Repository: modelscope/evalscope
Issue: 参数无效情况
Root cause: Configuration Error

## 自查清单

在提交 issue 之前，请确保您已完成以下步骤:
- [x] 我已仔细阅读了[相关使用说明文档](https://evalscope.readthedocs.io/zh-cn/latest/get_started/parameters.html)
- [x] 我已查看了[常见问题解答](https://evalscope.readthedocs.io/zh-cn/latest/get_started/faq.html)
- [x] 我已搜索并查看了现有的 issues，确认这不是一个重复的问题

## 问题描述

我在客户端代码传参改变模型输出token数，但是没有效果，请问怎么解决？

vllm.log文件中看到的max_token数还是默认值，不是客户端参数


## EvalScope 版本（必填）
v0.17.1

## 使用的工具
- [x] Native / 原生框架
- [ ] Opencompass backend
- [ ] VLMEvalKit backend
- [ ] RAGEval backend
- [ ] Perf / 模型推理压测工具
- [ ] Arena / 竞技场模式

## 执行的代码或指令
```
VLLM_USE_MODELSCOPE=True CUDA_VISIBLE_DEVICES=0,1,2,3 python -m vllm.entrypoints.openai.api_server --model xxxxxx --served-model-name DeepSeek-R1-Distill-Qwen-1.5B --trust_remote_code --port 8801 --tensor-parallel-size 4  > ./vllm.log 2>&1 &
```
```
from evalscope import TaskConfig, run_task
from evalscope.constants import EvalType

system_prompt = "You are a helpful assistant. To answer the user's question, you first think about the reasoning process and then provide the user with the answer. The reasoning process and answer are enclosed within <think> and <answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here<answer>."

task_cfg = TaskConfig(
    model='DeepSeek-R1-Distill-Qwen-1.5B',
    api_url=f'http://127.0.0.1:8801/v1',
    api_key='EMPTY',
    eval_type=EvalType.SERVICE,
    datasets=[
        'aime25'
    ],
    dataset_args={
        "aime25": {
            "system_prompt": system_prompt
        }
    },
    eval_batch_size=15,
    generation_config={
        "max_length": 6000,
        "max_tokens": 10000,
        "max_new_tokens": 4000,
        "temperature": 0.6,
        "top_p": 0.95
    },
    stream=True,
)
run_task(task_cfg=task_cfg)
```



## 错误日志

vllm.log:
```
INFO 09-22 23:13:49 [logger.py:43] Received request chatcmpl-ae1e00ba12574181838fbfe6adf16ab5: prompt: "
system\nYou are a helpful assistant. To answer the user's question, you first think about the reasoning process and then provide the user with the answer. The reasoning process and answer are enclosed within <think> and <answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here<answer>.
\n
user\nThere are exactly three positive real numbers $ k $ such that the function\n$ f(x) = \\frac{(x - 18)(x - 72)(x - 98)(x - k)}{x} $\ndefined over the positive real numbers achieves its minimum value at exactly two positive real numbers $ x $. Find the sum of these three values of $ k $.\nPlease reason step by step, and put your final answer within \\boxed{}.
\n
assistant\n", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.6, top_p=0.95, top_k=0, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=2048, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, prompt_embeds shape: None, lora_request: None, prompt_adapter_request: None.
```




升级evalscope到最新版本（1.0.2）再尝试一下

> 感谢你的反馈！我们将关闭此问题。如果您有任何疑问，请随时重新打开它。如果EvalScope对您有所帮助，欢迎给我们点个STAR以示支持，谢谢！