clean up Dockerfile

Dockerfile

@@ -1,10 +1,6 @@
 FROM modelhubxc-4pd.tencentcloudcr.com/xc_agent_platform/python:3.11-slim
 
-ENV PYTHONUNBUFFERED=1 \
-    
-    CONTEST_API_TOKEN="ef1ef82f3c9efee413d602345fbe224d" \
-    CONTRIBUTORS="zhoushasha" \
-    GPU_TYPE="Cambricon_mlu-370-x8"
+ENV PYTHONUNBUFFERED=1
 
 WORKDIR /app
 

main.py

@@ -9,7 +9,6 @@ import json
 import os
 import signal
 import threading
-import traceback
 from datetime import datetime
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from typing import List, Tuple
@@ -20,16 +19,15 @@ import requests
 #  配置（全部从环境变量读取，不硬编码敏感信息）
 # ══════════════════════════════════════════════════════════
 BASE_URL           = os.environ.get("BASE_URL", "https://modelhub.org.cn")
-LOGIN_ENDPOINT     = "/adminApi/user/login"
 SUBMIT_ENDPOINT    = "/adminApi/async/task/create-contest-task"
 
-USER_ACCOUNT       = os.environ["USER_ACCOUNT"]        # 必填
-USER_PASSWORD      = os.environ["USER_PASSWORD"]        # 必填
-CONTEST_API_TOKEN  = os.environ["CONTEST_API_TOKEN"]    # 必填
-STRATEGY_ID        = os.environ.get("STRATEGY_ID", "")  # 平台注入
-CONTRIBUTORS       = os.environ.get("CONTRIBUTORS", USER_ACCOUNT)
-GPU_TYPE           = os.environ.get("GPU_TYPE", "Cambricon_mlu-370-x8")
-TASK_TYPE          = os.environ.get("TASK_TYPE", "text-generation")
+# 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
+AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODE4NTE0NzcsImlhdCI6MTc4MTI0NjY3N30.p3uvCpG50aLNifNVVXxvzmWJahbLM5K1671FVCtj8E8"
+CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
+CONTRIBUTORS      = "zhoushasha"
+GPU_TYPE          = "Cambricon_mlu-370-x8"
+TASK_TYPE         = "text-generation"
+STRATEGY_ID       = os.environ.get("STRATEGY_ID", "")  # 平台自动注入，无需修改
 
 HTTP_HOST = "0.0.0.0"
 HTTP_PORT = 8080
@@ -38,26 +36,34 @@ HTTP_PORT = 8080
 #  模型列表
 # ══════════════════════════════════════════════════════════
 ALL_MODEL_IDS = [
-    "AI-ModelScope/gemma-2b",
-    "AI-ModelScope/falcon-mamba-7b",
-    "katanemo/deepseek-2",
-    "OpenBMB/MiniCPM4-0.5B",
-    "NousResearch/Meta-Llama-3-8B-Instruct",
-    "MediaTek-Research/Breeze-7B-Instruct-v1_0",
-    "QLUNLP/BianCang-Qwen2.5-7B-Instruct",
-    "OpenBMB/MiniCPM4-Survey",
-    "OpenBMB/MiniCPM4-8B",
-    "PaddlePaddle/ERNIE-4.5-0.3B-PT",
-    "LLM-Research/Llama-Guard-3-8B",
-    "OpenBMB/MiniCPM-2B-dpo-fp16",
-    "OpenBMB/MiniCPM4.1-8B",
-    "Cylingo/Xinyuan-LLM-14B-0428",
-    "Fengshenbang/Ziya-LLaMA-13B-v1",
-    "baichuan-inc/Baichuan2-13B-Chat",
-    "LLM-Research/gemma-2-9b-it",
-    "Qwen/CodeQwen1.5-7B-Chat",
-    "OpenBMB/cpm-bee-10b",
-    "OpenBMB/MiniCPM3-4B",
+    "NovaSky-AI/Sky-T1-7B",
+    "prithivMLmods/TESS-QwenRe-Fact-0.5B",
+    "Magpie-Align/Llama-3.1-8B-Magpie-Align-v0.1",
+    "Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.1",
+    "Magpie-Align/Llama-3.1-8B-Magpie-Align-SFT-v0.2",
+    "LLM-Research/layerskip-llama2-7B",
+    "PowerInfer/SmallThinker-3B-Preview",
+    "HuggingFaceTB/SmolLM2-360M",
+    "ModelCloud.AI/Llama3.2-1B-Instruct",
+    "NousResearch/DeepHermes-3-Llama-3-8B-Preview",
+    "PAI/DistilQwen2.5-DS3-0324-14B",
+    "OpenBMB/MiniCPM-1B-sft-bf16",
+    "OpenBMB/MiniCPM4-8B-marlin-vLLM",
+    "OpenBMB/MiniCPM-2B-sft-fp32",
+    "unsloth/gemma-3-1b-it",
+    "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
+    "Magpie-Align/Llama-3-8B-WildChat",
+    "allenai/tulu-2-7b",
+    "argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
+    "Fengshenbang/Ziya2-13B-Chat",
+    "AI-ModelScope/starcoder2-15b",
+    "AI-ModelScope/phi-2",
+    "KoboldAI/OPT-13B-Erebus",
+    "NousResearch/OLMo-Bitnet-1B",
+    "AI-ModelScope/merlinite-7b",
+    "IndexTeam/Index-1.9B-Chat",
+    "AI-ModelScope/gpt2",
+    "togethercomputer/GPT-JT-6B-v1",
 ]
 
 # ══════════════════════════════════════════════════════════
@@ -110,21 +116,6 @@ def _run_http():
 # ══════════════════════════════════════════════════════════
 #  业务逻辑
 # ══════════════════════════════════════════════════════════
-def _login() -> str:
-    headers = {"Content-Type": "application/json"}
-    resp = requests.post(
-        BASE_URL + LOGIN_ENDPOINT,
-        headers=headers,
-        json={"userAccount": USER_ACCOUNT, "userPassword": USER_PASSWORD},
-        timeout=30,
-    )
-    data = resp.json()
-    if data.get("code") != 0:
-        raise RuntimeError(f"登录失败: {data.get('message')}")
-    print("[worker] 登录成功", flush=True)
-    return data["data"]["token"]
-
-
 def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
     headers = {
         "Content-Type": "application/json",
@@ -192,12 +183,8 @@ def _run_worker():
     _state["phase"] = "submitting"
 
     successful: List[Tuple[str, str]] = []
-    try:
-        token = _login()
-    except Exception:
-        traceback.print_exc()
-        _state["phase"] = "error"
-        return
+    token = AUTH_TOKEN
+    print("[worker] 使用预设 Token，跳过登录", flush=True)
 
     for model_id in ALL_MODEL_IDS:
         if _shutdown.is_set():