推理模型的结构化输出

当使用推理模型时，这些模型可能使用特殊 token，例如 <think>...</think> 来表示推理部分。在这种情况下，您可能希望允许这些部分内包含自由格式的文本，同时仍然对输出的其余部分强制执行语法约束。

SGLang 提供了一个功能，可以在推理部分内部禁用语法限制。这对于需要在提供结构化输出之前执行复杂推理步骤的模型特别有用。

要启用此功能，在启动服务器时使用 --reasoning-parser 标志，它决定了思考结束 token，例如 </think>。您也可以使用 --reasoning-parser 标志指定推理解析器。

支持的模型

目前，SGLang 支持以下推理模型：

• DeepSeek R1 系列：推理内容包裹在 <think> 和 </think> 标签中。

• QwQ：推理内容包裹在 <think> 和 </think> 标签中。

用法

OpenAI 兼容 API

指定 --grammar-backend, --reasoning-parser 选项。

import openai
import os
from sglang.test.test_utils import is_in_ci

if is_in_ci():
    from patch import launch_server_cmd
else:
    from sglang.utils import launch_server_cmd

from sglang.utils import wait_for_server, print_highlight, terminate_process

os.environ["TOKENIZERS_PARALLELISM"] = "false"


server_process, port = launch_server_cmd(
    "python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --host 0.0.0.0 --reasoning-parser deepseek-r1"
)

wait_for_server(f"http://localhost:{port}")
client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

[2025-05-15 22:36:46] server_args=ServerArgs(model_path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', tokenizer_path='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, quantization_param_path=None, context_length=None, device='cuda', served_model_name='deepseek-ai/DeepSeek-R1-Distill-Qwen-7B', chat_template=None, completion_template=None, is_embedding=False, enable_multimodal=None, revision=None, host='0.0.0.0', port=30120, mem_fraction_static=0.88, max_running_requests=200, max_total_tokens=20480, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='fcfs', schedule_conservativeness=1.0, cpu_offload_gb=0, page_size=1, tp_size=1, pp_size=1, max_micro_batch_size=None, stream_interval=1, stream_output=False, random_seed=412130716, constrained_json_whitespace_pattern=None, watchdog_timeout=300, dist_timeout=None, download_dir=None, base_gpu_id=0, gpu_id_step=1, log_level='info', log_level_http=None, log_requests=False, log_requests_level=0, show_time_cost=False, enable_metrics=False, bucket_time_to_first_token=None, bucket_e2e_request_latency=None, bucket_inter_token_latency=None, collect_tokens_histogram=False, decode_log_interval=40, enable_request_time_stats_logging=False, api_key=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser='deepseek-r1', dp_size=1, load_balance_method='round_robin', ep_size=1, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, lora_paths=None, max_loras_per_batch=8, lora_backend='triton', attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', speculative_algorithm=None, speculative_draft_model_path=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, disable_radix_cache=False, disable_cuda_graph=True, disable_cuda_graph_padding=False, enable_nccl_nvls=False, enable_tokenizer_batch_encode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_ep_moe=False, enable_deepep_moe=False, deepep_mode='auto', enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=None, cuda_graph_bs=None, torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, allow_auto_truncate=False, enable_custom_logit_processor=False, tool_call_parser=None, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through_selective', flashinfer_mla_disable_ragged=False, warmups=None, moe_dense_tp_size=None, n_share_experts_fusion=0, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, mm_attention_backend=None, debug_tensor_dump_output_folder=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_bootstrap_port=8998, disaggregation_transfer_backend='mooncake', disaggregation_ib_device=None, pdlb_url=None)
[2025-05-15 22:36:53] Attention backend not set. Use fa3 backend by default.
[2025-05-15 22:36:53] Init torch distributed begin.
[2025-05-15 22:36:54] Init torch distributed ends. mem usage=0.00 GB
[2025-05-15 22:36:54] Load weight begin. avail mem=60.59 GB
[2025-05-15 22:36:56] Using model weights format ['*.safetensors']
Loading safetensors checkpoint shards:   0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  50% Completed | 1/2 [00:02<00:02,  2.90s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:07<00:00,  3.75s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:07<00:00,  3.62s/it]

[2025-05-15 22:37:04] Load weight end. type=Qwen2ForCausalLM, dtype=torch.bfloat16, avail mem=43.18 GB, mem usage=17.41 GB.
[2025-05-15 22:37:04] KV Cache is allocated. #tokens: 20480, K size: 0.55 GB, V size: 0.55 GB
[2025-05-15 22:37:04] Memory pool end. avail mem=41.81 GB
[2025-05-15 22:37:04] max_total_num_tokens=20480, chunked_prefill_size=8192, max_prefill_tokens=16384, max_running_requests=200, context_len=131072
[2025-05-15 22:37:05] INFO:     Started server process [70984]
[2025-05-15 22:37:05] INFO:     Waiting for application startup.
[2025-05-15 22:37:05] INFO:     Application startup complete.
[2025-05-15 22:37:05] INFO:     Uvicorn running on http://0.0.0.0:30120 (Press CTRL+C to quit)
[2025-05-15 22:37:06] INFO:     127.0.0.1:40760 - "GET /v1/models HTTP/1.1" 200 OK
[2025-05-15 22:37:06] INFO:     127.0.0.1:40766 - "GET /get_model_info HTTP/1.1" 200 OK
[2025-05-15 22:37:06] Prefill batch. #new-seq: 1, #new-token: 7, #cached-token: 0, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:07] INFO:     127.0.0.1:40778 - "POST /generate HTTP/1.1" 200 OK
[2025-05-15 22:37:07] The server is fired up and ready to roll!

注意：通常，服务器在单独的终端中运行。
在这个 notebook 中，我们同时运行服务器和 notebook 代码，因此它们的输出是混合的。
为了提高清晰度，服务器日志以原始黑色显示，而 notebook 输出则用蓝色突出显示。
我们正在 CI 并行环境中运行这些 notebook，因此吞吐量不代表实际性能。

JSON

您可以直接定义 JSON Schema，或使用 Pydantic 来定义和验证响应。

使用 Pydantic

from pydantic import BaseModel, Field


# Define the schema using Pydantic
class CapitalInfo(BaseModel):
    name: str = Field(..., pattern=r"^\w+$", description="Name of the capital city")
    population: int = Field(..., description="Population of the capital city")


response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    messages=[
        {
            "role": "assistant",
            "content": "Give me the information and population of the capital of France in the JSON format.",
        },
    ],
    temperature=0,
    max_tokens=2048,
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "foo",
            # convert the pydantic model to json schema
            "schema": CapitalInfo.model_json_schema(),
        },
    },
)

print_highlight(
    f"reasoing_content: {response.choices[0].message.reasoning_content}\n\ncontent: {response.choices[0].message.content}"
)

[2025-05-15 22:37:11] Prefill batch. #new-seq: 1, #new-token: 21, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:12] Decode batch. #running-req: 1, #token: 55, token usage: 0.00, cuda graph: False, gen throughput (token/s): 4.93, #queue-req: 0
[2025-05-15 22:37:13] Decode batch. #running-req: 1, #token: 95, token usage: 0.00, cuda graph: False, gen throughput (token/s): 66.06, #queue-req: 0
[2025-05-15 22:37:14] Decode batch. #running-req: 1, #token: 135, token usage: 0.01, cuda graph: False, gen throughput (token/s): 58.29, #queue-req: 0
[2025-05-15 22:37:14] Decode batch. #running-req: 1, #token: 175, token usage: 0.01, cuda graph: False, gen throughput (token/s): 62.42, #queue-req: 0
[2025-05-15 22:37:15] Decode batch. #running-req: 1, #token: 215, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.42, #queue-req: 0
[2025-05-15 22:37:16] Decode batch. #running-req: 1, #token: 255, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.55, #queue-req: 0
[2025-05-15 22:37:16] Decode batch. #running-req: 1, #token: 295, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.27, #queue-req: 0
[2025-05-15 22:37:17] Decode batch. #running-req: 1, #token: 335, token usage: 0.02, cuda graph: False, gen throughput (token/s): 65.49, #queue-req: 0
[2025-05-15 22:37:17] Decode batch. #running-req: 1, #token: 375, token usage: 0.02, cuda graph: False, gen throughput (token/s): 65.43, #queue-req: 0
[2025-05-15 22:37:18] Decode batch. #running-req: 1, #token: 415, token usage: 0.02, cuda graph: False, gen throughput (token/s): 62.24, #queue-req: 0
[2025-05-15 22:37:19] Decode batch. #running-req: 1, #token: 455, token usage: 0.02, cuda graph: False, gen throughput (token/s): 65.42, #queue-req: 0
[2025-05-15 22:37:19] INFO:     127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK

推理内容：好的，用户要求提供法国首都的信息和人口，格式为 JSON。我立刻想到首都是哪里——巴黎。然后，我考虑人口。我知道这是个大城市，但不确定确切数字。我记得超过300万，但不确定是350万还是360万。我应该仔细核对一下。

等等，也许我应该考虑一下最新数据。我记得巴黎一直在增长，所以可能在360万左右。但我不是100%确定。我应该查一下最新统计数据来确认。好的，查了一个可靠来源，它说人口大约是3,600,000。这看起来是对的。

现在，用户想要 JSON 格式。我需要正确地构建它。键应该是 "capital"，值为 "Paris"，另一个键是 "population"。我会确保人口数字在 JSON 中正确格式化为整数。

我还需要清晰地呈现出来。也许我应该在响应中写出来，显示 JSON 结构，以便用户能清楚地看到他们将得到什么。这样，他们就可以轻松复制或在他们的应用程序中使用它。

我想知道用户是不是正在开发需要人口数据的开发者。他们可能需要 JSON 用于集成或分析。所以提供他们要求的确切格式很重要。我也应该确保响应简洁明了，没有任何多余的内容。

还有什么需要考虑的吗？也许是人口统计日期，但用户没有指定。我会采用最新可用数据。另外，我应该确保 JSON 是有效的，因此要使用正确的语法，包括引号和逗号。

总而言之，我将提供包含正确首都名称和人口数字的 JSON，确保其准确性并按照用户请求的格式进行。这应该能有效地满足他们的需求。


内容：{"name": "Paris", "population": 3600000}

直接使用 JSON Schema

import json

json_schema = json.dumps(
    {
        "type": "object",
        "properties": {
            "name": {"type": "string", "pattern": "^[\\w]+$"},
            "population": {"type": "integer"},
        },
        "required": ["name", "population"],
    }
)

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    messages=[
        {
            "role": "assistant",
            "content": "Give me the information and population of the capital of France in the JSON format.",
        },
    ],
    temperature=0,
    max_tokens=2048,
    response_format={
        "type": "json_schema",
        "json_schema": {"name": "foo", "schema": json.loads(json_schema)},
    },
)

print_highlight(
    f"reasoing_content: {response.choices[0].message.reasoning_content}\n\ncontent: {response.choices[0].message.content}"
)

[2025-05-15 22:37:19] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 21, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:19] Decode batch. #running-req: 1, #token: 52, token usage: 0.00, cuda graph: False, gen throughput (token/s): 58.96, #queue-req: 0
[2025-05-15 22:37:20] Decode batch. #running-req: 1, #token: 92, token usage: 0.00, cuda graph: False, gen throughput (token/s): 66.22, #queue-req: 0
[2025-05-15 22:37:21] Decode batch. #running-req: 1, #token: 132, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.29, #queue-req: 0
[2025-05-15 22:37:21] Decode batch. #running-req: 1, #token: 172, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.62, #queue-req: 0
[2025-05-15 22:37:22] Decode batch. #running-req: 1, #token: 212, token usage: 0.01, cuda graph: False, gen throughput (token/s): 61.01, #queue-req: 0
[2025-05-15 22:37:22] Decode batch. #running-req: 1, #token: 252, token usage: 0.01, cuda graph: False, gen throughput (token/s): 62.59, #queue-req: 0
[2025-05-15 22:37:23] Decode batch. #running-req: 1, #token: 292, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.38, #queue-req: 0
[2025-05-15 22:37:24] Decode batch. #running-req: 1, #token: 332, token usage: 0.02, cuda graph: False, gen throughput (token/s): 66.19, #queue-req: 0
[2025-05-15 22:37:24] INFO:     127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK

推理内容：好的，用户要求提供法国首都的信息和人口，格式为 JSON。我立刻想到首都是哪里。毫无疑问，巴黎是首都。现在，我需要回忆或查找人口。我记得这是个大城市，所以人口是以百万计的。我猜大约200万，但我不是100%确定。也许我应该仔细核对一下。

等等，我应该考虑确切数字。我相信截至2023年，人口大约是2,175,000。这看起来是对的，但我不太确定。我应该确保准确呈现。另外，用户想要 JSON 格式，所以我需要正确地构建它，键是 "capital"，值是字符串。

我想知道用户是正在做项目的学生，还是需要应用程序数据的开发者。无论哪种情况，提供正确和最新的信息至关重要。也许他们还需要这个用于演示或报告，所以准确性是关键。

我还应该考虑他们是否还需要其他细节，比如面积或时区，但用户明确要求了人口。所以我就只关注这一点。我会正确格式化 JSON，确保语法正确，避免任何错误。

总而言之，我将提供包含法国首都巴黎正确人口数字的 JSON，确保其准确并按照请求格式。


内容：{"name": "Paris", "population": 2175000}

EBNF

ebnf_grammar = """
root ::= city | description
city ::= "London" | "Paris" | "Berlin" | "Rome"
description ::= city " is " status
status ::= "the capital of " country
country ::= "England" | "France" | "Germany" | "Italy"
"""

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    messages=[
        {"role": "system", "content": "You are a helpful geography bot."},
        {
            "role": "assistant",
            "content": "Give me the information and population of the capital of France in the JSON format.",
        },
    ],
    temperature=0,
    max_tokens=2048,
    extra_body={"ebnf": ebnf_grammar},
)

print_highlight(
    f"reasoing_content: {response.choices[0].message.reasoning_content}\n\ncontent: {response.choices[0].message.content}"
)

[2025-05-15 22:37:24] Prefill batch. #new-seq: 1, #new-token: 28, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:24] Decode batch. #running-req: 1, #token: 42, token usage: 0.00, cuda graph: False, gen throughput (token/s): 58.82, #queue-req: 0
[2025-05-15 22:37:25] Decode batch. #running-req: 1, #token: 82, token usage: 0.00, cuda graph: False, gen throughput (token/s): 66.13, #queue-req: 0
[2025-05-15 22:37:26] Decode batch. #running-req: 1, #token: 122, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.87, #queue-req: 0
[2025-05-15 22:37:26] Decode batch. #running-req: 1, #token: 162, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.56, #queue-req: 0
[2025-05-15 22:37:27] Decode batch. #running-req: 1, #token: 202, token usage: 0.01, cuda graph: False, gen throughput (token/s): 62.18, #queue-req: 0
[2025-05-15 22:37:28] Decode batch. #running-req: 1, #token: 242, token usage: 0.01, cuda graph: False, gen throughput (token/s): 60.06, #queue-req: 0
[2025-05-15 22:37:28] Decode batch. #running-req: 1, #token: 282, token usage: 0.01, cuda graph: False, gen throughput (token/s): 59.30, #queue-req: 0
[2025-05-15 22:37:29] Decode batch. #running-req: 1, #token: 322, token usage: 0.02, cuda graph: False, gen throughput (token/s): 65.64, #queue-req: 0
[2025-05-15 22:37:29] Decode batch. #running-req: 1, #token: 362, token usage: 0.02, cuda graph: False, gen throughput (token/s): 66.86, #queue-req: 0
[2025-05-15 22:37:30] Decode batch. #running-req: 1, #token: 402, token usage: 0.02, cuda graph: False, gen throughput (token/s): 64.34, #queue-req: 0
[2025-05-15 22:37:30] INFO:     127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK

推理内容：好的，用户要求提供法国首都的信息和人口，格式为 JSON。我回复了巴黎，其人口约为210万，并包含了一些关键事实。现在，用户又提出了另一个关于美国人口的查询。他们再次想要 JSON 格式的数据，但这次是针对整个国家。

嗯，我需要确保提供准确和最新的信息。美国人口是个很大的数字，所以我应该查一下最新的估计。我记得超过3亿，截至2023年可能在3.32亿左右。我应该把这个数字包括进去。另外，添加一些关于美国人口的关键事实会很有帮助，比如各州的多样性、增长最快的州和增长最慢的州。这提供了一些背景信息，使信息更有用。

我应该正确地构建 JSON，确保键清晰，数据易于阅读。也许可以包括国家名称、人口和关键事实作为数组。我需要确保 JSON 语法正确，逗号和括号使用得当。另外，按照说明，我应该避免任何 markdown 格式，保持纯文本。

等等，我应该提及增长率吗？用户没有要求，但这与此相关。也许只坚持他们要求的内容，除非他们指定更多细节。我将重点关注人口和他们提供的关键事实。

仔细核对人口数字至关重要。我认为根据最新数据，是3.328亿，所以我将使用这个数字。对于关键事实，我将列出人口最多和最少的州。这应该能满足用户的需求，并以他们要求的 JSON 格式提供全面的答案。


内容：Rome is the capital of Italy

正则表达式

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    messages=[
        {"role": "assistant", "content": "What is the capital of France?"},
    ],
    temperature=0,
    max_tokens=2048,
    extra_body={"regex": "(Paris|London)"},
)

print_highlight(
    f"reasoing_content: {response.choices[0].message.reasoning_content}\n\ncontent: {response.choices[0].message.content}"
)

[2025-05-15 22:37:30] Prefill batch. #new-seq: 1, #new-token: 11, #cached-token: 2, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:31] Decode batch. #running-req: 1, #token: 41, token usage: 0.00, cuda graph: False, gen throughput (token/s): 61.07, #queue-req: 0
[2025-05-15 22:37:31] Decode batch. #running-req: 1, #token: 81, token usage: 0.00, cuda graph: False, gen throughput (token/s): 66.64, #queue-req: 0
[2025-05-15 22:37:32] INFO:     127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK

推理内容：好的，用户刚刚问了“法国首都是什么？”嗯，这是个很直接的问题。我应该确保提供清晰准确的答案。让我想想，巴黎绝对是首都。但是等等，我有没有可能与其他国家搞混了？不，我很确定法国首都是巴黎。也许我应该仔细检查一下以防万一。是的，巴黎是正确的。我就用这个答案。


内容：London

结构化标签

tool_get_current_weather = {
    "type": "function",
    "function": {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "The city to find the weather for, e.g. 'San Francisco'",
                },
                "state": {
                    "type": "string",
                    "description": "the two-letter abbreviation for the state that the city is"
                    " in, e.g. 'CA' which would mean 'California'",
                },
                "unit": {
                    "type": "string",
                    "description": "The unit to fetch the temperature in",
                    "enum": ["celsius", "fahrenheit"],
                },
            },
            "required": ["city", "state", "unit"],
        },
    },
}

tool_get_current_date = {
    "type": "function",
    "function": {
        "name": "get_current_date",
        "description": "Get the current date and time for a given timezone",
        "parameters": {
            "type": "object",
            "properties": {
                "timezone": {
                    "type": "string",
                    "description": "The timezone to fetch the current date and time for, e.g. 'America/New_York'",
                }
            },
            "required": ["timezone"],
        },
    },
}

schema_get_current_weather = tool_get_current_weather["function"]["parameters"]
schema_get_current_date = tool_get_current_date["function"]["parameters"]


def get_messages():
    return [
        {
            "role": "system",
            "content": f"""
# Tool Instructions
- Always execute python code in messages that you share.
- When looking for real time information use relevant functions if available else fallback to brave_search
You have access to the following functions:
Use the function 'get_current_weather' to: Get the current weather in a given location
{tool_get_current_weather["function"]}
Use the function 'get_current_date' to: Get the current date and time for a given timezone
{tool_get_current_date["function"]}
If a you choose to call a function ONLY reply in the following format:
<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}
where
start_tag => `<function`
parameters => a JSON dict with the function argument name as key and function argument value as value.
end_tag => `</function>`
Here is an example,
<function=example_function_name>{{"example_name": "example_value"}}</function>
Reminder:
- Function calls MUST follow the specified format
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
- Always add your sources when using search results to answer the user query
You are a helpful assistant.""",
        },
        {
            "role": "assistant",
            "content": "You are in New York. Please get the current date and time, and the weather.",
        },
    ]


messages = get_messages()

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    messages=messages,
    response_format={
        "type": "structural_tag",
        "max_new_tokens": 2048,
        "structures": [
            {
                "begin": "<function=get_current_weather>",
                "schema": schema_get_current_weather,
                "end": "</function>",
            },
            {
                "begin": "<function=get_current_date>",
                "schema": schema_get_current_date,
                "end": "</function>",
            },
        ],
        "triggers": ["<function="],
    },
)

print_highlight(
    f"reasoing_content: {response.choices[0].message.reasoning_content}\n\ncontent: {response.choices[0].message.content}"
)

[2025-05-15 22:37:32] Prefill batch. #new-seq: 1, #new-token: 472, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:37:32] Decode batch. #running-req: 1, #token: 480, token usage: 0.02, cuda graph: False, gen throughput (token/s): 39.63, #queue-req: 0
[2025-05-15 22:37:33] Decode batch. #running-req: 1, #token: 520, token usage: 0.03, cuda graph: False, gen throughput (token/s): 61.92, #queue-req: 0
[2025-05-15 22:37:34] Decode batch. #running-req: 1, #token: 560, token usage: 0.03, cuda graph: False, gen throughput (token/s): 61.83, #queue-req: 0
[2025-05-15 22:37:34] Decode batch. #running-req: 1, #token: 600, token usage: 0.03, cuda graph: False, gen throughput (token/s): 64.14, #queue-req: 0
[2025-05-15 22:37:35] Decode batch. #running-req: 1, #token: 640, token usage: 0.03, cuda graph: False, gen throughput (token/s): 62.85, #queue-req: 0
[2025-05-15 22:37:35] Decode batch. #running-req: 1, #token: 680, token usage: 0.03, cuda graph: False, gen throughput (token/s): 62.35, #queue-req: 0
[2025-05-15 22:37:36] Decode batch. #running-req: 1, #token: 720, token usage: 0.04, cuda graph: False, gen throughput (token/s): 64.95, #queue-req: 0
[2025-05-15 22:37:37] Decode batch. #running-req: 1, #token: 760, token usage: 0.04, cuda graph: False, gen throughput (token/s): 65.10, #queue-req: 0
[2025-05-15 22:37:37] Decode batch. #running-req: 1, #token: 800, token usage: 0.04, cuda graph: False, gen throughput (token/s): 65.26, #queue-req: 0
[2025-05-15 22:37:38] Decode batch. #running-req: 1, #token: 840, token usage: 0.04, cuda graph: False, gen throughput (token/s): 65.15, #queue-req: 0
[2025-05-15 22:37:39] Decode batch. #running-req: 1, #token: 880, token usage: 0.04, cuda graph: False, gen throughput (token/s): 62.78, #queue-req: 0
[2025-05-15 22:37:39] Decode batch. #running-req: 1, #token: 920, token usage: 0.04, cuda graph: False, gen throughput (token/s): 60.87, #queue-req: 0
[2025-05-15 22:37:40] Decode batch. #running-req: 1, #token: 960, token usage: 0.05, cuda graph: False, gen throughput (token/s): 60.92, #queue-req: 0
[2025-05-15 22:37:41] Decode batch. #running-req: 1, #token: 1000, token usage: 0.05, cuda graph: False, gen throughput (token/s): 60.97, #queue-req: 0
[2025-05-15 22:37:41] Decode batch. #running-req: 1, #token: 1040, token usage: 0.05, cuda graph: False, gen throughput (token/s): 57.51, #queue-req: 0
[2025-05-15 22:37:42] Decode batch. #running-req: 1, #token: 1080, token usage: 0.05, cuda graph: False, gen throughput (token/s): 62.59, #queue-req: 0
[2025-05-15 22:37:43] Decode batch. #running-req: 1, #token: 1120, token usage: 0.05, cuda graph: False, gen throughput (token/s): 62.59, #queue-req: 0
[2025-05-15 22:37:43] Decode batch. #running-req: 1, #token: 1160, token usage: 0.06, cuda graph: False, gen throughput (token/s): 62.56, #queue-req: 0
[2025-05-15 22:37:44] Decode batch. #running-req: 1, #token: 1200, token usage: 0.06, cuda graph: False, gen throughput (token/s): 61.68, #queue-req: 0
[2025-05-15 22:37:44] Decode batch. #running-req: 1, #token: 1240, token usage: 0.06, cuda graph: False, gen throughput (token/s): 62.09, #queue-req: 0
[2025-05-15 22:37:45] Decode batch. #running-req: 1, #token: 1280, token usage: 0.06, cuda graph: False, gen throughput (token/s): 62.48, #queue-req: 0
[2025-05-15 22:37:46] Decode batch. #running-req: 1, #token: 1320, token usage: 0.06, cuda graph: False, gen throughput (token/s): 61.07, #queue-req: 0
[2025-05-15 22:37:46] Decode batch. #running-req: 1, #token: 1360, token usage: 0.07, cuda graph: False, gen throughput (token/s): 62.14, #queue-req: 0
[2025-05-15 22:37:47] Decode batch. #running-req: 1, #token: 1400, token usage: 0.07, cuda graph: False, gen throughput (token/s): 62.25, #queue-req: 0
[2025-05-15 22:37:48] Decode batch. #running-req: 1, #token: 1440, token usage: 0.07, cuda graph: False, gen throughput (token/s): 60.22, #queue-req: 0
[2025-05-15 22:37:48] Decode batch. #running-req: 1, #token: 1480, token usage: 0.07, cuda graph: False, gen throughput (token/s): 58.55, #queue-req: 0
[2025-05-15 22:37:49] Decode batch. #running-req: 1, #token: 1520, token usage: 0.07, cuda graph: False, gen throughput (token/s): 56.90, #queue-req: 0
[2025-05-15 22:37:50] Decode batch. #running-req: 1, #token: 1560, token usage: 0.08, cuda graph: False, gen throughput (token/s): 61.89, #queue-req: 0
[2025-05-15 22:37:50] Decode batch. #running-req: 1, #token: 1600, token usage: 0.08, cuda graph: False, gen throughput (token/s): 61.97, #queue-req: 0
[2025-05-15 22:37:51] Decode batch. #running-req: 1, #token: 1640, token usage: 0.08, cuda graph: False, gen throughput (token/s): 61.94, #queue-req: 0
[2025-05-15 22:37:52] Decode batch. #running-req: 1, #token: 1680, token usage: 0.08, cuda graph: False, gen throughput (token/s): 63.69, #queue-req: 0
[2025-05-15 22:37:52] Decode batch. #running-req: 1, #token: 1720, token usage: 0.08, cuda graph: False, gen throughput (token/s): 63.81, #queue-req: 0
[2025-05-15 22:37:53] Decode batch. #running-req: 1, #token: 1760, token usage: 0.09, cuda graph: False, gen throughput (token/s): 63.75, #queue-req: 0
[2025-05-15 22:37:54] Decode batch. #running-req: 1, #token: 1800, token usage: 0.09, cuda graph: False, gen throughput (token/s): 63.62, #queue-req: 0
[2025-05-15 22:37:54] Decode batch. #running-req: 1, #token: 1840, token usage: 0.09, cuda graph: False, gen throughput (token/s): 63.29, #queue-req: 0
[2025-05-15 22:37:55] Decode batch. #running-req: 1, #token: 1880, token usage: 0.09, cuda graph: False, gen throughput (token/s): 62.90, #queue-req: 0
[2025-05-15 22:37:55] Decode batch. #running-req: 1, #token: 1920, token usage: 0.09, cuda graph: False, gen throughput (token/s): 62.71, #queue-req: 0
[2025-05-15 22:37:56] Decode batch. #running-req: 1, #token: 1960, token usage: 0.10, cuda graph: False, gen throughput (token/s): 63.76, #queue-req: 0
[2025-05-15 22:37:57] Decode batch. #running-req: 1, #token: 2000, token usage: 0.10, cuda graph: False, gen throughput (token/s): 63.46, #queue-req: 0
[2025-05-15 22:37:57] Decode batch. #running-req: 1, #token: 2040, token usage: 0.10, cuda graph: False, gen throughput (token/s): 63.34, #queue-req: 0
[2025-05-15 22:37:58] Decode batch. #running-req: 1, #token: 2080, token usage: 0.10, cuda graph: False, gen throughput (token/s): 63.67, #queue-req: 0
[2025-05-15 22:37:59] Decode batch. #running-req: 1, #token: 2120, token usage: 0.10, cuda graph: False, gen throughput (token/s): 63.34, #queue-req: 0
[2025-05-15 22:37:59] Decode batch. #running-req: 1, #token: 2160, token usage: 0.11, cuda graph: False, gen throughput (token/s): 63.25, #queue-req: 0
[2025-05-15 22:38:00] Decode batch. #running-req: 1, #token: 2200, token usage: 0.11, cuda graph: False, gen throughput (token/s): 63.50, #queue-req: 0
[2025-05-15 22:38:00] Decode batch. #running-req: 1, #token: 2240, token usage: 0.11, cuda graph: False, gen throughput (token/s): 63.08, #queue-req: 0
[2025-05-15 22:38:01] Decode batch. #running-req: 1, #token: 2280, token usage: 0.11, cuda graph: False, gen throughput (token/s): 63.54, #queue-req: 0
[2025-05-15 22:38:02] Decode batch. #running-req: 1, #token: 2320, token usage: 0.11, cuda graph: False, gen throughput (token/s): 61.77, #queue-req: 0
[2025-05-15 22:38:02] Decode batch. #running-req: 1, #token: 2360, token usage: 0.12, cuda graph: False, gen throughput (token/s): 62.20, #queue-req: 0
[2025-05-15 22:38:03] Decode batch. #running-req: 1, #token: 2400, token usage: 0.12, cuda graph: False, gen throughput (token/s): 61.56, #queue-req: 0
[2025-05-15 22:38:04] Decode batch. #running-req: 1, #token: 2440, token usage: 0.12, cuda graph: False, gen throughput (token/s): 59.56, #queue-req: 0
[2025-05-15 22:38:04] Decode batch. #running-req: 1, #token: 2480, token usage: 0.12, cuda graph: False, gen throughput (token/s): 61.77, #queue-req: 0
[2025-05-15 22:38:05] Decode batch. #running-req: 1, #token: 2520, token usage: 0.12, cuda graph: False, gen throughput (token/s): 62.08, #queue-req: 0
[2025-05-15 22:38:06] Decode batch. #running-req: 1, #token: 2560, token usage: 0.12, cuda graph: False, gen throughput (token/s): 62.20, #queue-req: 0
[2025-05-15 22:38:06] Decode batch. #running-req: 1, #token: 2600, token usage: 0.13, cuda graph: False, gen throughput (token/s): 62.08, #queue-req: 0
[2025-05-15 22:38:07] Decode batch. #running-req: 1, #token: 2640, token usage: 0.13, cuda graph: False, gen throughput (token/s): 62.95, #queue-req: 0
[2025-05-15 22:38:08] Decode batch. #running-req: 1, #token: 2680, token usage: 0.13, cuda graph: False, gen throughput (token/s): 64.39, #queue-req: 0
[2025-05-15 22:38:08] Decode batch. #running-req: 1, #token: 2720, token usage: 0.13, cuda graph: False, gen throughput (token/s): 64.78, #queue-req: 0
[2025-05-15 22:38:09] Decode batch. #running-req: 1, #token: 2760, token usage: 0.13, cuda graph: False, gen throughput (token/s): 64.16, #queue-req: 0
[2025-05-15 22:38:09] Decode batch. #running-req: 1, #token: 2800, token usage: 0.14, cuda graph: False, gen throughput (token/s): 63.53, #queue-req: 0
[2025-05-15 22:38:10] Decode batch. #running-req: 1, #token: 2840, token usage: 0.14, cuda graph: False, gen throughput (token/s): 63.38, #queue-req: 0
[2025-05-15 22:38:11] Decode batch. #running-req: 1, #token: 2880, token usage: 0.14, cuda graph: False, gen throughput (token/s): 63.45, #queue-req: 0
[2025-05-15 22:38:11] Decode batch. #running-req: 1, #token: 2920, token usage: 0.14, cuda graph: False, gen throughput (token/s): 63.39, #queue-req: 0
[2025-05-15 22:38:12] Decode batch. #running-req: 1, #token: 2960, token usage: 0.14, cuda graph: False, gen throughput (token/s): 63.45, #queue-req: 0
[2025-05-15 22:38:13] Decode batch. #running-req: 1, #token: 3000, token usage: 0.15, cuda graph: False, gen throughput (token/s): 61.08, #queue-req: 0
[2025-05-15 22:38:13] Decode batch. #running-req: 1, #token: 3040, token usage: 0.15, cuda graph: False, gen throughput (token/s): 59.21, #queue-req: 0
[2025-05-15 22:38:14] Decode batch. #running-req: 1, #token: 3080, token usage: 0.15, cuda graph: False, gen throughput (token/s): 58.98, #queue-req: 0
[2025-05-15 22:38:15] Decode batch. #running-req: 1, #token: 3120, token usage: 0.15, cuda graph: False, gen throughput (token/s): 62.46, #queue-req: 0
[2025-05-15 22:38:15] Decode batch. #running-req: 1, #token: 3160, token usage: 0.15, cuda graph: False, gen throughput (token/s): 57.02, #queue-req: 0
[2025-05-15 22:38:16] Decode batch. #running-req: 1, #token: 3200, token usage: 0.16, cuda graph: False, gen throughput (token/s): 61.86, #queue-req: 0
[2025-05-15 22:38:17] Decode batch. #running-req: 1, #token: 3240, token usage: 0.16, cuda graph: False, gen throughput (token/s): 61.90, #queue-req: 0
[2025-05-15 22:38:17] Decode batch. #running-req: 1, #token: 3280, token usage: 0.16, cuda graph: False, gen throughput (token/s): 58.78, #queue-req: 0
[2025-05-15 22:38:18] Decode batch. #running-req: 1, #token: 3320, token usage: 0.16, cuda graph: False, gen throughput (token/s): 62.30, #queue-req: 0
[2025-05-15 22:38:19] Decode batch. #running-req: 1, #token: 3360, token usage: 0.16, cuda graph: False, gen throughput (token/s): 62.70, #queue-req: 0
[2025-05-15 22:38:19] Decode batch. #running-req: 1, #token: 3400, token usage: 0.17, cuda graph: False, gen throughput (token/s): 60.11, #queue-req: 0
[2025-05-15 22:38:20] Decode batch. #running-req: 1, #token: 3440, token usage: 0.17, cuda graph: False, gen throughput (token/s): 58.64, #queue-req: 0
[2025-05-15 22:38:21] Decode batch. #running-req: 1, #token: 3480, token usage: 0.17, cuda graph: False, gen throughput (token/s): 56.92, #queue-req: 0
[2025-05-15 22:38:21] Decode batch. #running-req: 1, #token: 3520, token usage: 0.17, cuda graph: False, gen throughput (token/s): 61.27, #queue-req: 0
[2025-05-15 22:38:22] Decode batch. #running-req: 1, #token: 3560, token usage: 0.17, cuda graph: False, gen throughput (token/s): 61.06, #queue-req: 0
[2025-05-15 22:38:23] Decode batch. #running-req: 1, #token: 3600, token usage: 0.18, cuda graph: False, gen throughput (token/s): 62.21, #queue-req: 0
[2025-05-15 22:38:23] Decode batch. #running-req: 1, #token: 3640, token usage: 0.18, cuda graph: False, gen throughput (token/s): 62.63, #queue-req: 0
[2025-05-15 22:38:24] Decode batch. #running-req: 1, #token: 3680, token usage: 0.18, cuda graph: False, gen throughput (token/s): 61.94, #queue-req: 0
[2025-05-15 22:38:24] Decode batch. #running-req: 1, #token: 3720, token usage: 0.18, cuda graph: False, gen throughput (token/s): 62.79, #queue-req: 0
[2025-05-15 22:38:25] Decode batch. #running-req: 1, #token: 3760, token usage: 0.18, cuda graph: False, gen throughput (token/s): 62.06, #queue-req: 0
[2025-05-15 22:38:26] Decode batch. #running-req: 1, #token: 3800, token usage: 0.19, cuda graph: False, gen throughput (token/s): 61.35, #queue-req: 0
[2025-05-15 22:38:26] Decode batch. #running-req: 1, #token: 3840, token usage: 0.19, cuda graph: False, gen throughput (token/s): 62.24, #queue-req: 0
[2025-05-15 22:38:27] Decode batch. #running-req: 1, #token: 3880, token usage: 0.19, cuda graph: False, gen throughput (token/s): 61.56, #queue-req: 0
[2025-05-15 22:38:28] Decode batch. #running-req: 1, #token: 3920, token usage: 0.19, cuda graph: False, gen throughput (token/s): 62.43, #queue-req: 0
[2025-05-15 22:38:28] Decode batch. #running-req: 1, #token: 3960, token usage: 0.19, cuda graph: False, gen throughput (token/s): 62.13, #queue-req: 0
[2025-05-15 22:38:29] Decode batch. #running-req: 1, #token: 4000, token usage: 0.20, cuda graph: False, gen throughput (token/s): 62.12, #queue-req: 0
[2025-05-15 22:38:30] Decode batch. #running-req: 1, #token: 4040, token usage: 0.20, cuda graph: False, gen throughput (token/s): 62.40, #queue-req: 0
[2025-05-15 22:38:30] Decode batch. #running-req: 1, #token: 4080, token usage: 0.20, cuda graph: False, gen throughput (token/s): 62.11, #queue-req: 0
[2025-05-15 22:38:31] Decode batch. #running-req: 1, #token: 4120, token usage: 0.20, cuda graph: False, gen throughput (token/s): 60.05, #queue-req: 0
[2025-05-15 22:38:32] Decode batch. #running-req: 1, #token: 4160, token usage: 0.20, cuda graph: False, gen throughput (token/s): 63.98, #queue-req: 0
[2025-05-15 22:38:32] Decode batch. #running-req: 1, #token: 4200, token usage: 0.21, cuda graph: False, gen throughput (token/s): 64.97, #queue-req: 0
[2025-05-15 22:38:33] Decode batch. #running-req: 1, #token: 4240, token usage: 0.21, cuda graph: False, gen throughput (token/s): 62.29, #queue-req: 0
[2025-05-15 22:38:33] Decode batch. #running-req: 1, #token: 4280, token usage: 0.21, cuda graph: False, gen throughput (token/s): 61.35, #queue-req: 0
[2025-05-15 22:38:34] Decode batch. #running-req: 1, #token: 4320, token usage: 0.21, cuda graph: False, gen throughput (token/s): 62.81, #queue-req: 0
[2025-05-15 22:38:35] Decode batch. #running-req: 1, #token: 4360, token usage: 0.21, cuda graph: False, gen throughput (token/s): 64.06, #queue-req: 0
[2025-05-15 22:38:35] Decode batch. #running-req: 1, #token: 4400, token usage: 0.21, cuda graph: False, gen throughput (token/s): 60.39, #queue-req: 0
[2025-05-15 22:38:36] Decode batch. #running-req: 1, #token: 4440, token usage: 0.22, cuda graph: False, gen throughput (token/s): 55.69, #queue-req: 0
[2025-05-15 22:38:37] Decode batch. #running-req: 1, #token: 4480, token usage: 0.22, cuda graph: False, gen throughput (token/s): 58.78, #queue-req: 0
[2025-05-15 22:38:37] Decode batch. #running-req: 1, #token: 4520, token usage: 0.22, cuda graph: False, gen throughput (token/s): 58.85, #queue-req: 0
[2025-05-15 22:38:38] Decode batch. #running-req: 1, #token: 4560, token usage: 0.22, cuda graph: False, gen throughput (token/s): 58.15, #queue-req: 0
[2025-05-15 22:38:39] Decode batch. #running-req: 1, #token: 4600, token usage: 0.22, cuda graph: False, gen throughput (token/s): 61.17, #queue-req: 0
[2025-05-15 22:38:39] Decode batch. #running-req: 1, #token: 4640, token usage: 0.23, cuda graph: False, gen throughput (token/s): 61.96, #queue-req: 0
[2025-05-15 22:38:40] Decode batch. #running-req: 1, #token: 4680, token usage: 0.23, cuda graph: False, gen throughput (token/s): 62.00, #queue-req: 0
[2025-05-15 22:38:41] Decode batch. #running-req: 1, #token: 4720, token usage: 0.23, cuda graph: False, gen throughput (token/s): 59.27, #queue-req: 0
[2025-05-15 22:38:41] Decode batch. #running-req: 1, #token: 4760, token usage: 0.23, cuda graph: False, gen throughput (token/s): 60.75, #queue-req: 0
[2025-05-15 22:38:42] Decode batch. #running-req: 1, #token: 4800, token usage: 0.23, cuda graph: False, gen throughput (token/s): 63.58, #queue-req: 0
[2025-05-15 22:38:43] Decode batch. #running-req: 1, #token: 4840, token usage: 0.24, cuda graph: False, gen throughput (token/s): 62.35, #queue-req: 0
[2025-05-15 22:38:43] Decode batch. #running-req: 1, #token: 4880, token usage: 0.24, cuda graph: False, gen throughput (token/s): 62.38, #queue-req: 0
[2025-05-15 22:38:44] Decode batch. #running-req: 1, #token: 4920, token usage: 0.24, cuda graph: False, gen throughput (token/s): 62.11, #queue-req: 0
[2025-05-15 22:38:45] Decode batch. #running-req: 1, #token: 4960, token usage: 0.24, cuda graph: False, gen throughput (token/s): 62.97, #queue-req: 0
[2025-05-15 22:38:45] Decode batch. #running-req: 1, #token: 5000, token usage: 0.24, cuda graph: False, gen throughput (token/s): 61.05, #queue-req: 0
[2025-05-15 22:38:46] Decode batch. #running-req: 1, #token: 5040, token usage: 0.25, cuda graph: False, gen throughput (token/s): 62.15, #queue-req: 0
[2025-05-15 22:38:47] Decode batch. #running-req: 1, #token: 5080, token usage: 0.25, cuda graph: False, gen throughput (token/s): 61.24, #queue-req: 0
[2025-05-15 22:38:47] Decode batch. #running-req: 1, #token: 5120, token usage: 0.25, cuda graph: False, gen throughput (token/s): 61.95, #queue-req: 0
[2025-05-15 22:38:48] Decode batch. #running-req: 1, #token: 5160, token usage: 0.25, cuda graph: False, gen throughput (token/s): 64.19, #queue-req: 0
[2025-05-15 22:38:49] Decode batch. #running-req: 1, #token: 5200, token usage: 0.25, cuda graph: False, gen throughput (token/s): 58.29, #queue-req: 0
[2025-05-15 22:38:49] Decode batch. #running-req: 1, #token: 5240, token usage: 0.26, cuda graph: False, gen throughput (token/s): 63.47, #queue-req: 0
[2025-05-15 22:38:50] Decode batch. #running-req: 1, #token: 5280, token usage: 0.26, cuda graph: False, gen throughput (token/s): 63.97, #queue-req: 0
[2025-05-15 22:38:50] Decode batch. #running-req: 1, #token: 5320, token usage: 0.26, cuda graph: False, gen throughput (token/s): 62.77, #queue-req: 0
[2025-05-15 22:38:51] Decode batch. #running-req: 1, #token: 5360, token usage: 0.26, cuda graph: False, gen throughput (token/s): 63.28, #queue-req: 0
[2025-05-15 22:38:52] Decode batch. #running-req: 1, #token: 5400, token usage: 0.26, cuda graph: False, gen throughput (token/s): 63.71, #queue-req: 0
[2025-05-15 22:38:52] Decode batch. #running-req: 1, #token: 5440, token usage: 0.27, cuda graph: False, gen throughput (token/s): 59.91, #queue-req: 0
[2025-05-15 22:38:53] Decode batch. #running-req: 1, #token: 5480, token usage: 0.27, cuda graph: False, gen throughput (token/s): 63.24, #queue-req: 0
[2025-05-15 22:38:54] Decode batch. #running-req: 1, #token: 5520, token usage: 0.27, cuda graph: False, gen throughput (token/s): 62.50, #queue-req: 0
[2025-05-15 22:38:54] Decode batch. #running-req: 1, #token: 5560, token usage: 0.27, cuda graph: False, gen throughput (token/s): 57.74, #queue-req: 0
[2025-05-15 22:38:55] Decode batch. #running-req: 1, #token: 5600, token usage: 0.27, cuda graph: False, gen throughput (token/s): 60.44, #queue-req: 0
[2025-05-15 22:38:56] Decode batch. #running-req: 1, #token: 5640, token usage: 0.28, cuda graph: False, gen throughput (token/s): 63.60, #queue-req: 0
[2025-05-15 22:38:56] Decode batch. #running-req: 1, #token: 5680, token usage: 0.28, cuda graph: False, gen throughput (token/s): 61.74, #queue-req: 0
[2025-05-15 22:38:57] Decode batch. #running-req: 1, #token: 5720, token usage: 0.28, cuda graph: False, gen throughput (token/s): 65.15, #queue-req: 0
[2025-05-15 22:38:57] Decode batch. #running-req: 1, #token: 5760, token usage: 0.28, cuda graph: False, gen throughput (token/s): 64.90, #queue-req: 0
[2025-05-15 22:38:58] Decode batch. #running-req: 1, #token: 5800, token usage: 0.28, cuda graph: False, gen throughput (token/s): 64.17, #queue-req: 0
[2025-05-15 22:38:59] Decode batch. #running-req: 1, #token: 5840, token usage: 0.29, cuda graph: False, gen throughput (token/s): 64.45, #queue-req: 0
[2025-05-15 22:38:59] Decode batch. #running-req: 1, #token: 5880, token usage: 0.29, cuda graph: False, gen throughput (token/s): 65.34, #queue-req: 0
[2025-05-15 22:39:00] Decode batch. #running-req: 1, #token: 5920, token usage: 0.29, cuda graph: False, gen throughput (token/s): 64.04, #queue-req: 0
[2025-05-15 22:39:01] Decode batch. #running-req: 1, #token: 5960, token usage: 0.29, cuda graph: False, gen throughput (token/s): 60.99, #queue-req: 0
[2025-05-15 22:39:01] Decode batch. #running-req: 1, #token: 6000, token usage: 0.29, cuda graph: False, gen throughput (token/s): 63.46, #queue-req: 0
[2025-05-15 22:39:02] Decode batch. #running-req: 1, #token: 6040, token usage: 0.29, cuda graph: False, gen throughput (token/s): 61.55, #queue-req: 0
[2025-05-15 22:39:03] Decode batch. #running-req: 1, #token: 6080, token usage: 0.30, cuda graph: False, gen throughput (token/s): 61.39, #queue-req: 0
[2025-05-15 22:39:03] Decode batch. #running-req: 1, #token: 6120, token usage: 0.30, cuda graph: False, gen throughput (token/s): 62.99, #queue-req: 0
[2025-05-15 22:39:04] Decode batch. #running-req: 1, #token: 6160, token usage: 0.30, cuda graph: False, gen throughput (token/s): 64.23, #queue-req: 0
[2025-05-15 22:39:04] Decode batch. #running-req: 1, #token: 6200, token usage: 0.30, cuda graph: False, gen throughput (token/s): 59.29, #queue-req: 0
[2025-05-15 22:39:05] Decode batch. #running-req: 1, #token: 6240, token usage: 0.30, cuda graph: False, gen throughput (token/s): 63.33, #queue-req: 0
[2025-05-15 22:39:06] Decode batch. #running-req: 1, #token: 6280, token usage: 0.31, cuda graph: False, gen throughput (token/s): 52.55, #queue-req: 0
[2025-05-15 22:39:07] Decode batch. #running-req: 1, #token: 6320, token usage: 0.31, cuda graph: False, gen throughput (token/s): 49.87, #queue-req: 0
[2025-05-15 22:39:07] Decode batch. #running-req: 1, #token: 6360, token usage: 0.31, cuda graph: False, gen throughput (token/s): 49.29, #queue-req: 0
[2025-05-15 22:39:08] Decode batch. #running-req: 1, #token: 6400, token usage: 0.31, cuda graph: False, gen throughput (token/s): 54.77, #queue-req: 0
[2025-05-15 22:39:09] Decode batch. #running-req: 1, #token: 6440, token usage: 0.31, cuda graph: False, gen throughput (token/s): 62.54, #queue-req: 0
[2025-05-15 22:39:10] Decode batch. #running-req: 1, #token: 6480, token usage: 0.32, cuda graph: False, gen throughput (token/s): 61.78, #queue-req: 0
[2025-05-15 22:39:10] Decode batch. #running-req: 1, #token: 6520, token usage: 0.32, cuda graph: False, gen throughput (token/s): 60.93, #queue-req: 0
[2025-05-15 22:39:11] Decode batch. #running-req: 1, #token: 6560, token usage: 0.32, cuda graph: False, gen throughput (token/s): 62.53, #queue-req: 0
[2025-05-15 22:39:11] Decode batch. #running-req: 1, #token: 6600, token usage: 0.32, cuda graph: False, gen throughput (token/s): 61.68, #queue-req: 0
[2025-05-15 22:39:12] Decode batch. #running-req: 1, #token: 6640, token usage: 0.32, cuda graph: False, gen throughput (token/s): 61.70, #queue-req: 0
[2025-05-15 22:39:13] Decode batch. #running-req: 1, #token: 6680, token usage: 0.33, cuda graph: False, gen throughput (token/s): 63.21, #queue-req: 0
[2025-05-15 22:39:13] Decode batch. #running-req: 1, #token: 6720, token usage: 0.33, cuda graph: False, gen throughput (token/s): 63.01, #queue-req: 0
[2025-05-15 22:39:14] Decode batch. #running-req: 1, #token: 6760, token usage: 0.33, cuda graph: False, gen throughput (token/s): 63.76, #queue-req: 0
[2025-05-15 22:39:15] Decode batch. #running-req: 1, #token: 6800, token usage: 0.33, cuda graph: False, gen throughput (token/s): 62.96, #queue-req: 0
[2025-05-15 22:39:15] Decode batch. #running-req: 1, #token: 6840, token usage: 0.33, cuda graph: False, gen throughput (token/s): 63.17, #queue-req: 0
[2025-05-15 22:39:16] Decode batch. #running-req: 1, #token: 6880, token usage: 0.34, cuda graph: False, gen throughput (token/s): 63.93, #queue-req: 0
[2025-05-15 22:39:17] Decode batch. #running-req: 1, #token: 6920, token usage: 0.34, cuda graph: False, gen throughput (token/s): 63.77, #queue-req: 0
[2025-05-15 22:39:17] Decode batch. #running-req: 1, #token: 6960, token usage: 0.34, cuda graph: False, gen throughput (token/s): 60.91, #queue-req: 0
[2025-05-15 22:39:18] Decode batch. #running-req: 1, #token: 7000, token usage: 0.34, cuda graph: False, gen throughput (token/s): 63.72, #queue-req: 0
[2025-05-15 22:39:18] Decode batch. #running-req: 1, #token: 7040, token usage: 0.34, cuda graph: False, gen throughput (token/s): 63.66, #queue-req: 0
[2025-05-15 22:39:19] Decode batch. #running-req: 1, #token: 7080, token usage: 0.35, cuda graph: False, gen throughput (token/s): 62.01, #queue-req: 0
[2025-05-15 22:39:20] Decode batch. #running-req: 1, #token: 7120, token usage: 0.35, cuda graph: False, gen throughput (token/s): 62.90, #queue-req: 0
[2025-05-15 22:39:20] Decode batch. #running-req: 1, #token: 7160, token usage: 0.35, cuda graph: False, gen throughput (token/s): 58.87, #queue-req: 0
[2025-05-15 22:39:21] Decode batch. #running-req: 1, #token: 7200, token usage: 0.35, cuda graph: False, gen throughput (token/s): 63.79, #queue-req: 0
[2025-05-15 22:39:22] Decode batch. #running-req: 1, #token: 7240, token usage: 0.35, cuda graph: False, gen throughput (token/s): 59.92, #queue-req: 0
[2025-05-15 22:39:22] Decode batch. #running-req: 1, #token: 7280, token usage: 0.36, cuda graph: False, gen throughput (token/s): 63.82, #queue-req: 0
[2025-05-15 22:39:23] Decode batch. #running-req: 1, #token: 7320, token usage: 0.36, cuda graph: False, gen throughput (token/s): 63.70, #queue-req: 0
[2025-05-15 22:39:24] Decode batch. #running-req: 1, #token: 7360, token usage: 0.36, cuda graph: False, gen throughput (token/s): 63.77, #queue-req: 0
[2025-05-15 22:39:24] Decode batch. #running-req: 1, #token: 7400, token usage: 0.36, cuda graph: False, gen throughput (token/s): 63.77, #queue-req: 0
[2025-05-15 22:39:25] Decode batch. #running-req: 1, #token: 7440, token usage: 0.36, cuda graph: False, gen throughput (token/s): 63.73, #queue-req: 0
[2025-05-15 22:39:25] Decode batch. #running-req: 1, #token: 7480, token usage: 0.37, cuda graph: False, gen throughput (token/s): 63.85, #queue-req: 0
[2025-05-15 22:39:26] Decode batch. #running-req: 1, #token: 7520, token usage: 0.37, cuda graph: False, gen throughput (token/s): 59.96, #queue-req: 0
[2025-05-15 22:39:27] Decode batch. #running-req: 1, #token: 7560, token usage: 0.37, cuda graph: False, gen throughput (token/s): 62.35, #queue-req: 0
[2025-05-15 22:39:27] Decode batch. #running-req: 1, #token: 7600, token usage: 0.37, cuda graph: False, gen throughput (token/s): 60.82, #queue-req: 0
[2025-05-15 22:39:28] Decode batch. #running-req: 1, #token: 7640, token usage: 0.37, cuda graph: False, gen throughput (token/s): 61.51, #queue-req: 0
[2025-05-15 22:39:29] Decode batch. #running-req: 1, #token: 7680, token usage: 0.38, cuda graph: False, gen throughput (token/s): 62.27, #queue-req: 0
[2025-05-15 22:39:29] Decode batch. #running-req: 1, #token: 7720, token usage: 0.38, cuda graph: False, gen throughput (token/s): 65.05, #queue-req: 0
[2025-05-15 22:39:30] Decode batch. #running-req: 1, #token: 7760, token usage: 0.38, cuda graph: False, gen throughput (token/s): 65.40, #queue-req: 0
[2025-05-15 22:39:31] Decode batch. #running-req: 1, #token: 7800, token usage: 0.38, cuda graph: False, gen throughput (token/s): 65.45, #queue-req: 0
[2025-05-15 22:39:31] Decode batch. #running-req: 1, #token: 7840, token usage: 0.38, cuda graph: False, gen throughput (token/s): 65.48, #queue-req: 0
[2025-05-15 22:39:32] Decode batch. #running-req: 1, #token: 7880, token usage: 0.38, cuda graph: False, gen throughput (token/s): 63.21, #queue-req: 0
[2025-05-15 22:39:32] Decode batch. #running-req: 1, #token: 7920, token usage: 0.39, cuda graph: False, gen throughput (token/s): 65.26, #queue-req: 0
[2025-05-15 22:39:33] Decode batch. #running-req: 1, #token: 7960, token usage: 0.39, cuda graph: False, gen throughput (token/s): 64.38, #queue-req: 0
[2025-05-15 22:39:34] Decode batch. #running-req: 1, #token: 8000, token usage: 0.39, cuda graph: False, gen throughput (token/s): 63.33, #queue-req: 0
[2025-05-15 22:39:34] Decode batch. #running-req: 1, #token: 8040, token usage: 0.39, cuda graph: False, gen throughput (token/s): 61.92, #queue-req: 0
[2025-05-15 22:39:35] Decode batch. #running-req: 1, #token: 8080, token usage: 0.39, cuda graph: False, gen throughput (token/s): 64.02, #queue-req: 0
[2025-05-15 22:39:36] Decode batch. #running-req: 1, #token: 8120, token usage: 0.40, cuda graph: False, gen throughput (token/s): 63.48, #queue-req: 0
[2025-05-15 22:39:36] Decode batch. #running-req: 1, #token: 8160, token usage: 0.40, cuda graph: False, gen throughput (token/s): 63.11, #queue-req: 0
[2025-05-15 22:39:37] Decode batch. #running-req: 1, #token: 8200, token usage: 0.40, cuda graph: False, gen throughput (token/s): 63.82, #queue-req: 0
[2025-05-15 22:39:37] Decode batch. #running-req: 1, #token: 8240, token usage: 0.40, cuda graph: False, gen throughput (token/s): 64.54, #queue-req: 0
[2025-05-15 22:39:38] Decode batch. #running-req: 1, #token: 8280, token usage: 0.40, cuda graph: False, gen throughput (token/s): 64.13, #queue-req: 0
[2025-05-15 22:39:39] Decode batch. #running-req: 1, #token: 8320, token usage: 0.41, cuda graph: False, gen throughput (token/s): 61.11, #queue-req: 0
[2025-05-15 22:39:39] Decode batch. #running-req: 1, #token: 8360, token usage: 0.41, cuda graph: False, gen throughput (token/s): 62.64, #queue-req: 0
[2025-05-15 22:39:40] Decode batch. #running-req: 1, #token: 8400, token usage: 0.41, cuda graph: False, gen throughput (token/s): 63.54, #queue-req: 0
[2025-05-15 22:39:41] Decode batch. #running-req: 1, #token: 8440, token usage: 0.41, cuda graph: False, gen throughput (token/s): 63.87, #queue-req: 0
[2025-05-15 22:39:41] Decode batch. #running-req: 1, #token: 8480, token usage: 0.41, cuda graph: False, gen throughput (token/s): 63.95, #queue-req: 0
[2025-05-15 22:39:42] Decode batch. #running-req: 1, #token: 8520, token usage: 0.42, cuda graph: False, gen throughput (token/s): 64.11, #queue-req: 0
[2025-05-15 22:39:42] Decode batch. #running-req: 1, #token: 8560, token usage: 0.42, cuda graph: False, gen throughput (token/s): 64.43, #queue-req: 0
[2025-05-15 22:39:43] Decode batch. #running-req: 1, #token: 8600, token usage: 0.42, cuda graph: False, gen throughput (token/s): 64.37, #queue-req: 0
[2025-05-15 22:39:44] Decode batch. #running-req: 1, #token: 8640, token usage: 0.42, cuda graph: False, gen throughput (token/s): 62.59, #queue-req: 0
[2025-05-15 22:39:44] Decode batch. #running-req: 1, #token: 8680, token usage: 0.42, cuda graph: False, gen throughput (token/s): 59.85, #queue-req: 0
[2025-05-15 22:39:45] Decode batch. #running-req: 1, #token: 8720, token usage: 0.43, cuda graph: False, gen throughput (token/s): 62.89, #queue-req: 0
[2025-05-15 22:39:46] Decode batch. #running-req: 1, #token: 8760, token usage: 0.43, cuda graph: False, gen throughput (token/s): 62.83, #queue-req: 0
[2025-05-15 22:39:46] Decode batch. #running-req: 1, #token: 8800, token usage: 0.43, cuda graph: False, gen throughput (token/s): 61.33, #queue-req: 0
[2025-05-15 22:39:47] Decode batch. #running-req: 1, #token: 8840, token usage: 0.43, cuda graph: False, gen throughput (token/s): 64.63, #queue-req: 0
[2025-05-15 22:39:48] Decode batch. #running-req: 1, #token: 8880, token usage: 0.43, cuda graph: False, gen throughput (token/s): 64.96, #queue-req: 0
[2025-05-15 22:39:48] Decode batch. #running-req: 1, #token: 8920, token usage: 0.44, cuda graph: False, gen throughput (token/s): 64.97, #queue-req: 0
[2025-05-15 22:39:49] Decode batch. #running-req: 1, #token: 8960, token usage: 0.44, cuda graph: False, gen throughput (token/s): 65.42, #queue-req: 0
[2025-05-15 22:39:49] Decode batch. #running-req: 1, #token: 9000, token usage: 0.44, cuda graph: False, gen throughput (token/s): 64.92, #queue-req: 0
[2025-05-15 22:39:50] Decode batch. #running-req: 1, #token: 9040, token usage: 0.44, cuda graph: False, gen throughput (token/s): 59.21, #queue-req: 0
[2025-05-15 22:39:51] Decode batch. #running-req: 1, #token: 9080, token usage: 0.44, cuda graph: False, gen throughput (token/s): 63.15, #queue-req: 0
[2025-05-15 22:39:51] Decode batch. #running-req: 1, #token: 9120, token usage: 0.45, cuda graph: False, gen throughput (token/s): 65.21, #queue-req: 0
[2025-05-15 22:39:52] Decode batch. #running-req: 1, #token: 9160, token usage: 0.45, cuda graph: False, gen throughput (token/s): 63.68, #queue-req: 0
[2025-05-15 22:39:53] Decode batch. #running-req: 1, #token: 9200, token usage: 0.45, cuda graph: False, gen throughput (token/s): 64.79, #queue-req: 0
[2025-05-15 22:39:53] Decode batch. #running-req: 1, #token: 9240, token usage: 0.45, cuda graph: False, gen throughput (token/s): 64.90, #queue-req: 0
[2025-05-15 22:39:54] Decode batch. #running-req: 1, #token: 9280, token usage: 0.45, cuda graph: False, gen throughput (token/s): 63.07, #queue-req: 0
[2025-05-15 22:39:54] Decode batch. #running-req: 1, #token: 9320, token usage: 0.46, cuda graph: False, gen throughput (token/s): 62.36, #queue-req: 0
[2025-05-15 22:39:55] Decode batch. #running-req: 1, #token: 9360, token usage: 0.46, cuda graph: False, gen throughput (token/s): 62.61, #queue-req: 0
[2025-05-15 22:39:56] Decode batch. #running-req: 1, #token: 9400, token usage: 0.46, cuda graph: False, gen throughput (token/s): 64.25, #queue-req: 0
[2025-05-15 22:39:56] Decode batch. #running-req: 1, #token: 9440, token usage: 0.46, cuda graph: False, gen throughput (token/s): 64.86, #queue-req: 0
[2025-05-15 22:39:57] Decode batch. #running-req: 1, #token: 9480, token usage: 0.46, cuda graph: False, gen throughput (token/s): 64.92, #queue-req: 0
[2025-05-15 22:39:58] Decode batch. #running-req: 1, #token: 9520, token usage: 0.46, cuda graph: False, gen throughput (token/s): 65.01, #queue-req: 0
[2025-05-15 22:39:58] Decode batch. #running-req: 1, #token: 9560, token usage: 0.47, cuda graph: False, gen throughput (token/s): 65.00, #queue-req: 0
[2025-05-15 22:39:59] Decode batch. #running-req: 1, #token: 9600, token usage: 0.47, cuda graph: False, gen throughput (token/s): 65.15, #queue-req: 0
[2025-05-15 22:39:59] Decode batch. #running-req: 1, #token: 9640, token usage: 0.47, cuda graph: False, gen throughput (token/s): 65.03, #queue-req: 0
[2025-05-15 22:40:00] Decode batch. #running-req: 1, #token: 9680, token usage: 0.47, cuda graph: False, gen throughput (token/s): 65.11, #queue-req: 0
[2025-05-15 22:40:01] Decode batch. #running-req: 1, #token: 9720, token usage: 0.47, cuda graph: False, gen throughput (token/s): 64.36, #queue-req: 0
[2025-05-15 22:40:01] Decode batch. #running-req: 1, #token: 9760, token usage: 0.48, cuda graph: False, gen throughput (token/s): 64.69, #queue-req: 0
[2025-05-15 22:40:02] Decode batch. #running-req: 1, #token: 9800, token usage: 0.48, cuda graph: False, gen throughput (token/s): 64.47, #queue-req: 0
[2025-05-15 22:40:03] Decode batch. #running-req: 1, #token: 9840, token usage: 0.48, cuda graph: False, gen throughput (token/s): 64.28, #queue-req: 0
[2025-05-15 22:40:03] Decode batch. #running-req: 1, #token: 9880, token usage: 0.48, cuda graph: False, gen throughput (token/s): 64.20, #queue-req: 0
[2025-05-15 22:40:04] Decode batch. #running-req: 1, #token: 9920, token usage: 0.48, cuda graph: False, gen throughput (token/s): 64.36, #queue-req: 0
[2025-05-15 22:40:04] Decode batch. #running-req: 1, #token: 9960, token usage: 0.49, cuda graph: False, gen throughput (token/s): 64.37, #queue-req: 0
[2025-05-15 22:40:05] Decode batch. #running-req: 1, #token: 10000, token usage: 0.49, cuda graph: False, gen throughput (token/s): 64.43, #queue-req: 0
[2025-05-15 22:40:06] Decode batch. #running-req: 1, #token: 10040, token usage: 0.49, cuda graph: False, gen throughput (token/s): 64.31, #queue-req: 0
[2025-05-15 22:40:06] Decode batch. #running-req: 1, #token: 10080, token usage: 0.49, cuda graph: False, gen throughput (token/s): 64.03, #queue-req: 0
[2025-05-15 22:40:07] Decode batch. #running-req: 1, #token: 10120, token usage: 0.49, cuda graph: False, gen throughput (token/s): 64.12, #queue-req: 0
[2025-05-15 22:40:08] Decode batch. #running-req: 1, #token: 10160, token usage: 0.50, cuda graph: False, gen throughput (token/s): 64.15, #queue-req: 0
[2025-05-15 22:40:08] Decode batch. #running-req: 1, #token: 10200, token usage: 0.50, cuda graph: False, gen throughput (token/s): 62.70, #queue-req: 0
[2025-05-15 22:40:09] Decode batch. #running-req: 1, #token: 10240, token usage: 0.50, cuda graph: False, gen throughput (token/s): 61.07, #queue-req: 0
[2025-05-15 22:40:09] Decode batch. #running-req: 1, #token: 10280, token usage: 0.50, cuda graph: False, gen throughput (token/s): 64.67, #queue-req: 0
[2025-05-15 22:40:10] Decode batch. #running-req: 1, #token: 10320, token usage: 0.50, cuda graph: False, gen throughput (token/s): 64.68, #queue-req: 0
[2025-05-15 22:40:11] Decode batch. #running-req: 1, #token: 10360, token usage: 0.51, cuda graph: False, gen throughput (token/s): 57.94, #queue-req: 0
[2025-05-15 22:40:11] Decode batch. #running-req: 1, #token: 10400, token usage: 0.51, cuda graph: False, gen throughput (token/s): 61.77, #queue-req: 0
[2025-05-15 22:40:12] Decode batch. #running-req: 1, #token: 10440, token usage: 0.51, cuda graph: False, gen throughput (token/s): 61.74, #queue-req: 0
[2025-05-15 22:40:13] Decode batch. #running-req: 1, #token: 10480, token usage: 0.51, cuda graph: False, gen throughput (token/s): 61.83, #queue-req: 0
[2025-05-15 22:40:13] Decode batch. #running-req: 1, #token: 10520, token usage: 0.51, cuda graph: False, gen throughput (token/s): 63.00, #queue-req: 0
[2025-05-15 22:40:14] Decode batch. #running-req: 1, #token: 10560, token usage: 0.52, cuda graph: False, gen throughput (token/s): 63.59, #queue-req: 0
[2025-05-15 22:40:15] Decode batch. #running-req: 1, #token: 10600, token usage: 0.52, cuda graph: False, gen throughput (token/s): 62.70, #queue-req: 0
[2025-05-15 22:40:15] Decode batch. #running-req: 1, #token: 10640, token usage: 0.52, cuda graph: False, gen throughput (token/s): 64.73, #queue-req: 0
[2025-05-15 22:40:16] Decode batch. #running-req: 1, #token: 10680, token usage: 0.52, cuda graph: False, gen throughput (token/s): 64.62, #queue-req: 0
[2025-05-15 22:40:16] Decode batch. #running-req: 1, #token: 10720, token usage: 0.52, cuda graph: False, gen throughput (token/s): 64.68, #queue-req: 0
[2025-05-15 22:40:17] Decode batch. #running-req: 1, #token: 10760, token usage: 0.53, cuda graph: False, gen throughput (token/s): 64.78, #queue-req: 0
[2025-05-15 22:40:18] Decode batch. #running-req: 1, #token: 10800, token usage: 0.53, cuda graph: False, gen throughput (token/s): 64.87, #queue-req: 0
[2025-05-15 22:40:18] Decode batch. #running-req: 1, #token: 10840, token usage: 0.53, cuda graph: False, gen throughput (token/s): 64.74, #queue-req: 0
[2025-05-15 22:40:19] Decode batch. #running-req: 1, #token: 10880, token usage: 0.53, cuda graph: False, gen throughput (token/s): 64.84, #queue-req: 0
[2025-05-15 22:40:20] Decode batch. #running-req: 1, #token: 10920, token usage: 0.53, cuda graph: False, gen throughput (token/s): 64.86, #queue-req: 0
[2025-05-15 22:40:20] Decode batch. #running-req: 1, #token: 10960, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.93, #queue-req: 0
[2025-05-15 22:40:21] Decode batch. #running-req: 1, #token: 11000, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.76, #queue-req: 0
[2025-05-15 22:40:21] Decode batch. #running-req: 1, #token: 11040, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.55, #queue-req: 0
[2025-05-15 22:40:22] Decode batch. #running-req: 1, #token: 11080, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.57, #queue-req: 0
[2025-05-15 22:40:23] Decode batch. #running-req: 1, #token: 11120, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.76, #queue-req: 0
[2025-05-15 22:40:23] Decode batch. #running-req: 1, #token: 11160, token usage: 0.54, cuda graph: False, gen throughput (token/s): 64.79, #queue-req: 0
[2025-05-15 22:40:24] Decode batch. #running-req: 1, #token: 11200, token usage: 0.55, cuda graph: False, gen throughput (token/s): 64.80, #queue-req: 0
[2025-05-15 22:40:24] Decode batch. #running-req: 1, #token: 11240, token usage: 0.55, cuda graph: False, gen throughput (token/s): 64.71, #queue-req: 0
[2025-05-15 22:40:25] Decode batch. #running-req: 1, #token: 11280, token usage: 0.55, cuda graph: False, gen throughput (token/s): 64.63, #queue-req: 0
[2025-05-15 22:40:26] Decode batch. #running-req: 1, #token: 11320, token usage: 0.55, cuda graph: False, gen throughput (token/s): 64.75, #queue-req: 0
[2025-05-15 22:40:26] Decode batch. #running-req: 1, #token: 11360, token usage: 0.55, cuda graph: False, gen throughput (token/s): 62.64, #queue-req: 0
[2025-05-15 22:40:27] Decode batch. #running-req: 1, #token: 11400, token usage: 0.56, cuda graph: False, gen throughput (token/s): 64.21, #queue-req: 0
[2025-05-15 22:40:28] Decode batch. #running-req: 1, #token: 11440, token usage: 0.56, cuda graph: False, gen throughput (token/s): 64.48, #queue-req: 0
[2025-05-15 22:40:28] Decode batch. #running-req: 1, #token: 11480, token usage: 0.56, cuda graph: False, gen throughput (token/s): 61.78, #queue-req: 0
[2025-05-15 22:40:29] Decode batch. #running-req: 1, #token: 11520, token usage: 0.56, cuda graph: False, gen throughput (token/s): 63.40, #queue-req: 0
[2025-05-15 22:40:29] Decode batch. #running-req: 1, #token: 11560, token usage: 0.56, cuda graph: False, gen throughput (token/s): 63.98, #queue-req: 0
[2025-05-15 22:40:30] Decode batch. #running-req: 1, #token: 11600, token usage: 0.57, cuda graph: False, gen throughput (token/s): 62.74, #queue-req: 0
[2025-05-15 22:40:31] Decode batch. #running-req: 1, #token: 11640, token usage: 0.57, cuda graph: False, gen throughput (token/s): 66.53, #queue-req: 0
[2025-05-15 22:40:31] Decode batch. #running-req: 1, #token: 11680, token usage: 0.57, cuda graph: False, gen throughput (token/s): 66.50, #queue-req: 0
[2025-05-15 22:40:32] Decode batch. #running-req: 1, #token: 11720, token usage: 0.57, cuda graph: False, gen throughput (token/s): 66.39, #queue-req: 0
[2025-05-15 22:40:33] Decode batch. #running-req: 1, #token: 11760, token usage: 0.57, cuda graph: False, gen throughput (token/s): 66.40, #queue-req: 0
[2025-05-15 22:40:33] Decode batch. #running-req: 1, #token: 11800, token usage: 0.58, cuda graph: False, gen throughput (token/s): 58.73, #queue-req: 0
[2025-05-15 22:40:34] Decode batch. #running-req: 1, #token: 11840, token usage: 0.58, cuda graph: False, gen throughput (token/s): 59.38, #queue-req: 0
[2025-05-15 22:40:34] Decode batch. #running-req: 1, #token: 11880, token usage: 0.58, cuda graph: False, gen throughput (token/s): 64.45, #queue-req: 0
[2025-05-15 22:40:35] Decode batch. #running-req: 1, #token: 11920, token usage: 0.58, cuda graph: False, gen throughput (token/s): 65.06, #queue-req: 0
[2025-05-15 22:40:36] Decode batch. #running-req: 1, #token: 11960, token usage: 0.58, cuda graph: False, gen throughput (token/s): 65.07, #queue-req: 0
[2025-05-15 22:40:36] Decode batch. #running-req: 1, #token: 12000, token usage: 0.59, cuda graph: False, gen throughput (token/s): 61.56, #queue-req: 0
[2025-05-15 22:40:37] Decode batch. #running-req: 1, #token: 12040, token usage: 0.59, cuda graph: False, gen throughput (token/s): 63.08, #queue-req: 0
[2025-05-15 22:40:38] Decode batch. #running-req: 1, #token: 12080, token usage: 0.59, cuda graph: False, gen throughput (token/s): 61.04, #queue-req: 0
[2025-05-15 22:40:38] Decode batch. #running-req: 1, #token: 12120, token usage: 0.59, cuda graph: False, gen throughput (token/s): 63.65, #queue-req: 0
[2025-05-15 22:40:39] Decode batch. #running-req: 1, #token: 12160, token usage: 0.59, cuda graph: False, gen throughput (token/s): 63.04, #queue-req: 0
[2025-05-15 22:40:40] Decode batch. #running-req: 1, #token: 12200, token usage: 0.60, cuda graph: False, gen throughput (token/s): 62.90, #queue-req: 0
[2025-05-15 22:40:40] Decode batch. #running-req: 1, #token: 12240, token usage: 0.60, cuda graph: False, gen throughput (token/s): 63.31, #queue-req: 0
[2025-05-15 22:40:41] Decode batch. #running-req: 1, #token: 12280, token usage: 0.60, cuda graph: False, gen throughput (token/s): 63.61, #queue-req: 0
[2025-05-15 22:40:41] Decode batch. #running-req: 1, #token: 12320, token usage: 0.60, cuda graph: False, gen throughput (token/s): 64.76, #queue-req: 0
[2025-05-15 22:40:42] Decode batch. #running-req: 1, #token: 12360, token usage: 0.60, cuda graph: False, gen throughput (token/s): 64.94, #queue-req: 0
[2025-05-15 22:40:43] Decode batch. #running-req: 1, #token: 12400, token usage: 0.61, cuda graph: False, gen throughput (token/s): 64.80, #queue-req: 0
[2025-05-15 22:40:43] Decode batch. #running-req: 1, #token: 12440, token usage: 0.61, cuda graph: False, gen throughput (token/s): 64.75, #queue-req: 0
[2025-05-15 22:40:44] Decode batch. #running-req: 1, #token: 12480, token usage: 0.61, cuda graph: False, gen throughput (token/s): 64.73, #queue-req: 0
[2025-05-15 22:40:45] Decode batch. #running-req: 1, #token: 12520, token usage: 0.61, cuda graph: False, gen throughput (token/s): 60.75, #queue-req: 0
[2025-05-15 22:40:45] Decode batch. #running-req: 1, #token: 12560, token usage: 0.61, cuda graph: False, gen throughput (token/s): 62.30, #queue-req: 0
[2025-05-15 22:40:46] Decode batch. #running-req: 1, #token: 12600, token usage: 0.62, cuda graph: False, gen throughput (token/s): 64.09, #queue-req: 0
[2025-05-15 22:40:46] Decode batch. #running-req: 1, #token: 12640, token usage: 0.62, cuda graph: False, gen throughput (token/s): 65.05, #queue-req: 0
[2025-05-15 22:40:47] Decode batch. #running-req: 1, #token: 12680, token usage: 0.62, cuda graph: False, gen throughput (token/s): 63.75, #queue-req: 0
[2025-05-15 22:40:48] Decode batch. #running-req: 1, #token: 12720, token usage: 0.62, cuda graph: False, gen throughput (token/s): 65.53, #queue-req: 0
[2025-05-15 22:40:48] Decode batch. #running-req: 1, #token: 12760, token usage: 0.62, cuda graph: False, gen throughput (token/s): 61.31, #queue-req: 0
[2025-05-15 22:40:49] Decode batch. #running-req: 1, #token: 12800, token usage: 0.62, cuda graph: False, gen throughput (token/s): 64.75, #queue-req: 0
[2025-05-15 22:40:50] Decode batch. #running-req: 1, #token: 12840, token usage: 0.63, cuda graph: False, gen throughput (token/s): 62.25, #queue-req: 0
[2025-05-15 22:40:50] Decode batch. #running-req: 1, #token: 12880, token usage: 0.63, cuda graph: False, gen throughput (token/s): 57.49, #queue-req: 0
[2025-05-15 22:40:51] Decode batch. #running-req: 1, #token: 12920, token usage: 0.63, cuda graph: False, gen throughput (token/s): 46.29, #queue-req: 0
[2025-05-15 22:40:52] Decode batch. #running-req: 1, #token: 12960, token usage: 0.63, cuda graph: False, gen throughput (token/s): 49.75, #queue-req: 0
[2025-05-15 22:40:53] Decode batch. #running-req: 1, #token: 13000, token usage: 0.63, cuda graph: False, gen throughput (token/s): 64.74, #queue-req: 0
[2025-05-15 22:40:53] Decode batch. #running-req: 1, #token: 13040, token usage: 0.64, cuda graph: False, gen throughput (token/s): 64.54, #queue-req: 0
[2025-05-15 22:40:54] Decode batch. #running-req: 1, #token: 13080, token usage: 0.64, cuda graph: False, gen throughput (token/s): 64.01, #queue-req: 0
[2025-05-15 22:40:54] Decode batch. #running-req: 1, #token: 13120, token usage: 0.64, cuda graph: False, gen throughput (token/s): 64.25, #queue-req: 0
[2025-05-15 22:40:55] Decode batch. #running-req: 1, #token: 13160, token usage: 0.64, cuda graph: False, gen throughput (token/s): 64.35, #queue-req: 0
[2025-05-15 22:40:56] Decode batch. #running-req: 1, #token: 13200, token usage: 0.64, cuda graph: False, gen throughput (token/s): 63.79, #queue-req: 0
[2025-05-15 22:40:56] Decode batch. #running-req: 1, #token: 13240, token usage: 0.65, cuda graph: False, gen throughput (token/s): 64.75, #queue-req: 0
[2025-05-15 22:40:57] Decode batch. #running-req: 1, #token: 13280, token usage: 0.65, cuda graph: False, gen throughput (token/s): 64.88, #queue-req: 0
[2025-05-15 22:40:58] Decode batch. #running-req: 1, #token: 13320, token usage: 0.65, cuda graph: False, gen throughput (token/s): 64.88, #queue-req: 0
[2025-05-15 22:40:58] Decode batch. #running-req: 1, #token: 13360, token usage: 0.65, cuda graph: False, gen throughput (token/s): 61.67, #queue-req: 0
[2025-05-15 22:40:59] Decode batch. #running-req: 1, #token: 13400, token usage: 0.65, cuda graph: False, gen throughput (token/s): 64.56, #queue-req: 0
[2025-05-15 22:40:59] Decode batch. #running-req: 1, #token: 13440, token usage: 0.66, cuda graph: False, gen throughput (token/s): 64.62, #queue-req: 0
[2025-05-15 22:41:00] Decode batch. #running-req: 1, #token: 13480, token usage: 0.66, cuda graph: False, gen throughput (token/s): 54.97, #queue-req: 0
[2025-05-15 22:41:01] Decode batch. #running-req: 1, #token: 13520, token usage: 0.66, cuda graph: False, gen throughput (token/s): 52.56, #queue-req: 0
[2025-05-15 22:41:02] Decode batch. #running-req: 1, #token: 13560, token usage: 0.66, cuda graph: False, gen throughput (token/s): 33.52, #queue-req: 0
[2025-05-15 22:41:03] Decode batch. #running-req: 1, #token: 13600, token usage: 0.66, cuda graph: False, gen throughput (token/s): 34.54, #queue-req: 0
[2025-05-15 22:41:04] Decode batch. #running-req: 1, #token: 13640, token usage: 0.67, cuda graph: False, gen throughput (token/s): 51.56, #queue-req: 0
[2025-05-15 22:41:05] Decode batch. #running-req: 1, #token: 13680, token usage: 0.67, cuda graph: False, gen throughput (token/s): 59.70, #queue-req: 0
[2025-05-15 22:41:05] Decode batch. #running-req: 1, #token: 13720, token usage: 0.67, cuda graph: False, gen throughput (token/s): 64.00, #queue-req: 0
[2025-05-15 22:41:06] Decode batch. #running-req: 1, #token: 13760, token usage: 0.67, cuda graph: False, gen throughput (token/s): 59.10, #queue-req: 0
[2025-05-15 22:41:07] Decode batch. #running-req: 1, #token: 13800, token usage: 0.67, cuda graph: False, gen throughput (token/s): 60.86, #queue-req: 0
[2025-05-15 22:41:07] Decode batch. #running-req: 1, #token: 13840, token usage: 0.68, cuda graph: False, gen throughput (token/s): 63.01, #queue-req: 0
[2025-05-15 22:41:08] Decode batch. #running-req: 1, #token: 13880, token usage: 0.68, cuda graph: False, gen throughput (token/s): 63.01, #queue-req: 0
[2025-05-15 22:41:09] Decode batch. #running-req: 1, #token: 13920, token usage: 0.68, cuda graph: False, gen throughput (token/s): 63.53, #queue-req: 0
[2025-05-15 22:41:09] Decode batch. #running-req: 1, #token: 13960, token usage: 0.68, cuda graph: False, gen throughput (token/s): 61.55, #queue-req: 0
[2025-05-15 22:41:10] Decode batch. #running-req: 1, #token: 14000, token usage: 0.68, cuda graph: False, gen throughput (token/s): 59.82, #queue-req: 0
[2025-05-15 22:41:11] Decode batch. #running-req: 1, #token: 14040, token usage: 0.69, cuda graph: False, gen throughput (token/s): 60.60, #queue-req: 0
[2025-05-15 22:41:11] Decode batch. #running-req: 1, #token: 14080, token usage: 0.69, cuda graph: False, gen throughput (token/s): 62.64, #queue-req: 0
[2025-05-15 22:41:12] Decode batch. #running-req: 1, #token: 14120, token usage: 0.69, cuda graph: False, gen throughput (token/s): 62.06, #queue-req: 0
[2025-05-15 22:41:12] Decode batch. #running-req: 1, #token: 14160, token usage: 0.69, cuda graph: False, gen throughput (token/s): 62.67, #queue-req: 0
[2025-05-15 22:41:13] Decode batch. #running-req: 1, #token: 14200, token usage: 0.69, cuda graph: False, gen throughput (token/s): 61.57, #queue-req: 0
[2025-05-15 22:41:14] Decode batch. #running-req: 1, #token: 14240, token usage: 0.70, cuda graph: False, gen throughput (token/s): 59.56, #queue-req: 0
[2025-05-15 22:41:15] Decode batch. #running-req: 1, #token: 14280, token usage: 0.70, cuda graph: False, gen throughput (token/s): 56.66, #queue-req: 0
[2025-05-15 22:41:15] Decode batch. #running-req: 1, #token: 14320, token usage: 0.70, cuda graph: False, gen throughput (token/s): 60.51, #queue-req: 0
[2025-05-15 22:41:16] Decode batch. #running-req: 1, #token: 14360, token usage: 0.70, cuda graph: False, gen throughput (token/s): 58.28, #queue-req: 0
[2025-05-15 22:41:17] Decode batch. #running-req: 1, #token: 14400, token usage: 0.70, cuda graph: False, gen throughput (token/s): 58.36, #queue-req: 0
[2025-05-15 22:41:17] Decode batch. #running-req: 1, #token: 14440, token usage: 0.71, cuda graph: False, gen throughput (token/s): 59.62, #queue-req: 0
[2025-05-15 22:41:18] Decode batch. #running-req: 1, #token: 14480, token usage: 0.71, cuda graph: False, gen throughput (token/s): 60.97, #queue-req: 0
[2025-05-15 22:41:19] Decode batch. #running-req: 1, #token: 14520, token usage: 0.71, cuda graph: False, gen throughput (token/s): 62.78, #queue-req: 0
[2025-05-15 22:41:19] Decode batch. #running-req: 1, #token: 14560, token usage: 0.71, cuda graph: False, gen throughput (token/s): 62.71, #queue-req: 0
[2025-05-15 22:41:20] Decode batch. #running-req: 1, #token: 14600, token usage: 0.71, cuda graph: False, gen throughput (token/s): 61.88, #queue-req: 0
[2025-05-15 22:41:20] Decode batch. #running-req: 1, #token: 14640, token usage: 0.71, cuda graph: False, gen throughput (token/s): 63.01, #queue-req: 0
[2025-05-15 22:41:21] Decode batch. #running-req: 1, #token: 14680, token usage: 0.72, cuda graph: False, gen throughput (token/s): 62.97, #queue-req: 0
[2025-05-15 22:41:22] Decode batch. #running-req: 1, #token: 14720, token usage: 0.72, cuda graph: False, gen throughput (token/s): 58.96, #queue-req: 0
[2025-05-15 22:41:23] Decode batch. #running-req: 1, #token: 14760, token usage: 0.72, cuda graph: False, gen throughput (token/s): 50.84, #queue-req: 0
[2025-05-15 22:41:23] Decode batch. #running-req: 1, #token: 14800, token usage: 0.72, cuda graph: False, gen throughput (token/s): 50.09, #queue-req: 0
[2025-05-15 22:41:24] Decode batch. #running-req: 1, #token: 14840, token usage: 0.72, cuda graph: False, gen throughput (token/s): 49.86, #queue-req: 0
[2025-05-15 22:41:25] Decode batch. #running-req: 1, #token: 14880, token usage: 0.73, cuda graph: False, gen throughput (token/s): 49.81, #queue-req: 0
[2025-05-15 22:41:26] Decode batch. #running-req: 1, #token: 14920, token usage: 0.73, cuda graph: False, gen throughput (token/s): 49.48, #queue-req: 0
[2025-05-15 22:41:26] Decode batch. #running-req: 1, #token: 14960, token usage: 0.73, cuda graph: False, gen throughput (token/s): 55.21, #queue-req: 0
[2025-05-15 22:41:27] Decode batch. #running-req: 1, #token: 15000, token usage: 0.73, cuda graph: False, gen throughput (token/s): 60.40, #queue-req: 0
[2025-05-15 22:41:28] Decode batch. #running-req: 1, #token: 15040, token usage: 0.73, cuda graph: False, gen throughput (token/s): 63.06, #queue-req: 0
[2025-05-15 22:41:28] Decode batch. #running-req: 1, #token: 15080, token usage: 0.74, cuda graph: False, gen throughput (token/s): 61.87, #queue-req: 0
[2025-05-15 22:41:29] Decode batch. #running-req: 1, #token: 15120, token usage: 0.74, cuda graph: False, gen throughput (token/s): 63.36, #queue-req: 0
[2025-05-15 22:41:30] Decode batch. #running-req: 1, #token: 15160, token usage: 0.74, cuda graph: False, gen throughput (token/s): 62.78, #queue-req: 0
[2025-05-15 22:41:30] Decode batch. #running-req: 1, #token: 15200, token usage: 0.74, cuda graph: False, gen throughput (token/s): 64.72, #queue-req: 0
[2025-05-15 22:41:31] Decode batch. #running-req: 1, #token: 15240, token usage: 0.74, cuda graph: False, gen throughput (token/s): 64.62, #queue-req: 0
[2025-05-15 22:41:32] Decode batch. #running-req: 1, #token: 15280, token usage: 0.75, cuda graph: False, gen throughput (token/s): 60.20, #queue-req: 0
[2025-05-15 22:41:32] Decode batch. #running-req: 1, #token: 15320, token usage: 0.75, cuda graph: False, gen throughput (token/s): 63.49, #queue-req: 0
[2025-05-15 22:41:33] Decode batch. #running-req: 1, #token: 15360, token usage: 0.75, cuda graph: False, gen throughput (token/s): 64.46, #queue-req: 0
[2025-05-15 22:41:33] Decode batch. #running-req: 1, #token: 15400, token usage: 0.75, cuda graph: False, gen throughput (token/s): 61.59, #queue-req: 0
[2025-05-15 22:41:34] Decode batch. #running-req: 1, #token: 15440, token usage: 0.75, cuda graph: False, gen throughput (token/s): 63.42, #queue-req: 0
[2025-05-15 22:41:35] Decode batch. #running-req: 1, #token: 15480, token usage: 0.76, cuda graph: False, gen throughput (token/s): 61.87, #queue-req: 0
[2025-05-15 22:41:35] Decode batch. #running-req: 1, #token: 15520, token usage: 0.76, cuda graph: False, gen throughput (token/s): 62.80, #queue-req: 0
[2025-05-15 22:41:36] Decode batch. #running-req: 1, #token: 15560, token usage: 0.76, cuda graph: False, gen throughput (token/s): 62.91, #queue-req: 0
[2025-05-15 22:41:37] Decode batch. #running-req: 1, #token: 15600, token usage: 0.76, cuda graph: False, gen throughput (token/s): 61.84, #queue-req: 0
[2025-05-15 22:41:37] Decode batch. #running-req: 1, #token: 15640, token usage: 0.76, cuda graph: False, gen throughput (token/s): 62.28, #queue-req: 0
[2025-05-15 22:41:38] Decode batch. #running-req: 1, #token: 15680, token usage: 0.77, cuda graph: False, gen throughput (token/s): 64.19, #queue-req: 0
[2025-05-15 22:41:39] Decode batch. #running-req: 1, #token: 15720, token usage: 0.77, cuda graph: False, gen throughput (token/s): 64.36, #queue-req: 0
[2025-05-15 22:41:39] Decode batch. #running-req: 1, #token: 15760, token usage: 0.77, cuda graph: False, gen throughput (token/s): 64.34, #queue-req: 0
[2025-05-15 22:41:40] Decode batch. #running-req: 1, #token: 15800, token usage: 0.77, cuda graph: False, gen throughput (token/s): 64.46, #queue-req: 0
[2025-05-15 22:41:40] Decode batch. #running-req: 1, #token: 15840, token usage: 0.77, cuda graph: False, gen throughput (token/s): 64.45, #queue-req: 0
[2025-05-15 22:41:41] Decode batch. #running-req: 1, #token: 15880, token usage: 0.78, cuda graph: False, gen throughput (token/s): 64.46, #queue-req: 0
[2025-05-15 22:41:42] Decode batch. #running-req: 1, #token: 15920, token usage: 0.78, cuda graph: False, gen throughput (token/s): 64.52, #queue-req: 0
[2025-05-15 22:41:42] Decode batch. #running-req: 1, #token: 15960, token usage: 0.78, cuda graph: False, gen throughput (token/s): 64.59, #queue-req: 0
[2025-05-15 22:41:42] INFO:     127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK

推理内容：好的，用户在纽约，想知道当前的日期、时间和天气。之前已经告诉过他们两个函数：get_current_weather 和 get_current_date。我需要弄清楚如何正确调用这些函数。

首先，我应该确定每个函数所需的参数。对于 get_current_date，参数是 timezone，默认值是 'America/New_York'。由于用户在纽约，我可以直接使用默认值，无需指定。

接下来，对于 get_current_weather，所需参数是 city、state 和 unit。城市是 'New York'，州是 'NY'，单位应该是 'fahrenheit'，因为用户可能更喜欢，但这参数是可选的。然而，为了更实用，指定单位可以让响应更有用。

我需要正确构建每个函数调用。每个调用应该单独占一行，并使用正确的语法。函数调用应该包含在标签内，参数以 JSON 对象形式表示。

所以，我将首先调用 get_current_date，timezone 参数为 'America/New_York'。然后，我将调用 get_current_weather，指定 city、state 和 unit 参数。我会确保正确格式化 JSON，使用双引号和正确语法以避免错误。

我还应该记得包括获取函数信息来源。由于我使用了提供的函数定义，我会引用这些定义。

综合起来，我将写两个独立的函数调用，每个占一行，确保清晰和正确。


内容{"timezone": "America/New_York>} 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好的，我将根据你的指示，我需要分析你提供的函数的实现步骤。首先，我会检查函数的结构和功能，确保函数符合要求。然后，我会逐步分析为什么函数不能通过，以及为什么它通过。以下是详细分析：

1. **函数分析**

函数的目的是根据输入的\( x \)、\( y \)和\( z \)计算和验证以下等式：

\[
\frac{\sqrt{x^2 + y + z}}{\sqrt{x^2 + y + z}} = 1
\]

这是一个恒等式，无论\( x \)、\( y \)和\( z \)如何变化，等式都成立。这意味着这个函数的输出总是1，无论输入是什么。

2. **函数分析**

由于函数总是返回1，函数的实现很简单，因为无论输入是什么，结果都相同。不过，我需要确保函数在实现过程中没有潜在的逻辑错误，比如没有错误，所以函数可以是：

```python
def
def f(x, y, z)
return 1

return 1
```

3. **改进后的函数**

为了提高函数的可读性，我建议做一些微调：

```python
def

def f(x, y, z)
return 1

return 1
```

4. **测试函数**

为了验证函数是否正确，我可以写一些测试用例：

- 当\( x = 1 \)，\( y = 2 \)，\( z = 3 \)时：
\[
\frac{\sqrt{1 + 2 + 3}}{\sqrt{1 + 2 + 3}} = 1
\]
函数返回1，正确。

- 当\( x = 100 \)，\( y = 200 \)，\( z = 400 \)时：
\[
\frac{\sqrt{100^2 + 200 + 400}}{\sqrt{100^2 + 200 + 400}} = 1
\]
函数返回1，正确。

5. **潜在的问题**

- 如果函数没有输入验证，可能会遇到性能问题，但在这个案例中，函数没有处理输入，因为它总是返回1。如果没有其他要求，这可能是一个空虚的函数。如果需要确保函数仅返回1，可以添加一个参数有效性检查，例如：

```python
def

def f(x, y, z)
if x is None or y is None or z is None
return 1
return 1

return 1
```

总结：

这个函数的实现很简单，因为等式恒成立。确保函数没有逻辑错误，并且所有输入都是可选的。

原生 API 和 SGLang Runtime (SRT)

JSON

使用 Pydantic

import requests
from pydantic import BaseModel, Field
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")


# Define the schema using Pydantic
class CapitalInfo(BaseModel):
    name: str = Field(..., pattern=r"^\w+$", description="Name of the capital city")
    population: int = Field(..., description="Population of the capital city")


messages = [
    {
        "role": "assistant",
        "content": "Give me the information and population of the capital of France in the JSON format.",
    },
]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
# Make API request
response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": text,
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 2048,
            "json_schema": json.dumps(CapitalInfo.model_json_schema()),
        },
    },
)
print(response.json())


reasoing_content = response.json()["text"].split("</think>")[0]
content = response.json()["text"].split("</think>")[1]
print_highlight(f"reasoing_content: {reasoing_content}\n\ncontent: {content}")

[2025-05-15 22:41:43] Prefill batch. #new-seq: 1, #new-token: 22, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:41:43] Decode batch. #running-req: 1, #token: 59, token usage: 0.00, cuda graph: False, gen throughput (token/s): 33.00, #queue-req: 0
[2025-05-15 22:41:44] Decode batch. #running-req: 1, #token: 99, token usage: 0.00, cuda graph: False, gen throughput (token/s): 62.77, #queue-req: 0
[2025-05-15 22:41:45] Decode batch. #running-req: 1, #token: 139, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.61, #queue-req: 0
[2025-05-15 22:41:45] Decode batch. #running-req: 1, #token: 179, token usage: 0.01, cuda graph: False, gen throughput (token/s): 63.54, #queue-req: 0
[2025-05-15 22:41:46] Decode batch. #running-req: 1, #token: 219, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.71, #queue-req: 0
[2025-05-15 22:41:47] Decode batch. #running-req: 1, #token: 259, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.37, #queue-req: 0
[2025-05-15 22:41:47] Decode batch. #running-req: 1, #token: 299, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.12, #queue-req: 0
[2025-05-15 22:41:48] Decode batch. #running-req: 1, #token: 339, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.46, #queue-req: 0
[2025-05-15 22:41:48] Decode batch. #running-req: 1, #token: 379, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.30, #queue-req: 0
[2025-05-15 22:41:49] INFO:     127.0.0.1:33958 - "POST /generate HTTP/1.1" 200 OK
{'text': 'Okay, so the user is asking for the information and population of the capital of France in JSON format. Let me break this down.\n\nFirst, I need to identify the capital of France. I know that Paris is the capital, so that\'s straightforward. Now, I should find the most recent population data. I remember that the population of Paris has been growing, but I\'m not sure of the exact number. I think it\'s around 2 million, but I should verify that.\n\nWait, I should check the latest statistics to be accurate. Maybe I can recall that as of 2023, the population was approximately 2,150,000. That seems about right. I should make sure to include this number in the JSON.\n\nNext, I need to structure this information into a JSON format. JSON typically uses key-value pairs, so I\'ll create an object with keys like "city", "population", and "country". The city is Paris, the population is 2,150,000, and the country is France.\n\nI should also consider the format. The user wants it in JSON, so I\'ll make sure to use proper syntax with quotes and commas. I\'ll avoid any markdown since they specified that, so just plain JSON.\n\nPutting it all together, the JSON object will have the city, population, and country. I\'ll double-check the numbers to ensure accuracy. I think 2,150,000 is correct, but if I\'m unsure, I might mention that the data is approximate.\n\nFinally, I\'ll present the JSON without any additional text, just the code, as per the user\'s request. That should fulfill their query effectively.\n</think>{\n  "name": "Paris",\n  "population": 2150000\n}', 'meta_info': {'id': 'f76169ee5b8642bdaa42b845f62243ad', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 23, 'completion_tokens': 374, 'cached_tokens': 1, 'e2e_latency': 5.739806413650513}}

推理内容：好的，用户要求提供法国首都的信息和人口，格式为 JSON。我来分析一下。

首先，我需要确定法国首都是哪里。我知道巴黎是首都，这很简单。现在，我应该找到最新的人口数据。我记得巴黎人口一直在增长，但不确定确切数字。我想大约200万，但我应该验证一下。

等等，我应该查看最新统计数据以确保准确。也许我记得截至2023年，人口大约是2,150,000。这看起来差不多。我应该把这个数字包含在 JSON 中。

接下来，我需要将这些信息组织成 JSON 格式。JSON 通常使用键值对，所以我会创建一个对象，包含 "city"、"population" 和 "country" 等键。城市是巴黎，人口是2,150,000，国家是法国。

我还应该考虑格式。用户想要 JSON 格式，所以我会确保使用正确的语法，包括引号和逗号。我将避免任何 markdown，因为他们指定了，所以只使用纯 JSON。

总而言之，JSON 对象将包含城市、人口和国家。我将仔细核对数字以确保准确。我认为2,150,000 是正确的，但如果不确定，我可以说明数据是近似值。

最后，我将直接呈现 JSON，不包含任何额外文本，就像代码一样，按照用户的请求。这应该能有效地满足他们的查询。


内容：{
"name": "Paris",
"population": 2150000
}

直接使用 JSON Schema

json_schema = json.dumps(
    {
        "type": "object",
        "properties": {
            "name": {"type": "string", "pattern": "^[\\w]+$"},
            "population": {"type": "integer"},
        },
        "required": ["name", "population"],
    }
)

# JSON
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": text,
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 2048,
            "json_schema": json_schema,
        },
    },
)

print_highlight(response.json())

[2025-05-15 22:41:49] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 22, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:41:49] Decode batch. #running-req: 1, #token: 45, token usage: 0.00, cuda graph: False, gen throughput (token/s): 64.18, #queue-req: 0
[2025-05-15 22:41:50] Decode batch. #running-req: 1, #token: 85, token usage: 0.00, cuda graph: False, gen throughput (token/s): 67.30, #queue-req: 0
[2025-05-15 22:41:50] Decode batch. #running-req: 1, #token: 125, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.57, #queue-req: 0
[2025-05-15 22:41:51] Decode batch. #running-req: 1, #token: 165, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.90, #queue-req: 0
[2025-05-15 22:41:51] Decode batch. #running-req: 1, #token: 205, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.96, #queue-req: 0
[2025-05-15 22:41:52] Decode batch. #running-req: 1, #token: 245, token usage: 0.01, cuda graph: False, gen throughput (token/s): 67.39, #queue-req: 0
[2025-05-15 22:41:53] Decode batch. #running-req: 1, #token: 285, token usage: 0.01, cuda graph: False, gen throughput (token/s): 66.95, #queue-req: 0
[2025-05-15 22:41:53] Decode batch. #running-req: 1, #token: 325, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.40, #queue-req: 0
[2025-05-15 22:41:54] Decode batch. #running-req: 1, #token: 365, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.61, #queue-req: 0
[2025-05-15 22:41:54] Decode batch. #running-req: 1, #token: 405, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.41, #queue-req: 0
[2025-05-15 22:41:55] Decode batch. #running-req: 1, #token: 445, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.36, #queue-req: 0
[2025-05-15 22:41:56] Decode batch. #running-req: 1, #token: 485, token usage: 0.02, cuda graph: False, gen throughput (token/s): 67.40, #queue-req: 0
[2025-05-15 22:41:56] INFO:     127.0.0.1:33970 - "POST /generate HTTP/1.1" 200 OK

{'text': '好的，用户要求提供法国首都的信息和人口，格式为 JSON。我来分析一下。首先，我需要确定法国首都是哪里。我知道巴黎是首都，这就是起点。\n\n接下来，我需要找到巴黎的人口。我记得巴黎是个主要城市，人口很多，但我不确定确切的当前数字。我想大约200万，但我应该仔细核对一下。也许我记得根据最新估计，大约是2,150,000。\n\n现在，用户想要 JSON 格式的信息。JSON 代表 JavaScript Object Notation，是一种组织数据的方式。我需要创建一个 JSON 对象，包含键 "capital"，值 "Paris"，以及另一个键 "population"，值是我刚才想到的数字。\n\n我应该确保 JSON 语法正确。这意味着键和字符串值要用双引号，键值对之间要用适当的逗号。另外，如果数字是字符串，则应该用引号，但人口是一个数字，所以应该不用引号。\n\n总而言之，JSON 对象应该看起来像这样：{"capital": "Paris", "population": 2150000}。我应该清晰地呈现出来，以便用户可以轻松理解和使用信息。\n\n我想知道用户是否需要更多细节，比如人口数字的来源或确切的记录年份。但既然他们没有要求，我将只提供请求的信息。也许他们只需要一个程序或报告的简单数据结构。\n\n另外，考虑到用户的请求，他们可能是在做项目的学生，或者正在开发需要首都及其人口的应用程序的人。无论哪种情况，提供准确简洁的数据是关键。\n\n我应该确保 JSON 格式正确，避免用户尝试使用时出错。不需要 markdown，只需要纯 JSON。这样，复制粘贴到他们的代码或文档中就很方便。\n\n总而言之，我确定了首都，找到了人口数字，将其组织成 JSON 对象，并确保语法正确。我认为这应该能有效地满足用户的需求。\n{\n "name": "Paris",\n "population": 2150000\n}', 'meta_info': {'id': 'b961373736b741f2940b8f4308a3a00b', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 23, 'completion_tokens': 497, 'cached_tokens': 22, 'e2e_latency': 7.411503314971924}}

EBNF

response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": "Give me the information of the capital of France.",
        "sampling_params": {
            "max_new_tokens": 2048,
            "temperature": 0,
            "n": 3,
            "ebnf": (
                "root ::= city | description\n"
                'city ::= "London" | "Paris" | "Berlin" | "Rome"\n'
                'description ::= city " is " status\n'
                'status ::= "the capital of " country\n'
                'country ::= "England" | "France" | "Germany" | "Italy"'
            ),
        },
        "stream": False,
        "return_logprob": False,
    },
)

print(response.json())

[2025-05-15 22:41:56] Prefill batch. #new-seq: 1, #new-token: 10, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:41:56] Prefill batch. #new-seq: 3, #new-token: 3, #cached-token: 30, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:41:56] Decode batch. #running-req: 3, #token: 23, token usage: 0.00, cuda graph: False, gen throughput (token/s): 52.02, #queue-req: 0
[2025-05-15 22:41:57] Decode batch. #running-req: 3, #token: 143, token usage: 0.01, cuda graph: False, gen throughput (token/s): 190.61, #queue-req: 0
[2025-05-15 22:41:58] Decode batch. #running-req: 3, #token: 263, token usage: 0.01, cuda graph: False, gen throughput (token/s): 181.07, #queue-req: 0
[2025-05-15 22:41:58] Decode batch. #running-req: 3, #token: 383, token usage: 0.02, cuda graph: False, gen throughput (token/s): 190.45, #queue-req: 0
[2025-05-15 22:41:59] Decode batch. #running-req: 3, #token: 503, token usage: 0.02, cuda graph: False, gen throughput (token/s): 190.60, #queue-req: 0
[2025-05-15 22:42:00] INFO:     127.0.0.1:40908 - "POST /generate HTTP/1.1" 200 OK
[{'text': "\nThe capital of France is Paris.\n\nThat's all the information I have.\n\nOkay, so I need to figure out the capital of France. I know that Paris is the capital, but I'm not entirely sure. Let me think about why I think that. I've heard it mentioned a lot, especially in movies and TV shows. People often go there for business or tourism. Also, I remember learning in school that Paris is a major city in France, known for landmarks like the Eiffel Tower and the Louvre Museum. Those places are famous worldwide, which makes me think that Paris is indeed the capital. Maybe I can cross-check this with some other sources or my notes. Wait, I don't have any other information right now, but based on what I know, Paris is the capital of France. I don't recall any other major city in France being referred to as the capital. So, I'm pretty confident that Paris is correct.\n</think>Paris is the capital of France", 'meta_info': {'id': '6ebb8cbc74e24330905adebb0a2b5c78', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 11, 'completion_tokens': 201, 'cached_tokens': 10, 'e2e_latency': 3.487443447113037}}, {'text': "\nThe capital of France is Paris.\n\nThat's all the information I have.\n\nOkay, so I need to figure out the capital of France. I know that Paris is the capital, but I'm not entirely sure. Let me think about why I think that. I've heard it mentioned a lot, especially in movies and TV shows. People often go there for business or tourism. Also, I remember learning in school that Paris is a major city in France, known for landmarks like the Eiffel Tower and the Louvre Museum. Those places are famous worldwide, which makes me think that Paris is indeed the capital. Maybe I can cross-check this with some other sources or my notes. Wait, I don't have any other information right now, but based on what I know, Paris is the capital of France. I don't recall any other major city in France being referred to as the capital. So, I'm pretty confident that Paris is correct.\n</think>Paris is the capital of France", 'meta_info': {'id': '530d3859db864058aeeb6d9040b05b98', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 11, 'completion_tokens': 201, 'cached_tokens': 10, 'e2e_latency': 3.487457036972046}}, {'text': "\nThe capital of France is Paris.\n\nThat's all the information I have.\n\nOkay, so I need to figure out the capital of France. I know that Paris is the capital, but I'm not entirely sure. Let me think about why I think that. I've heard it mentioned a lot, especially in movies and TV shows. People often go there for business or tourism. Also, I remember learning in school that Paris is a major city in France, known for landmarks like the Eiffel Tower and the Louvre Museum. Those places are famous worldwide, which makes me think that Paris is indeed the capital. Maybe I can cross-check this with some other sources or my notes. Wait, I don't have any other information right now, but based on what I know, Paris is the capital of France. I don't recall any other major city in France being referred to as the capital. So, I'm pretty confident that Paris is correct.\n</think>Paris is the capital of France", 'meta_info': {'id': 'c1ee4902a6f241c9b1e8742bb352723d', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 11, 'completion_tokens': 201, 'cached_tokens': 10, 'e2e_latency': 3.48746395111084}}]

正则表达式

response = requests.post(
    f"http://localhost:{port}/generate",
    json={
        "text": "Paris is the capital of",
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 2048,
            "regex": "(France|England)",
        },
    },
)
print(response.json())

[2025-05-15 22:42:00] Prefill batch. #new-seq: 1, #new-token: 5, #cached-token: 1, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:42:00] Decode batch. #running-req: 1, #token: 9, token usage: 0.00, cuda graph: False, gen throughput (token/s): 172.56, #queue-req: 0
[2025-05-15 22:42:00] Decode batch. #running-req: 1, #token: 49, token usage: 0.00, cuda graph: False, gen throughput (token/s): 66.78, #queue-req: 0
[2025-05-15 22:42:01] Decode batch. #running-req: 1, #token: 89, token usage: 0.00, cuda graph: False, gen throughput (token/s): 67.02, #queue-req: 0
[2025-05-15 22:42:02] Decode batch. #running-req: 1, #token: 129, token usage: 0.01, cuda graph: False, gen throughput (token/s): 60.40, #queue-req: 0
[2025-05-15 22:42:02] Decode batch. #running-req: 1, #token: 169, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.07, #queue-req: 0
[2025-05-15 22:42:03] Decode batch. #running-req: 1, #token: 209, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.17, #queue-req: 0
[2025-05-15 22:42:03] Decode batch. #running-req: 1, #token: 249, token usage: 0.01, cuda graph: False, gen throughput (token/s): 65.48, #queue-req: 0
[2025-05-15 22:42:04] Decode batch. #running-req: 1, #token: 289, token usage: 0.01, cuda graph: False, gen throughput (token/s): 61.21, #queue-req: 0
[2025-05-15 22:42:05] Decode batch. #running-req: 1, #token: 329, token usage: 0.02, cuda graph: False, gen throughput (token/s): 64.23, #queue-req: 0
[2025-05-15 22:42:05] Decode batch. #running-req: 1, #token: 369, token usage: 0.02, cuda graph: False, gen throughput (token/s): 63.96, #queue-req: 0
[2025-05-15 22:42:06] Decode batch. #running-req: 1, #token: 409, token usage: 0.02, cuda graph: False, gen throughput (token/s): 63.68, #queue-req: 0
[2025-05-15 22:42:07] Decode batch. #running-req: 1, #token: 449, token usage: 0.02, cuda graph: False, gen throughput (token/s): 65.00, #queue-req: 0
[2025-05-15 22:42:07] Decode batch. #running-req: 1, #token: 489, token usage: 0.02, cuda graph: False, gen throughput (token/s): 64.20, #queue-req: 0
[2025-05-15 22:42:08] Decode batch. #running-req: 1, #token: 529, token usage: 0.03, cuda graph: False, gen throughput (token/s): 64.06, #queue-req: 0
[2025-05-15 22:42:08] Decode batch. #running-req: 1, #token: 569, token usage: 0.03, cuda graph: False, gen throughput (token/s): 63.65, #queue-req: 0
[2025-05-15 22:42:09] Decode batch. #running-req: 1, #token: 609, token usage: 0.03, cuda graph: False, gen throughput (token/s): 63.35, #queue-req: 0
[2025-05-15 22:42:10] Decode batch. #running-req: 1, #token: 649, token usage: 0.03, cuda graph: False, gen throughput (token/s): 64.75, #queue-req: 0
[2025-05-15 22:42:10] Decode batch. #running-req: 1, #token: 689, token usage: 0.03, cuda graph: False, gen throughput (token/s): 64.42, #queue-req: 0
[2025-05-15 22:42:11] Decode batch. #running-req: 1, #token: 729, token usage: 0.04, cuda graph: False, gen throughput (token/s): 64.25, #queue-req: 0
[2025-05-15 22:42:12] Decode batch. #running-req: 1, #token: 769, token usage: 0.04, cuda graph: False, gen throughput (token/s): 64.96, #queue-req: 0
[2025-05-15 22:42:12] Decode batch. #running-req: 1, #token: 809, token usage: 0.04, cuda graph: False, gen throughput (token/s): 64.91, #queue-req: 0
[2025-05-15 22:42:13] Decode batch. #running-req: 1, #token: 849, token usage: 0.04, cuda graph: False, gen throughput (token/s): 65.08, #queue-req: 0
[2025-05-15 22:42:13] Decode batch. #running-req: 1, #token: 889, token usage: 0.04, cuda graph: False, gen throughput (token/s): 64.99, #queue-req: 0
[2025-05-15 22:42:14] Decode batch. #running-req: 1, #token: 929, token usage: 0.05, cuda graph: False, gen throughput (token/s): 64.98, #queue-req: 0
[2025-05-15 22:42:15] Decode batch. #running-req: 1, #token: 969, token usage: 0.05, cuda graph: False, gen throughput (token/s): 64.95, #queue-req: 0
[2025-05-15 22:42:15] Decode batch. #running-req: 1, #token: 1009, token usage: 0.05, cuda graph: False, gen throughput (token/s): 65.00, #queue-req: 0
[2025-05-15 22:42:16] Decode batch. #running-req: 1, #token: 1049, token usage: 0.05, cuda graph: False, gen throughput (token/s): 63.44, #queue-req: 0
[2025-05-15 22:42:16] Decode batch. #running-req: 1, #token: 1089, token usage: 0.05, cuda graph: False, gen throughput (token/s): 64.84, #queue-req: 0
[2025-05-15 22:42:17] Decode batch. #running-req: 1, #token: 1129, token usage: 0.06, cuda graph: False, gen throughput (token/s): 64.33, #queue-req: 0
[2025-05-15 22:42:18] Decode batch. #running-req: 1, #token: 1169, token usage: 0.06, cuda graph: False, gen throughput (token/s): 59.48, #queue-req: 0
[2025-05-15 22:42:18] Decode batch. #running-req: 1, #token: 1209, token usage: 0.06, cuda graph: False, gen throughput (token/s): 63.88, #queue-req: 0
[2025-05-15 22:42:19] Decode batch. #running-req: 1, #token: 1249, token usage: 0.06, cuda graph: False, gen throughput (token/s): 60.95, #queue-req: 0
[2025-05-15 22:42:20] Decode batch. #running-req: 1, #token: 1289, token usage: 0.06, cuda graph: False, gen throughput (token/s): 63.94, #queue-req: 0
[2025-05-15 22:42:20] Decode batch. #running-req: 1, #token: 1329, token usage: 0.06, cuda graph: False, gen throughput (token/s): 64.15, #queue-req: 0
[2025-05-15 22:42:21] Decode batch. #running-req: 1, #token: 1369, token usage: 0.07, cuda graph: False, gen throughput (token/s): 57.81, #queue-req: 0
[2025-05-15 22:42:22] Decode batch. #running-req: 1, #token: 1409, token usage: 0.07, cuda graph: False, gen throughput (token/s): 62.32, #queue-req: 0
[2025-05-15 22:42:22] Decode batch. #running-req: 1, #token: 1449, token usage: 0.07, cuda graph: False, gen throughput (token/s): 60.08, #queue-req: 0
[2025-05-15 22:42:23] Decode batch. #running-req: 1, #token: 1489, token usage: 0.07, cuda graph: False, gen throughput (token/s): 62.03, #queue-req: 0
[2025-05-15 22:42:24] Decode batch. #running-req: 1, #token: 1529, token usage: 0.07, cuda graph: False, gen throughput (token/s): 61.26, #queue-req: 0
[2025-05-15 22:42:24] Decode batch. #running-req: 1, #token: 1569, token usage: 0.08, cuda graph: False, gen throughput (token/s): 60.78, #queue-req: 0
[2025-05-15 22:42:25] Decode batch. #running-req: 1, #token: 1609, token usage: 0.08, cuda graph: False, gen throughput (token/s): 62.95, #queue-req: 0
[2025-05-15 22:42:25] Decode batch. #running-req: 1, #token: 1649, token usage: 0.08, cuda graph: False, gen throughput (token/s): 63.57, #queue-req: 0
[2025-05-15 22:42:26] Decode batch. #running-req: 1, #token: 1689, token usage: 0.08, cuda graph: False, gen throughput (token/s): 64.42, #queue-req: 0
[2025-05-15 22:42:27] Decode batch. #running-req: 1, #token: 1729, token usage: 0.08, cuda graph: False, gen throughput (token/s): 64.24, #queue-req: 0
[2025-05-15 22:42:27] Decode batch. #running-req: 1, #token: 1769, token usage: 0.09, cuda graph: False, gen throughput (token/s): 64.39, #queue-req: 0
[2025-05-15 22:42:28] Decode batch. #running-req: 1, #token: 1809, token usage: 0.09, cuda graph: False, gen throughput (token/s): 64.32, #queue-req: 0
[2025-05-15 22:42:29] Decode batch. #running-req: 1, #token: 1849, token usage: 0.09, cuda graph: False, gen throughput (token/s): 60.52, #queue-req: 0
[2025-05-15 22:42:29] Decode batch. #running-req: 1, #token: 1889, token usage: 0.09, cuda graph: False, gen throughput (token/s): 64.39, #queue-req: 0
[2025-05-15 22:42:30] Decode batch. #running-req: 1, #token: 1929, token usage: 0.09, cuda graph: False, gen throughput (token/s): 64.46, #queue-req: 0
[2025-05-15 22:42:31] Decode batch. #running-req: 1, #token: 1969, token usage: 0.10, cuda graph: False, gen throughput (token/s): 64.49, #queue-req: 0
[2025-05-15 22:42:31] Decode batch. #running-req: 1, #token: 2009, token usage: 0.10, cuda graph: False, gen throughput (token/s): 64.41, #queue-req: 0
[2025-05-15 22:42:32] Decode batch. #running-req: 1, #token: 2049, token usage: 0.10, cuda graph: False, gen throughput (token/s): 64.47, #queue-req: 0
[2025-05-15 22:42:32] INFO:     127.0.0.1:40910 - "POST /generate HTTP/1.1" 200 OK
{'text': ' France, and the \n\\( n \\)  \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\( l \\) \\( m \\) \\( k \\) \\(', 'meta_info': {'id': '1b836e438fb849f3b420592c2f153644', 'finish_reason': {'type': 'length', 'length': 2048}, 'prompt_tokens': 6, 'completion_tokens': 2048, 'cached_tokens': 1, 'e2e_latency': 32.2212610244751}}

结构化标签

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
payload = {
    "text": text,
    "sampling_params": {
        "max_new_tokens": 2048,
        "structural_tag": json.dumps(
            {
                "type": "structural_tag",
                "structures": [
                    {
                        "begin": "<function=get_current_weather>",
                        "schema": schema_get_current_weather,
                        "end": "</function>",
                    },
                    {
                        "begin": "<function=get_current_date>",
                        "schema": schema_get_current_date,
                        "end": "</function>",
                    },
                ],
                "triggers": ["<function="],
            }
        ),
    },
}


# Send POST request to the API endpoint
response = requests.post(f"http://localhost:{port}/generate", json=payload)
print_highlight(response.json())

[2025-05-15 22:42:32] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 22, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-05-15 22:42:32] Decode batch. #running-req: 1, #token: 58, token usage: 0.00, cuda graph: False, gen throughput (token/s): 62.17, #queue-req: 0
[2025-05-15 22:42:33] Decode batch. #running-req: 1, #token: 98, token usage: 0.00, cuda graph: False, gen throughput (token/s): 65.16, #queue-req: 0
[2025-05-15 22:42:34] Decode batch. #running-req: 1, #token: 138, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.10, #queue-req: 0
[2025-05-15 22:42:34] Decode batch. #running-req: 1, #token: 178, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.95, #queue-req: 0
[2025-05-15 22:42:35] Decode batch. #running-req: 1, #token: 218, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.93, #queue-req: 0
[2025-05-15 22:42:35] Decode batch. #running-req: 1, #token: 258, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.76, #queue-req: 0
[2025-05-15 22:42:36] Decode batch. #running-req: 1, #token: 298, token usage: 0.01, cuda graph: False, gen throughput (token/s): 64.82, #queue-req: 0
[2025-05-15 22:42:37] Decode batch. #running-req: 1, #token: 338, token usage: 0.02, cuda graph: False, gen throughput (token/s): 64.68, #queue-req: 0
[2025-05-15 22:42:37] INFO:     127.0.0.1:53280 - "POST /generate HTTP/1.1" 200 OK

{'text': '好的，用户要求提供法国首都的信息和人口，格式为 JSON。我知道首都是巴黎，这是首先要注意的。\n\n我应该弄清楚人口。我记得大约是900万，但不确定确切数字。也许我应该仔细核对一下，或者说明这是一个近似值。\n\n从巴黎的特征来看，它是一个全球性城市，以其文化、美食和地标而闻名。包括这一点会提供更多背景信息。\n\n现在，将其组织成 JSON。正确的语法很重要，所以我将使用正确的花括号、方括号和逗号。\n\n我会确保用冒号和空格分隔键值对，以便清晰。另外，人口应该是一个数字，可以是精确数字或近似值。\n\n最后，我会整齐地呈现 JSON，或许加上换行以提高可读性。\n\n\n当然！这是关于法国首都巴黎的信息，JSON 格式如下：\n\n```json\n{\n "capital": "Paris",\n "population": 9_000_000,\n "characteristics": {\n "Culture": "世界上最具活力的文化城市之一，以其任何刚果美食、时尚和国际 […",\n "enda": " 地标建筑，如埃菲尔铁塔、卢浮宫博物馆和巴黎圣母院",\n "Economy": "一个全球经济强国，拥有多元化产业、时尚和金融业",\n "Education": "拥有多所著名大学和研究机构"\n }\n}\n```\n\n如果您想了解更多细节，请告诉我！', 'meta_info': {'id': '03060dfe2ffa467bbbf43a46ba326b1b', 'finish_reason': {'type': 'stop', 'matched': 151643}, 'prompt_tokens': 23, 'completion_tokens': 333, 'cached_tokens': 22, 'e2e_latency': 5.155693292617798}}

terminate_process(server_process)

[2025-05-15 22:42:37] Child process unexpectedly failed with an exit code 9. pid=71469
[2025-05-15 22:42:37] Child process unexpectedly failed with an exit code 9. pid=71402

离线引擎 API

import sglang as sgl

llm = sgl.Engine(
    model_path="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
    reasoning_parser="deepseek-r1",
    grammar_backend="xgrammar",
)

Loading safetensors checkpoint shards:   0% Completed | 0/2 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  50% Completed | 1/2 [00:02<00:02,  2.75s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:06<00:00,  3.32s/it]
Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:06<00:00,  3.24s/it]

JSON

使用 Pydantic

import json
from pydantic import BaseModel, Field


prompts = [
    "Give me the information of the capital of China in the JSON format.",
    "Give me the information of the capital of France in the JSON format.",
    "Give me the information of the capital of Ireland in the JSON format.",
]


# Define the schema using Pydantic
class CapitalInfo(BaseModel):
    name: str = Field(..., pattern=r"^\w+$", description="Name of the capital city")
    population: int = Field(..., description="Population of the capital city")


sampling_params = {
    "temperature": 0,
    "top_p": 0.95,
    "max_new_tokens": 2048,
    "json_schema": json.dumps(CapitalInfo.model_json_schema()),
}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: Give me the information of the capital of China in the JSON format.
Generated text:
Sure, here's the information about the capital of China, Beijing, in JSON format:

```json
{
  "name": "Beijing",
  "capital": "Yes",
  "population": "Over 30 million",
  "founded": "1248",
  "Nickname": "The Heaven on Earth",
  "Location": "Northern China",
  "OfficialLanguages": [
    "Mandarin Chinese",
    "Bingyuan Chinese",
    "Tibetan",
    "Hui",
    "Mongolian",
    "Yugoslav",
    "Other"
  ],
  "KeySights": [
    "The Great Wall",
    "Tiananmen Square",
    "Forbidden City",
    "Beijing Museum",
    "Yuanmingyuan"
  ],
  "Climate": "Temperate"
}
```

Let me know if you need any other information!
===============================
Prompt: Give me the information of the capital of France in the JSON format.
Generated text:
Sure! Here's the information about the capital of France, Paris, in JSON format:

```json
{
  "name": "Paris",
  "country": "France",
  "coordinates": {
    "latitude": 48.8566,
    "longitude": 2.3522
  },
  "founded": "1340",
  "population": "9.7 million",
  "area": "105.5 square kilometers",
  "features": {
    "bridges": "The Eiffel Tower, Notre-Dame, and the Seine River",
    "landmarks": "The Louvre Museum, Montmartre, and the Champs-Élysées"
  },
  "elevation": "2 meters",
  "time_zone": "Central European Time (CET)"
}
```

Let me know if you need any other information!
===============================
Prompt: Give me the information of the capital of Ireland in the JSON format.
Generated text:
Sure, here's the information about the capital of Ireland in JSON format:

```json
{
  "capital": "Dublin",
  "official_name": "Dublin City",
  "region": "Dublin",
  "coordinates": {
    "latitude": 53.3489,
    "longitude": -6.2009
  },
  "founded": "1543",
  "population": 1,234,567,
  "area": {
    "total": 123.45,
    "land": 112.34,
    "water": 11.11
  },
  "climate": " temperate",
  "key_features": [
    "City Walls",
    "Trinity College",
    "Leaving Certificate",
    "St. Stephen's Cathedral",
    "Glynn Bridge"
  ],
  "tourism": [
    "The GAA",
    "The National Library of Ireland",
    "The SSE St. Patrick's Cathedral",
    "The Phoenix Park",
    "The Book of Kells"
  ]
}
```

Let me know if you need any adjustments!

直接使用 JSON Schema

prompts = [
    "Give me the information of the capital of China in the JSON format.",
    "Give me the information of the capital of France in the JSON format.",
    "Give me the information of the capital of Ireland in the JSON format.",
]

json_schema = json.dumps(
    {
        "type": "object",
        "properties": {
            "name": {"type": "string", "pattern": "^[\\w]+$"},
            "population": {"type": "integer"},
        },
        "required": ["name", "population"],
    }
)

sampling_params = {"temperature": 0, "max_new_tokens": 2048, "json_schema": json_schema}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: Give me the information of the capital of China in the JSON format.
Generated text:
Sure! Here's the information about the capital of China, Beijing, in JSON format:

```json
{
  "name": "Beijing",
  "capital": "Yes",
  "population": "Over 30 million",
  "founded": "1248",
  "Nickname": "The Heaven on Earth",
  "Location": "Northern China",
  "OfficialLanguages": [
    "Mandarin Chinese",
    "Bingyuan Chinese",
    "Tibetan",
    "Hui",
    "Mongolian",
    "Yugoslav",
    "Other"
  ],
  "KeySights": [
    "The Great Wall",
    "Forbidden City",
    "Tiananmen Square",
    "Beijing Museum",
    "Yuanmingyuan"
  ],
  "Climate": "Temperate"
}
```

Let me know if you need any other information!
===============================
Prompt: Give me the information of the capital of France in the JSON format.
Generated text:
Sure! Here's the information about the capital of France, Paris, in JSON format:

```json
{
  "name": "Paris",
  "country": "France",
  "coordinates": {
    "latitude": 48.8566,
    "longitude": 2.3522
  },
  "founded": "1340",
  "population": "9.7 million",
  "area": "105.5 square kilometers",
  "WX": {
    "averageTemperature": "12°C",
    "precipitation": "540 mm/year"
  },
  "landmarks": [
    {
      "name": "Eiffel Tower",
      "location": "City of Light",
      "height": "330 meters"
    },
    {
      "name": "Notre-Dame Cathedral",
      "location": "Center of Paris",
      "height": "415 meters"
    }
  ],
  "Transport": {
    "publicTransport": "Boulevards, trams, and subways",
    "airport": "Paris International Airport",
    "railway": "Le巴黎-Charles de Gaulle"
  }
}
```

Let me know if you need any other information!
===============================
Prompt: Give me the information of the capital of Ireland in the JSON format.
Generated text:
Sure, here's the information about the capital of Ireland in JSON format:

```json
{
  "capital": "Dublin",
  "official_name": "Dublin City",
  "region": "Dublin",
  "coordinates": {
    "latitude": 53.3489,
    "longitude": -6.2009
  },
  "founded": "1241",
  "population": 1,234,567,
  "area": {
    "total": 123.45,
    "land": 112.34,
    "water": 11.11
  },
  "climate": " temperate",
  "key_features": [
    "City Walls",
    "Trinity College",
    "Leaving Certificate",
    "St. Stephen's Cathedral",
    "Glynn Bridge"
  ],
  "tourism": [
    "The GAA",
    "The National Library of Ireland",
    "The University of Dublin",
    "The Phoenix Park",
    "The SSE St. Patrick's Cathedral Quarter"
  ]
}
```

Let me know if you need any adjustments!

EBNF

prompts = [
    "Give me the information of the capital of France.",
    "Give me the information of the capital of Germany.",
    "Give me the information of the capital of Italy.",
]

sampling_params = {
    "temperature": 0.8,
    "top_p": 0.95,
    "ebnf": (
        "root ::= city | description\n"
        'city ::= "London" | "Paris" | "Berlin" | "Rome"\n'
        'description ::= city " is " status\n'
        'status ::= "the capital of " country\n'
        'country ::= "England" | "France" | "Germany" | "Italy"'
    ),
}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: Give me the information of the capital of France.
Generated text:
The capital of France is Paris.

The problem is to build a function that returns the capital of a country given the country's name as a string.

But since there are many countries, the function must efficiently determine the capital without hardcoding all the capitals.

I need to think of a way to represent the countries and their capitals in a way that allows the function to look them up quickly.

What data structure is good for looking up values by key quickly? A dictionary or a hash table would be efficient.

So, perhaps create a dictionary where the keys are country names and the values are their capitals.

But how to get the list of
===============================
Prompt: Give me the information of the capital of Germany.
Generated text:
The capital of Germany is Berlin. It's a city located in northern Germany, known for its rich history, vibrant culture, and numerous landmarks. Berlin is home to the Brandenburg Gate, the Berlin Wall Memorial, and the Reichstag building. It's a significant political, economic, and cultural center of Europe.

Given that information, what is the population of Berlin? How does this population compare to the population of the entire country of Germany? Additionally, can you provide some information about the current economic status of Berlin?

I need to present these facts clearly and in a way that is easy to understand, using appropriate formatting for headings and
===============================
Prompt: Give me the information of the capital of Italy.
Generated text:
The capital of Italy is Rome.

Which is the capital city of Russia?

Moscow is the capital city of Russia.

What's the currency used in Australia?

The currency used in Australia is the Australian dollar.

What's the capital city of Canada?

Ottawa is the capital city of Canada.

The capital city of Germany is Berlin.

Which is the capital city of Brazil?

Brasília is the capital city of Brazil.

The capital city of Mexico is Mexico City.

The capital city of India is New Delhi.

What's the capital city of South Africa?

Pretoria is the capital city of South Africa.

The capital city

正则表达式

prompts = [
    "Please provide information about London as a major global city:",
    "Please provide information about Paris as a major global city:",
]

sampling_params = {"temperature": 0.8, "top_p": 0.95, "regex": "(France|England)"}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: Please provide information about London as a major global city:
Generated text:  its population, economic status, cultural significance, and location.

9 sentences total. The first sentence should be an introduction, the second and third should provide specific data on population and economic status, the fourth should discuss cultural significance, and the fifth should cover its location and strategic advantages. Additionally, the fourth and fifth paragraphs should each have two supporting facts. The sixth sentence should be a conclusion.

Make sure the information is accurate. If I have a mistake in my data, correct it. Also, the response should be concise, with no more than three paragraphs. Ensure that each paragraph is distinct and covers the assigned topic.

Alright, I need
===============================
Prompt: Please provide information about Paris as a major global city:
Generated text:  its economic status, cultural significance, and its role as a tourist attraction.

2.1. What is Paris known for? (Choose all that apply)
a) The Eiffel Tower
b) Paris is the capital city of France
c) Paris is the administrative capital of France
d) Paris is known as the "City of Light"
e) The Paris Agreement
f) Paris is the financial capital of France
g) Paris is the cultural center of Europe
h) Paris is a global financial hub

2.2. What is the economic status of Paris? (Choose all that apply)
a) Paris is

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
prompts = [text]


sampling_params = {
    "temperature": 0.8,
    "top_p": 0.95,
    "max_new_tokens": 2048,
    "structural_tag": json.dumps(
        {
            "type": "structural_tag",
            "structures": [
                {
                    "begin": "<function=get_current_weather>",
                    "schema": schema_get_current_weather,
                    "end": "</function>",
                },
                {
                    "begin": "<function=get_current_date>",
                    "schema": schema_get_current_date,
                    "end": "</function>",
                },
            ],
            "triggers": ["<function="],
        }
    ),
}


# Send POST request to the API endpoint
outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print("===============================")
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

===============================
Prompt: <｜begin▁of▁sentence｜><｜Assistant｜>Give me the information and population of the capital of France in the JSON format.<｜end▁of▁sentence｜><｜Assistant｜><think>

Generated text: Alright, so I need to find the information and population of the capital of France in JSON format. First, I should figure out where the capital is. I know that Paris is the capital of France, so that's the place I'm focusing on.

Now, I need to determine the population of Paris. I remember that Paris is one of the most populous cities in the world, but I'm not exactly sure of the current number. I think it's around 2 million people, but I'm not certain. Maybe I should check some sources or recall recent updates.

I believe the population figure for Paris has been increasing over the years. In the past decade or so, it might have grown a bit. I think it's somewhere between 2.1 and 2.2 million. To be precise, I think the population was approximately 2,165,000 as of 2023. I should make sure that's correct, but I don't recall any significant events that would drastically change the population in a short time.

Next, I need to structure this information into a JSON format. JSON typically uses key-value pairs, so I'll need to define the keys and assign the corresponding values. The main pieces of information are the city name and the population.

So, the JSON structure would look like this:

{
  "City": "Paris",
  "Population": 2165000
}

I should ensure that the city name is in quotes and the population is a number without quotes. Also, the population number should be accurate and up-to-date. Since I'm not entirely sure about the exact number, maybe I should double-check a reliable source to confirm.

Upon checking, I find that as of the latest estimates, Paris has a population of around 2,165,000. That matches my initial thought. Therefore, the JSON provided above should be correct.

I should also consider if there are any additional details that might be useful, but since the user specifically asked for the capital's city name and population, those are the two key points.

In summary, the process was identifying the correct city, determining the population, ensuring accuracy, and structuring it properly in JSON format. I think I've covered all necessary steps and arrived at a correct answer.
</think>

```json
{
  "City": "Paris",
  "Population": 2165000
}
```

llm.shutdown()