[Bug]: In Version V0.9.0, Qwen3-32B-AWQ Error when turn off thinking and use guided_json simultaneously.

[luoling1993]

Your current environment

The output of python collect_env.py

==============================
        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 3.22.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.7.0+cu126
Is debug build               : False
CUDA used to build PyTorch   : 12.6
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.10.12 (main, Feb  4 2025, 14:57:36) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.15.0-91-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.6.85
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version        : 535.161.08
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:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             14
On-line CPU(s) list:                0-13
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 7
Socket(s):                          1
Stepping:                           6
BogoMIPS:                           4589.21
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 nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid md_clear arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          336 KiB (7 instances)
L1i cache:                          224 KiB (7 instances)
L2 cache:                           8.8 MiB (7 instances)
L3 cache:                           54 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-13
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Not affected
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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==26.4.0
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchvision==0.22.0
[pip3] transformers==4.52.3
[pip3] triton==3.3.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
Neuron SDK Version           : N/A
vLLM Version                 : 0.9.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-13    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
==============================
CUDA_HOME=/usr/local/cuda-12.6
CUDA_HOME=/usr/local/cuda-12.6
LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64:
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

In version V0.9.0, when using qwen3-32B-awq, if both "thinking" is turned off and guided_json is used, the temperature can only be set to 0. If set to other values, the entire service will be blocked.

service command

python -m vllm.entrypoints.openai.api_server --port 18101 --served-model-name Qwen-Text --model /data/modelscope/Qwen3-32B-AWQ --tokenizer /data/modelscope/Qwen3-32B-AWQ --max-model-len 16384 --gpu-memory-utilization 0.9

client code

from openai import OpenAI
from pydantic import BaseModel


client = OpenAI(api_key="xxx", base_url="http://127.0.0.1:18101/v1")


class OutputModel(BaseModel):
    result: int


prompt = """\
123+456等于多少？
结果以JSON格式给出:
{{
    "result": "结果"
}}
"""

# or
rsp = client.chat.completions.create(
    model="Qwen-Text",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
    temperature=0,
)
print(rsp)

# service will be blocked
rsp = client.chat.completions.create(
    model="Qwen-Text",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
    temperature=0.7,
)
print(rsp)

If the temperature is set to any value other than 0, the service becomes unresponsive, and nvidia-smi consistently shows this state indefinitely!
Volatile GPU-Util = 100% but Persistence-MPwr = 79W
"""
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.08 Driver Version: 535.161.08 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-80GB On | 00000000:65:01.0 Off | 0 |
| N/A 29C P0 79W / 400W | 77581MiB / 81920MiB | 100% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 902358 C /home/lucas/envs/nlp-vllm/bin/python 77572MiB |
+---------------------------------------------------------------------------------------+
"""

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• 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.

[luoling1993]

I also conducted additional tests on the following scenarios:

# it's ok
rsp = client.chat.completions.create(
    model="Qwen-Text",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
    temperature=0.7,
)
print(rsp)

# will be blocked
rsp = client.chat.completions.create(
    model="Qwen-Text",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"guided_json": OutputModel.model_json_schema()},
    temperature=0.7,
)
print(rsp)

Therefore, the issue likely arises in version v0.9.0 when using the Qwen3-32B-AWQ model. When guided_json is enabled, the temperature parameter must be set to 0.

[chaunceyjiang]

I will try to reproduce this issue locally.

[luoling1993]

Qwen2.5-72B-Instruct-AWQ can reproduce this issue

[chaunceyjiang]

I couldn't reproduce that issue, but I encountered a different one.
When the thinking mode is turned off, the structured output fails to work correctly.

vllm serve /home/jovyan/qwen3-32b-awq  --tensor-parallel-size=2 --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser qwen3

from openai import OpenAI
from pydantic import BaseModel


client = OpenAI(api_key="xxx", base_url="http://127.0.0.1:8000/v1")


class OutputModel(BaseModel):
    result: int


prompt = """\
123+456等于多少？
结果以JSON格式给出:
{{
    "result": "结果"
}}
"""

# or
rsp = client.chat.completions.create(
    model="",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
    temperature=0,
)
print('---')
print(rsp.choices[0].message.content)
print('---')
# service will be blocked
rsp = client.chat.completions.create(
    model="",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
    temperature=0.7,
)
print('---')
print(rsp.choices[0].message.content)
print('---')

# or
rsp = client.chat.completions.create(
    model="",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": OutputModel.model_json_schema()},
    temperature=0,
)
print('---')
print(rsp.choices[0].message.content)
print('---')
# service will be blocked
rsp = client.chat.completions.create(
    model="",
    messages=[
        {"role": "user", "content": prompt},
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": OutputModel.model_json_schema()},
    temperature=0.7,
)
print('---')
print(rsp.choices[0].message.content)
print('---')
class Step(BaseModel):
    ground_truth_key_ideas: str 
    system_response_key_ideas: str
    discussion: str
    recall: float
    precision: float



# client.chat.completions.create
json_schema = Step.model_json_schema()

chat_response = client.beta.chat.completions.parse(
    model="",
    messages=[
        {'role': 'system',
        'content': 'Your input fields are:\n1. `question` (str)\n2. `ground_truth` (str)\n3. `system_response` (str)\n\nYour output fields are:\n1. `ground_truth_key_ideas` (str): enumeration of key ideas in the ground truth\n2. `system_response_key_ideas` (str): enumeration of key ideas in the system response\n3. `discussion` (str): discussion of the overlap between ground truth and system response\n4. `recall` (float): fraction (out of 1.0) of ground truth covered by the system response\n5. `precision` (float): fraction (out of 1.0) of system response covered by the ground truth\n\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\nInputs will have the following structure:\n\n[[ ## question ## ]]\n{question}\n\n[[ ## ground_truth ## ]]\n{ground_truth}\n\n[[ ## system_response ## ]]\n{system_response}\n\nOutputs will be a JSON object with the following fields.\n\n{\n  "ground_truth_key_ideas": "{ground_truth_key_ideas}",\n  "system_response_key_ideas": "{system_response_key_ideas}",\n  "discussion": "{discussion}",\n  "recall": "{recall}        # note: the value you produce must be a single float value",\n  "precision": "{precision}        # note: the value you produce must be a single float value"\n}\n\nIn adhering to this structure, your objective is: \n        Compare a system\'s response to the ground truth to compute recall and precision of key ideas.\n        You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.'},
        {'role': 'user',
        'content': '[[ ## question ## ]]\nWhy is the action space of language modeling particularly large? Is it because of the vocab size? But then, moving in the real world also has a huge action space (degrees of movement).\n\n[[ ## ground_truth ## ]]\nThe action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i.e. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes, or a linear combination thereof. The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.\n\n[[ ## system_response ## ]]\nThe action space in language modeling is indeed particularly large due to the **vocabulary size**—at each step, the model must choose from thousands of possible words (e.g., 50k+ tokens in large vocabularies), leading to combinatorial explosion in sequence generation. This discrete, high-dimensional choice is distinct from real-world continuous action spaces (e.g., motor control with infinite degrees of movement). However, the challenges differ: language models face **discrete, high-cardinality decisions** with combinatorial complexity, while real-world actions often involve **continuous control**. Techniques like actor-critic methods (e.g., Bahdanau et al. 2016) or action space reduction (e.g., GALAD) address the former by managing variance and exploration in discrete, large vocabularies, whereas real-world control typically uses gradient-based methods for continuous spaces.\n\nRespond with a JSON object in the following order of fields: `ground_truth_key_ideas`, then `system_response_key_ideas`, then `discussion`, then `recall` (must be formatted as a valid Python float), then `precision` (must be formatted as a valid Python float).'}
    ],
    temperature=0.0,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": json_schema},
)
print("-----")
print(chat_response.choices[0].message.content)
print("-----")

chat_response = client.beta.chat.completions.parse(
    model="",
    messages=[
        {'role': 'system',
        'content': 'Your input fields are:\n1. `question` (str)\n2. `ground_truth` (str)\n3. `system_response` (str)\n\nYour output fields are:\n1. `ground_truth_key_ideas` (str): enumeration of key ideas in the ground truth\n2. `system_response_key_ideas` (str): enumeration of key ideas in the system response\n3. `discussion` (str): discussion of the overlap between ground truth and system response\n4. `recall` (float): fraction (out of 1.0) of ground truth covered by the system response\n5. `precision` (float): fraction (out of 1.0) of system response covered by the ground truth\n\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\nInputs will have the following structure:\n\n[[ ## question ## ]]\n{question}\n\n[[ ## ground_truth ## ]]\n{ground_truth}\n\n[[ ## system_response ## ]]\n{system_response}\n\nOutputs will be a JSON object with the following fields.\n\n{\n  "ground_truth_key_ideas": "{ground_truth_key_ideas}",\n  "system_response_key_ideas": "{system_response_key_ideas}",\n  "discussion": "{discussion}",\n  "recall": "{recall}        # note: the value you produce must be a single float value",\n  "precision": "{precision}        # note: the value you produce must be a single float value"\n}\n\nIn adhering to this structure, your objective is: \n        Compare a system\'s response to the ground truth to compute recall and precision of key ideas.\n        You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.'},
        {'role': 'user',
        'content': '[[ ## question ## ]]\nWhy is the action space of language modeling particularly large? Is it because of the vocab size? But then, moving in the real world also has a huge action space (degrees of movement).\n\n[[ ## ground_truth ## ]]\nThe action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i.e. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes, or a linear combination thereof. The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.\n\n[[ ## system_response ## ]]\nThe action space in language modeling is indeed particularly large due to the **vocabulary size**—at each step, the model must choose from thousands of possible words (e.g., 50k+ tokens in large vocabularies), leading to combinatorial explosion in sequence generation. This discrete, high-dimensional choice is distinct from real-world continuous action spaces (e.g., motor control with infinite degrees of movement). However, the challenges differ: language models face **discrete, high-cardinality decisions** with combinatorial complexity, while real-world actions often involve **continuous control**. Techniques like actor-critic methods (e.g., Bahdanau et al. 2016) or action space reduction (e.g., GALAD) address the former by managing variance and exploration in discrete, large vocabularies, whereas real-world control typically uses gradient-based methods for continuous spaces.\n\nRespond with a JSON object in the following order of fields: `ground_truth_key_ideas`, then `system_response_key_ideas`, then `discussion`, then `recall` (must be formatted as a valid Python float), then `precision` (must be formatted as a valid Python float).'}
    ],
    temperature=0.0,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": json_schema},
)
print("-----")
print(chat_response.choices[0].message.content)
print("-----")

---
```{  
    "result": 579  
}
---
---
```{
    "result": 579
}
---
---
{"result": 579}
---
---
{"result": 579}
---
-----
{"ground_truth_key_ideas": "1. Action space equals vocabulary size. 2. Vocabulary is large (tens of thousands of tokens). 3. Real-world movement can be condensed to three axes (X, Y, Z). 4. Typical RL problems have smaller action spaces but no explanation of their construction.", "system_response_key_ideas": "1. Vocabulary size (50k+ tokens) causes large action space. 2. Discrete, high-cardinality decisions vs. continuous real-world actions. 3. Combinatorial explosion in sequence generation. 4. Techniques like actor-critic methods/GALAD for managing large vocabularies. 5. Real-world control uses gradient-based methods for continuous spaces.", "discussion": "The system response accurately captures the ground truth's key points about vocabulary size and real-world action space differences. It expands on the ground truth by introducing the distinction between discrete/continuous action spaces and adding technical solutions (actor-critic methods, GALAD). The ground truth does not mention these techniques or the gradient-based methods for real-world control. The system response's combinatorial explosion concept aligns with the ground truth's 'tens of thousands of tokens' point. However, the ground truth's fourth point about typical RL problems having smaller action spaces is not addressed in the system response.", "recall": 0.75, "precision": 0.6}
-----
-----
{
{
  "ground_truth_key_ideas": "1. The action space in language modeling is equal to the vocabulary size. 2. Language model vocabularies are very large (tens of thousands of tokens). 3. Real-world locomotion can be condensed to three axes (X, Y, Z) or their combinations. 4. Typical RL problems have action spaces that are an order of magnitude smaller than language modeling. 5. The ground truth does not explain how typical RL action spaces are modeled or constructed.",
  "system_response_key_ideas": "1. The action space in language modeling is large due to the vocabulary size (e.g., 50k+ tokens). 2. This leads to combinatorial explosion in sequence generation. 3. Language modeling involves discrete, high-dimensional decisions, while real-world actions are continuous. 4. The challenges in language modeling differ from real-world control (discrete vs. continuous). 5. Techniques like actor-critic methods or action space reduction are used to manage large vocabularies. 6. Real-world control typically uses gradient-based methods for continuous spaces.",
  "discussion": "The system response captures the key idea that the action space in language modeling is large due to the vocabulary size, which aligns with the ground truth. It also expands on the distinction between discrete (language) and continuous (real-world) action spaces, which is not explicitly mentioned in the ground truth. The system response introduces additional details about techniques used to manage large vocabularies and real-world control, which are not part of the ground truth. The ground truth mentions that typical RL problems have smaller action spaces but does not elaborate, while the system response does not address this point. Overall, the system response covers most of the ground truth ideas and adds some relevant context.",
  "recall": 0.8,
  "precision": 0.75
}
-----

[luoling1993]

I couldn't reproduce that issue, but I encountered a different one. When the thinking mode is turned off, the structured output fails to work correctly.

vllm serve /home/jovyan/qwen3-32b-awq --tensor-parallel-size=2 --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser qwen3
from openai import OpenAI
from pydantic import BaseModel

client = OpenAI(api_key="xxx", base_url="http://127.0.0.1:8000/v1")

class OutputModel(BaseModel):
result: int

prompt = """
123+456等于多少？
结果以JSON格式给出:
{{
"result": "结果"
}}
"""

or

rsp = client.chat.completions.create(
model="",
messages=[
{"role": "user", "content": prompt},
],
extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
temperature=0,
)
print('---')
print(rsp.choices[0].message.content)
print('---')

service will be blocked

rsp = client.chat.completions.create(
model="",
messages=[
{"role": "user", "content": prompt},
],
extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": OutputModel.model_json_schema()},
temperature=0.7,
)
print('---')
print(rsp.choices[0].message.content)
print('---')

or

rsp = client.chat.completions.create(
model="",
messages=[
{"role": "user", "content": prompt},
],
extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": OutputModel.model_json_schema()},
temperature=0,
)
print('---')
print(rsp.choices[0].message.content)
print('---')

service will be blocked

rsp = client.chat.completions.create(
model="",
messages=[
{"role": "user", "content": prompt},
],
extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": OutputModel.model_json_schema()},
temperature=0.7,
)
print('---')
print(rsp.choices[0].message.content)
print('---')
class Step(BaseModel):
ground_truth_key_ideas: str
system_response_key_ideas: str
discussion: str
recall: float
precision: float

client.chat.completions.create

json_schema = Step.model_json_schema()

chat_response = client.beta.chat.completions.parse(
model="",
messages=[
{'role': 'system',
'content': 'Your input fields are:\n1. question (str)\n2. ground_truth (str)\n3. system_response (str)\n\nYour output fields are:\n1. ground_truth_key_ideas (str): enumeration of key ideas in the ground truth\n2. system_response_key_ideas (str): enumeration of key ideas in the system response\n3. discussion (str): discussion of the overlap between ground truth and system response\n4. recall (float): fraction (out of 1.0) of ground truth covered by the system response\n5. precision (float): fraction (out of 1.0) of system response covered by the ground truth\n\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\nInputs will have the following structure:\n\n[[ ## question ## ]]\n{question}\n\n[[ ## ground_truth ## ]]\n{ground_truth}\n\n[[ ## system_response ## ]]\n{system_response}\n\nOutputs will be a JSON object with the following fields.\n\n{\n "ground_truth_key_ideas": "{ground_truth_key_ideas}",\n "system_response_key_ideas": "{system_response_key_ideas}",\n "discussion": "{discussion}",\n "recall": "{recall} # note: the value you produce must be a single float value",\n "precision": "{precision} # note: the value you produce must be a single float value"\n}\n\nIn adhering to this structure, your objective is: \n Compare a system's response to the ground truth to compute recall and precision of key ideas.\n You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.'},
{'role': 'user',
'content': '[[ ## question ## ]]\nWhy is the action space of language modeling particularly large? Is it because of the vocab size? But then, moving in the real world also has a huge action space (degrees of movement).\n\n[[ ## ground_truth ## ]]\nThe action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i.e. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes, or a linear combination thereof. The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.\n\n[[ ## system_response ## ]]\nThe action space in language modeling is indeed particularly large due to the vocabulary size—at each step, the model must choose from thousands of possible words (e.g., 50k+ tokens in large vocabularies), leading to combinatorial explosion in sequence generation. This discrete, high-dimensional choice is distinct from real-world continuous action spaces (e.g., motor control with infinite degrees of movement). However, the challenges differ: language models face discrete, high-cardinality decisions with combinatorial complexity, while real-world actions often involve continuous control. Techniques like actor-critic methods (e.g., Bahdanau et al. 2016) or action space reduction (e.g., GALAD) address the former by managing variance and exploration in discrete, large vocabularies, whereas real-world control typically uses gradient-based methods for continuous spaces.\n\nRespond with a JSON object in the following order of fields: ground_truth_key_ideas, then system_response_key_ideas, then discussion, then recall (must be formatted as a valid Python float), then precision (must be formatted as a valid Python float).'}
],
temperature=0.0,
extra_body={"chat_template_kwargs": {"enable_thinking": True}, "guided_json": json_schema},
)
print("-----")
print(chat_response.choices[0].message.content)
print("-----")

chat_response = client.beta.chat.completions.parse(
model="",
messages=[
{'role': 'system',
'content': 'Your input fields are:\n1. question (str)\n2. ground_truth (str)\n3. system_response (str)\n\nYour output fields are:\n1. ground_truth_key_ideas (str): enumeration of key ideas in the ground truth\n2. system_response_key_ideas (str): enumeration of key ideas in the system response\n3. discussion (str): discussion of the overlap between ground truth and system response\n4. recall (float): fraction (out of 1.0) of ground truth covered by the system response\n5. precision (float): fraction (out of 1.0) of system response covered by the ground truth\n\nAll interactions will be structured in the following way, with the appropriate values filled in.\n\nInputs will have the following structure:\n\n[[ ## question ## ]]\n{question}\n\n[[ ## ground_truth ## ]]\n{ground_truth}\n\n[[ ## system_response ## ]]\n{system_response}\n\nOutputs will be a JSON object with the following fields.\n\n{\n "ground_truth_key_ideas": "{ground_truth_key_ideas}",\n "system_response_key_ideas": "{system_response_key_ideas}",\n "discussion": "{discussion}",\n "recall": "{recall} # note: the value you produce must be a single float value",\n "precision": "{precision} # note: the value you produce must be a single float value"\n}\n\nIn adhering to this structure, your objective is: \n Compare a system's response to the ground truth to compute recall and precision of key ideas.\n You will first enumerate key ideas in each response, discuss their overlap, and then report recall and precision.'},
{'role': 'user',
'content': '[[ ## question ## ]]\nWhy is the action space of language modeling particularly large? Is it because of the vocab size? But then, moving in the real world also has a huge action space (degrees of movement).\n\n[[ ## ground_truth ## ]]\nThe action space for language modeling is equal to the vocabulary set of language models. Since the vocabularies are very large (i.e. tens of thousands of possible tokens), the action space is also very large. In general, locomotion in the real world can be condensed to three quantities - moving across X, Y or Z axes, or a linear combination thereof. The authors mention that typical RL problems have an action space that is an order of magnitude smaller, but do not specifically explain how the action spaces for typical problems is modeled or constructed.\n\n[[ ## system_response ## ]]\nThe action space in language modeling is indeed particularly large due to the vocabulary size—at each step, the model must choose from thousands of possible words (e.g., 50k+ tokens in large vocabularies), leading to combinatorial explosion in sequence generation. This discrete, high-dimensional choice is distinct from real-world continuous action spaces (e.g., motor control with infinite degrees of movement). However, the challenges differ: language models face discrete, high-cardinality decisions with combinatorial complexity, while real-world actions often involve continuous control. Techniques like actor-critic methods (e.g., Bahdanau et al. 2016) or action space reduction (e.g., GALAD) address the former by managing variance and exploration in discrete, large vocabularies, whereas real-world control typically uses gradient-based methods for continuous spaces.\n\nRespond with a JSON object in the following order of fields: ground_truth_key_ideas, then system_response_key_ideas, then discussion, then recall (must be formatted as a valid Python float), then precision (must be formatted as a valid Python float).'}
],
temperature=0.0,
extra_body={"chat_template_kwargs": {"enable_thinking": False}, "guided_json": json_schema},
)
print("-----")
print(chat_response.choices[0].message.content)
print("-----")

---
```{  
    "result": 579  
}
---
---
```{
    "result": 579
}
---
---
{"result": 579}
---
---
{"result": 579}
---
-----
{"ground_truth_key_ideas": "1. Action space equals vocabulary size. 2. Vocabulary is large (tens of thousands of tokens). 3. Real-world movement can be condensed to three axes (X, Y, Z). 4. Typical RL problems have smaller action spaces but no explanation of their construction.", "system_response_key_ideas": "1. Vocabulary size (50k+ tokens) causes large action space. 2. Discrete, high-cardinality decisions vs. continuous real-world actions. 3. Combinatorial explosion in sequence generation. 4. Techniques like actor-critic methods/GALAD for managing large vocabularies. 5. Real-world control uses gradient-based methods for continuous spaces.", "discussion": "The system response accurately captures the ground truth's key points about vocabulary size and real-world action space differences. It expands on the ground truth by introducing the distinction between discrete/continuous action spaces and adding technical solutions (actor-critic methods, GALAD). The ground truth does not mention these techniques or the gradient-based methods for real-world control. The system response's combinatorial explosion concept aligns with the ground truth's 'tens of thousands of tokens' point. However, the ground truth's fourth point about typical RL problems having smaller action spaces is not addressed in the system response.", "recall": 0.75, "precision": 0.6}
-----
-----
{
{
  "ground_truth_key_ideas": "1. The action space in language modeling is equal to the vocabulary size. 2. Language model vocabularies are very large (tens of thousands of tokens). 3. Real-world locomotion can be condensed to three axes (X, Y, Z) or their combinations. 4. Typical RL problems have action spaces that are an order of magnitude smaller than language modeling. 5. The ground truth does not explain how typical RL action spaces are modeled or constructed.",
  "system_response_key_ideas": "1. The action space in language modeling is large due to the vocabulary size (e.g., 50k+ tokens). 2. This leads to combinatorial explosion in sequence generation. 3. Language modeling involves discrete, high-dimensional decisions, while real-world actions are continuous. 4. The challenges in language modeling differ from real-world control (discrete vs. continuous). 5. Techniques like actor-critic methods or action space reduction are used to manage large vocabularies. 6. Real-world control typically uses gradient-based methods for continuous spaces.",
  "discussion": "The system response captures the key idea that the action space in language modeling is large due to the vocabulary size, which aligns with the ground truth. It also expands on the distinction between discrete (language) and continuous (real-world) action spaces, which is not explicitly mentioned in the ground truth. The system response introduces additional details about techniques used to manage large vocabularies and real-world control, which are not part of the ground truth. The ground truth mentions that typical RL problems have smaller action spaces but does not elaborate, while the system response does not address this point. Overall, the system response covers most of the ground truth ideas and adds some relevant context.",
  "recall": 0.8,
  "precision": 0.75
}
-----

Can you try in single device(not set --tensor-parallel-size), use the start command like:
vllm serve /data/modelscope/Qwen3-32B-AWQ --port 18101 --served-model-name Qwen-Text --max-model-len 16384 --gpu-memory-utilization 0.9

1. My device is A100 80G
2. cuda-toolkit version is 12.6