mitigation; IBPB before exit to userspace
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.7
[pip3] numpy==2.4.4
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.15.1.9
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.29.7
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu130
[pip3] torchvision==0.25.0+cu130
[pip3] transformers==5.5.0
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.19.1rc1.dev39+gf53fa26e0 (git sha: f53fa26e0)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PHB NODE 0-47 0 N/A
GPU1 PHB X NODE 0-47 0 N/A
GPU2 NODE NODE X 0-47 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
==============================
LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_drros
🐛 Describe the bug
Trying to use claude code with gemma 4 (31B) but for some reason this didn't work well - if thinking is enabled the reasoning tags are leaking to chat. If I turn off reasoning with --default-chat-template-kwargs '{"enable_thinking": false}' it starts to leak tool calls to chat. Here is some examples:
reasoning on (command to run: vllm serve /mnt/nfs-esxi/LLM/gemma-4-31B-it-NVFP4/ --tensor-parallel-size 2 --host 0.0.0.0 --port 30000 --max-model-len $((200*1024)) --gpu-memory-utilization 0.9 --max-num-seqs 4 --enable-auto-tool-choice --reasoning-parser gemma4 --tool-call-parser gemma4 --served-model-name qwen3.5-397b-ud-q4-k-xl:thinking-coding-vision --kv-cache-dtype fp8
NB: alias is just to quickly return to using qwen as main model via llama.cpp
╭─── Claude Code v2.1.92 ────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Tips for getting started │
│ Welcome back Роман! Run /init to create a CLAUDE.md file with instructions for Claude │
│ ───────────────────────────────────────────────────────────────── │
│ ▐▛███▜▌ Recent activity │
│ ▝▜█████▛▘ No recent activity │
│ ▘▘ ▝▝ │
│ qwen3.5-397b-ud-q4-k-xl:think… · API Usage Billing │
│ · Айдеко │
│ ~/ideco/repos/main/rest-api-backend │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
⚠Auth conflict: Using ANTHROPIC_API_KEY instead of Anthropic Console key. Either unset ANTHROPIC_API_KEY, or run `claude /logout`.
⚠Auth conflict: Both a token (ANTHROPIC_AUTH_TOKEN) and an API key (ANTHROPIC_API_KEY) are set. This may lead to unexpected behavior.
· Trying to use ANTHROPIC_AUTH_TOKEN? Unset the ANTHROPIC_API_KEY environment variable, or claude /logout then say "No" to the API key approval before login.
· Trying to use ANTHROPIC_API_KEY? Unset the ANTHROPIC_AUTH_TOKEN environment variable.
❯ explain how this backend works
● To explain how this backend works, I first need to explore the codebase to understand its architecture, entry points, and core logic.
I'll start by exploring the project structure and key files. Since this is a broad request, I'll use the Explore agent to map out the system.
<|channel>thought
<channel|>
● Explore(Explore the codebase to understand the backend architecture, framework, and core flow.)
⎿ Done (5 tool uses · 30.4k tokens · 40s)
(ctrl+o to expand)
● Since you didn't specify a further request, I will assume you are satisfied with the architectural explanation.
If you'd like to dive deeper, I can help you with:
- Implementation details: Exploring specific handlers or updaters (e.g., how ShaperRules are managed).
- Data Flow: Tracing a specific API call from the route definition down to the etcd write.
- Development: Helping you add a new endpoint or modify existing business logic.
Let me know how you'd like to proceed!
✻ Crunched for 1m 2s
reasoning off (command vllm serve /mnt/nfs-esxi/LLM/gemma-4-31B-it-NVFP4/ --tensor-parallel-size 2 --host 0.0.0.0 --port 30000 --max-model-len $((200*1024)) --gpu-memory-utilization 0.9 --max-num-seqs 4 --enable-auto-tool-choice --reasoning-parser gemma4 --tool-call-parser gemma4 --served-model-name qwen3.5-397b-ud-q4-k-xl:thinking-coding-vision --kv-cache-dtype fp8 --default-chat-template-kwargs '{"enable_thinking": false}'
╭─── Claude Code v2.1.92 ────────────────────────────────────────────────────────────────────────────────────────────────╮
│ │ Tips for getting started │
│ Welcome back Роман! │ Run /init to create a CLAUDE.md file with instructions for Claude │
│ │ ───────────────────────────────────────────────────────────────── │
│ ▐▛███▜▌ │ Recent activity │
│ ▝▜█████▛▘ │ No recent activity │
│ ▘▘ ▝▝ │ │
│ qwen3.5-397b-ud-q4-k-xl:think… · API Usage Billing │ │
│ · Айдеко │ │
│ ~/ideco/repos/main/rest-api-backend │ │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
⚠Auth conflict: Using ANTHROPIC_API_KEY instead of Anthropic Console key. Either unset ANTHROPIC_API_KEY, or run `claude /logout`.
⚠Auth conflict: Both a token (ANTHROPIC_AUTH_TOKEN) and an API key (ANTHROPIC_API_KEY) are set. This may lead to unexpected behavior.
· Trying to use ANTHROPIC_AUTH_TOKEN? Unset the ANTHROPIC_API_KEY environment variable, or claude /logout then say "No" to the API key approval before login.
· Trying to use ANTHROPIC_API_KEY? Unset the ANTHROPIC_AUTH_TOKEN environment variable.
❯ explain how this backend works
● I will start by exploring the codebase to understand the architecture, entry points, and primary logic of this backend.
Since this is a broad exploration task, I'll use the Explore agent to map out the system.
<tool_call:Agent {
"description": "Explore the codebase to understand the backend architecture, framework, and request flow.",
"prompt": "Analyze the current working directory (/home/drros/ideco/repos/main/rest-api-backend) to explain how this backend works. \n\n1. Identify the framework used (e.g., FastAPI, Flask, aiohttp).\n2. Locate the entry point of the application and how the server is started.\n3. Map the high-level directory structure and explain the purpose of key folders.\n4. Identify how routing is handled and where the API endpoints
are defined.\n5. Determine how the application manages state, database connections, or external service interactions.\n6. Trace a typical request from the entry point to a handler and back.\n\nProvide a concise summary of the architecture and the request lifecycle.",
"subagent_type": "Explore"
}>
❯ try again
● Explore(Explore the codebase to understand the backend architecture, framework, and request flow.)
⎿ Done (3 tool uses · 27.4k tokens · 33s)
(ctrl+o to expand)
● It seems you didn't provide a follow-up question. Based on the exploration of the rest-api-backend, I can now help you with:
- Implementing new endpoints: I can guide you on where to add new handlers and how to register them in main.py.
- Modifying business logic: I can help you navigate the domain-specific packages (like cf_rest, dpi_profiles_rest, etc.) to change how features behave.
- Adding state management: I can show you how to use the etcd readers or Updater classes to store and retrieve configuration.
- Debugging specific flows: If you have a bug in a specific API call, I can trace the request from the UrlHandlers mapping down to the specific handler logic.
What would you like to do next?
✻ Baked for 44s
logs on the backend seems okayish:
(APIServer pid=266789) INFO: Started server process [266789]
(APIServer pid=266789) INFO: Waiting for application startup.
(APIServer pid=266789) INFO: Application startup complete.
(APIServer pid=266789) INFO: 192.168.0.61:52504 - "HEAD / HTTP/1.1" 404 Not Found
(APIServer pid=266789) INFO: 192.168.0.61:52504 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO: 192.168.0.61:52510 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO 04-05 23:06:55 [loggers.py:259] Engine 000: Avg prompt throughput: 23.0 tokens/s, Avg generation throughput: 0.4 tokens/s, Running: 2 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.5%, Prefix cache hit rate: 0.0%
(APIServer pid=266789) INFO 04-05 23:07:05 [loggers.py:259] Engine 000: Avg prompt throughput: 3010.3 tokens/s, Avg generation throughput: 14.2 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.7%, Prefix cache hit rate: 0.0%
(APIServer pid=266789) INFO 04-05 23:07:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 21.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=266789) INFO 04-05 23:07:25 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO: 192.168.0.61:56728 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO 04-05 23:07:45 [loggers.py:259] Engine 000: Avg prompt throughput: 41.8 tokens/s, Avg generation throughput: 5.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.8%, Prefix cache hit rate: 49.6%
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO 04-05 23:07:55 [loggers.py:259] Engine 000: Avg prompt throughput: 1603.4 tokens/s, Avg generation throughput: 26.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 5.0%, Prefix cache hit rate: 56.6%
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages/count_tokens?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO 04-05 23:08:05 [loggers.py:259] Engine 000: Avg prompt throughput: 1040.5 tokens/s, Avg generation throughput: 28.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.0%, Prefix cache hit rate: 57.4%
(APIServer pid=266789) INFO 04-05 23:08:15 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 40.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.1%, Prefix cache hit rate: 57.4%
(APIServer pid=266789) INFO: 192.168.0.61:56726 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
(APIServer pid=266789) INFO 04-05 23:08:25 [loggers.py:259] Engine 000: Avg prompt throughput: 122.9 tokens/s, Avg generation throughput: 38.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 8.0%, Prefix cache hit rate: 64.8%
(APIServer pid=266789) INFO 04-05 23:08:35 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 11.1 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 64.8%
(APIServer pid=266789) INFO 04-05 23:08:45 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 64.8%
Before submitting a new issue...
Your current environment
The output of
python collect_env.py🐛 Describe the bug
Trying to use claude code with gemma 4 (31B) but for some reason this didn't work well - if thinking is enabled the reasoning tags are leaking to chat. If I turn off reasoning with
--default-chat-template-kwargs '{"enable_thinking": false}'it starts to leak tool calls to chat. Here is some examples:reasoning on (command to run:
vllm serve /mnt/nfs-esxi/LLM/gemma-4-31B-it-NVFP4/ --tensor-parallel-size 2 --host 0.0.0.0 --port 30000 --max-model-len $((200*1024)) --gpu-memory-utilization 0.9 --max-num-seqs 4 --enable-auto-tool-choice --reasoning-parser gemma4 --tool-call-parser gemma4 --served-model-name qwen3.5-397b-ud-q4-k-xl:thinking-coding-vision --kv-cache-dtype fp8NB: alias is just to quickly return to using qwen as main model via llama.cpp
reasoning off (command
vllm serve /mnt/nfs-esxi/LLM/gemma-4-31B-it-NVFP4/ --tensor-parallel-size 2 --host 0.0.0.0 --port 30000 --max-model-len $((200*1024)) --gpu-memory-utilization 0.9 --max-num-seqs 4 --enable-auto-tool-choice --reasoning-parser gemma4 --tool-call-parser gemma4 --served-model-name qwen3.5-397b-ud-q4-k-xl:thinking-coding-vision --kv-cache-dtype fp8 --default-chat-template-kwargs '{"enable_thinking": false}'logs on the backend seems okayish:
Before submitting a new issue...