submit round-24 filter results on zhoushasha: MetaX_c-500(63)/Kunlunxin_p-800(1)/Cambricon_mlu-370-x8(1)/Biren_166m(95) + ppu_zw_810e(57), 217 models total

Also add a hygon_k100-ai config branch and HYGON_MODELS list (kept available but
excluded from GPU_JOBS per request). Refresh the expired AUTH_TOKEN.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

.gitignore

@@ -0,0 +1,2 @@
+.DS_Store
+__pycache__/

Dockerfile

@@ -1,6 +1,7 @@
 FROM modelhubxc-4pd.tencentcloudcr.com/xc_agent_platform/python:3.11-slim
 
-ENV PYTHONUNBUFFERED=1
+ENV PYTHONUNBUFFERED=1 
+    
 
 WORKDIR /app
 

README.md

@@ -1,5 +1,29 @@
 # xc_validation_strategy
 
-信创自动化模型适配平台 — 验证策略服务
+批量向 ModelHub XC 平台提交模型验证任务的策略服务，之后保持 HTTP 服务存活供平台探活。
 
-从 HuggingFace 周期性抓取新模型，自动完成同步、下载、提交验证任务的全流程，常驻运行在 xc_agent_platform 上。
+## 功能
+
+- 自动登录 ModelHub 获取 Token
+- 批量提交模型验证任务（vLLM 框架，Cambricon MLU-370-x8）
+- 提交结果写入 `submitted_validation_tasks.txt`
+- 暴露 `/health` 和 `/status` 接口满足平台运行时契约
+
+## 项目结构
+
+```
+.
+├── main.py            # 主入口：HTTP 服务 + 提交逻辑
+├── Dockerfile         # 平台镜像构建配置
+├── requirements.txt   # Python 依赖
+└── submitted_validation_tasks.txt   # 运行后自动生成，记录提交结果
+```
+
+## 平台契约说明
+
+本项目满足平台对策略镜像的全部必要约束：
+
+- Dockerfile 位于仓库根目录，基于官方轻量基础镜像
+- 暴露 8080 端口并实现 `GET /health`
+- 通过环境变量 `STRATEGY_ID` 获取策略 ID
+- 正确处理 `SIGTERM` 信号，支持优雅停机

main.py

@@ -1,15 +1,25 @@
 """
 xc_validation_strategy — 主入口
 
-启动后执行一次模型验证任务批量提交，之后保持 HTTP 服务存活。
+启动后针对 GPU_JOBS 中配置的 GPU 卡分别批量提交各自筛选出的模型验证任务
-同时暴露 /health（K8s 探活）和 /status（运行状态）。
+（当前仅提交 ppu_zw_810e，其余 4 张卡 Biren_166m/Cambricon_mlu-370-x8/MetaX_c-500/
+Kunlunxin_p-800 的 config_content 模板和模型列表仍保留在代码中，未列入本次 GPU_JOBS）
+（/adminApi/async/task/create-contest-task，
+Bearer Token 认证），之后保持 HTTP 服务存活。
+
+账号额度自动重试：如果某个模型提交时命中"当前等待中或运行中的异步模型验证
+任务数量已达上限"（账号额度已满），不算永久失败，会被留到下一轮；额度耗尽后
+本进程会原地等待 30 分钟，再自动重试所有因额度问题未提交成功的模型，如此循环，
+直至全部提交成功或进程被平台关闭——不需要重新部署新策略，循环逻辑在本进程内完成。
+非额度原因的失败（如模型已在验证中等）不会重试。
+
+同时暴露 /health（K8s 探活）和 /status（运行状态，含当前轮次/待重试数/下次重试时间）。
 """
 
 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
@@ -20,57 +30,291 @@ 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
+# 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
-USER_PASSWORD      = os.environ["USER_PASSWORD"]        # 必填
+AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3OTAwNjkxMjAsImlhdCI6MTc4OTQ2NDMyMH0.KzJac6ddaZdtLvjD6ZnoK1PNEKFoXdyDn9Hh4FxU9ic"
-CONTEST_API_TOKEN  = os.environ["CONTEST_API_TOKEN"]    # 必填
+CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
-STRATEGY_ID        = os.environ.get("STRATEGY_ID", "")  # 平台注入
+CONTRIBUTORS      = "zhoushasha"
-CONTRIBUTORS       = os.environ.get("CONTRIBUTORS", USER_ACCOUNT)
+TASK_TYPE         = "text-generation"
-GPU_TYPE           = os.environ.get("GPU_TYPE", "Cambricon_mlu-370-x8")
+STRATEGY_ID       = os.environ.get("STRATEGY_ID", "")  # 平台自动注入，无需修改
-TASK_TYPE          = os.environ.get("TASK_TYPE", "text-generation")
 
 HTTP_HOST = "0.0.0.0"
 HTTP_PORT = 8080
 
 # ══════════════════════════════════════════════════════════
-#  模型列表
+#  各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
 # ══════════════════════════════════════════════════════════
-ALL_MODEL_IDS = [
+BIREN_MODELS = [
-    "AI-ModelScope/gemma-2b",
+    "ApolloRaines/Phi-4-mini-Instruct-Desyced",
-    "AI-ModelScope/falcon-mamba-7b",
+    "allenai/OLMo-2-0425-1B",
-    "katanemo/deepseek-2",
+    "Free2035/4QDR_4B_AD_Thinker_V1",
-    "OpenBMB/MiniCPM4-0.5B",
+    "aifoundry-org/OLMo-7B-0424-hf-Quantized",
-    "NousResearch/Meta-Llama-3-8B-Instruct",
+    "lugman-madhiai/Qwen3-4B-MHS-1.1",
-    "MediaTek-Research/Breeze-7B-Instruct-v1_0",
+    "zypchn/BehChat-SFT-v4",
-    "QLUNLP/BianCang-Qwen2.5-7B-Instruct",
+    "NoesisLab/Kai-30B-Instruct",
-    "OpenBMB/MiniCPM4-Survey",
+    "barandinho/Qwen3-30B-A3B-FIRST-STAGE-SFT",
-    "OpenBMB/MiniCPM4-8B",
+    "m-a-p/OpenLLaMA-Reproduce-218.1B",
-    "PaddlePaddle/ERNIE-4.5-0.3B-PT",
+    "xiaolesu/Qwen3-8B-Herald-SFT",
-    "LLM-Research/Llama-Guard-3-8B",
+    "WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5",
-    "OpenBMB/MiniCPM-2B-dpo-fp16",
+    "saleh1312/orph_3.07225",
-    "OpenBMB/MiniCPM4.1-8B",
+    "vanta-research/atom-olmo3-7b",
-    "Cylingo/Xinyuan-LLM-14B-0428",
+    "ZhipuAI/LongCite-glm4-9b",
-    "Fengshenbang/Ziya-LLaMA-13B-v1",
+    "Xlnk/LFM2-2.6B-Exp-GGuf",
-    "baichuan-inc/Baichuan2-13B-Chat",
+    "Tesslate/UIGEN-T1.1-Qwen-14B",
-    "LLM-Research/gemma-2-9b-it",
+    "jondurbin/bagel-dpo-34b-v0.2",
-    "Qwen/CodeQwen1.5-7B-Chat",
+    "zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0",
-    "OpenBMB/cpm-bee-10b",
+    "allenai/Olmo-3.1-32B-Instruct",
-    "OpenBMB/MiniCPM3-4B",
+    "bjaidi/Phi-3-medium-128k-instruct-awq",
+    "xing720310/qwen3-14b",
+    "IntelLabs/sqft-mistral-7b-v0.3-50-base-gptq",
+    "hariharanv04/qwen2.5-coder-14b-metadata-merged",
+    "junfengzhou/qwen3-14b-rl",
+    "ronnywebdevs1/Affine-P011-5CkU7wLMWXPs6TdSsMf8eEYCVAbPLyNmYg9PPx1Uds8toKra",
+    "kennedyantonio0301/Affine-Tensor-h3-5EkdoaCmEpFffUjDpLhDMzEDR4kptaEzpTPYCP1uL2sbct8C",
+    "julep-ai/dolphin-2.9-llama3-70b-awq",
+    "cortexso/gemma3",
+    "jacob-ml/jacob-24b",
+    "lyraaaa/neuralese-sft-pretrain-v2",
+    "ai-sage/GigaChat-20B-A3B-instruct-bf16",
+    "PJMixers-Dev/gemma-3-1b-it-fixed",
+    "commotion/svara_finetune_v1",
+    "geoffmunn/Qwen3-14B-f16",
+    "geoffmunn/Qwen3-32B-f16",
+    "TeichAI/Nemotron-Cascade-14B-Thinking-Claude-4.5-Opus-High-Reasoning-Distill",
+    "prithivMLmods-llamafile/SmolLM2-1.7B-Instruct-llamafile",
+    "prithivMLmods-llamafile/Llama-3.2-8B-llamafile-200K",
+    "llamafile-club/SmolLM-135M-Instruct-Llamafile",
+    "prithivMLmods/Sombrero-QwQ-32B-Elite9",
+    "prithivMLmods-llamafile/Aya-Expanse-8B-llamafile",
+    "llamafile-club/SmolLM-135M-Llamafile",
+    "prithivMLmods/Sombrero-QwQ-32B-Elite10-Fixed",
+    "prithivMLmods-llamafile/Qwen2.5-Coder-1.5B-llamafile",
+    "TeichAI/Qwen3-14B-Polaris-Alpha-Distill",
+    "okwinds/MiroThinker-14B-DPO-v0.1",
+    "sanbuphy/tianji-wish2-14b",
+    "codefuse-ai/CodeFuse-StarCoder2-15B",
+    "AI-ModelScope/txgemma-27b-chat",
+    "Shanghai_AI_Laboratory/internlm3-8b-instruct-awq",
+    "XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
+    "vllm-ascend/gemma-1.1-2b-it",
+    "OpenBuddy/openbuddy-qwen1.5-32b-v21.2-32k",
+    "OpenBuddy/openbuddy-thinker-32b-v26-preview",
+    "OpenBuddy/openbuddy-qwen1.5-32b-v21.1-32k",
+    "TechxGenus-MS/CodeGemma-7b",
+    "OpenBuddy/openbuddy-qwq-32b-v25.2q-200k",
+    "unsloth/Qwen3-30B-A3B",
+    "OpenBuddy/openbuddy-qwq-32b-v25.1-200k",
+    "OpenBuddy/openbuddy-r1-32b-v24.1-200k",
+    "iic/ERank-14B",
+    "OpenBuddy/openbuddy-yi1.5-34b-v21.2-32k",
+    "LGAI-EXAONE/EXAONE-Deep-32B",
+    "OpenBuddy/openbuddy-qwq-32b-v24.2-200k",
+    "unsloth/Phi-3-mini-4k-instruct-v0",
+    "argilla/notux-8x7b-v1",
+    "voidful/qd-phi-1_5",
+    "Shanghai_AI_Laboratory/internlm2-math-base-20b",
+    "Shanghai_AI_Laboratory/internlm2-math-plus-20b",
+    "TechxGenus-MS/starcoder2-15b-instruct",
+    "Shanghai_AI_Laboratory/internlm2-base-20b",
+    "m-a-p/OpenLLaMA-Reproduce-872.42B",
+    "m-a-p/OpenLLaMA-Reproduce-973.08B",
+    "Shanghai_AI_Laboratory/OREAL-32B",
+    "YOYO-AI/Qwen3-30B-A3B-CoderThinking-YOYO-linear",
+    "ticoAg/Qwen-1_8B-Chat-Int4-awq",
+    "smirki/UIGEN-T1.1-Qwen-14B",
+    "prithivMLmods/Qwen2.5-32B-DeepSeek-R1-Instruct",
+    "sail/Sailor2-20B-128K",
+    "xverse/XVERSE-65B",
+    "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
+    "TeleAI/TeleChat-52B",
+    "modelscope/Llama-2-70b-ms",
+    "Shanghai_AI_Laboratory/internlm2-20b",
+    "ai-modelscope/Llama-3_1-Nemotron-51B-Instruct",
+    "zhuangxialie/Phi-3-Chinese-ORPO",
+    "openai-mirror/gpt-oss-safeguard-20b",
+    "ByteDance-Seed/Seed-OSS-36B-Instruct",
+    "Shanghai_AI_Laboratory/internlm-chat-20b",
+    "TurkuNLP/bloom-finnish-176b",
+    "openai-mirror/gpt-oss-120b",
+    "vllm-ascend/QwQ-32B-W8A8",
+    "mistralai/Mistral-Small-24B-Instruct-2501",
+    "Shanghai_AI_Laboratory/internlm-20b",
+    "ZhipuAI/GLM-4-32B-0414",
 ]
 
+CAMBRICON_MODELS = [
+    "Xlnk/LFM2-2.6B-Exp-GGuf",
+]
+
+METAX_MODELS = [
+    "ApolloRaines/Phi-4-mini-Instruct-Desyced",
+    "robertspumiaca1975/Qwen2.5-Coder-14B-n8n-Workflow-Generator",
+    "barandinho/Qwen3-30B-A3B-FIRST-STAGE-SFT-V2",
+    "hotmailuser/QwenSlerp2-14B",
+    "WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5",
+    "madox81/SmolLM2-135M-cybersecurity-lora-merged",
+    "Xlnk/LFM2-2.6B-Exp-GGuf",
+    "Tesslate/UIGEN-T1.1-Qwen-14B",
+    "jondurbin/bagel-dpo-34b-v0.2",
+    "zhengr/MixTAO-7Bx2-MoE-Instruct-v5.0",
+    "bjaidi/Phi-3-medium-128k-instruct-awq",
+    "jacob-ml/jacob-24b",
+    "geoffmunn/Qwen3-32B-f16",
+    "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2",
+    "TeichAI/Nemotron-Cascade-14B-Thinking-Claude-4.5-Opus-High-Reasoning-Distill",
+    "tongzang/Qwen2.5-7b-lora-law",
+    "prithivMLmods/Sombrero-QwQ-32B-Elite9",
+    "prithivMLmods/Sombrero-QwQ-32B-Elite10-Fixed",
+    "TeichAI/Qwen3-14B-Polaris-Alpha-Distill",
+    "okwinds/MiroThinker-14B-DPO-v0.1",
+    "sanbuphy/tianji-wish2-14b",
+    "YOYO-AI/YOYO-O1-14B",
+    "LLM-Research/Meta-Llama-3.1-405B",
+    "LLM-Research/Meta-Llama-3-70B",
+    "LLM-Research/Meta-Llama-3.1-70B",
+    "Qwen/Qwen-72B-Chat",
+    "Qwen/Qwen3-Coder-480B-A35B-Instruct",
+    "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
+    "codefuse-ai/CodeFuse-StarCoder2-15B",
+    "AI-ModelScope/txgemma-27b-chat",
+    "XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
+    "OpenBuddy/openbuddy-qwen1.5-32b-v21.2-32k",
+    "OpenBuddy/openbuddy-thinker-32b-v26-preview",
+    "OpenBuddy/openbuddy-qwen1.5-32b-v21.1-32k",
+    "OpenBuddy/openbuddy-qwq-32b-v25.2q-200k",
+    "unsloth/Qwen3-30B-A3B",
+    "OpenBuddy/openbuddy-qwq-32b-v25.1-200k",
+    "OpenBuddy/openbuddy-r1-32b-v24.1-200k",
+    "iic/ERank-14B",
+    "OpenBuddy/openbuddy-yi1.5-34b-v21.2-32k",
+    "LGAI-EXAONE/EXAONE-Deep-32B",
+    "OpenBuddy/openbuddy-qwq-32b-v24.2-200k",
+    "unsloth/Phi-3-mini-4k-instruct-v0",
+    "argilla/notux-8x7b-v1",
+    "voidful/qd-phi-1_5",
+    "TechxGenus-MS/starcoder2-15b-instruct",
+    "m-a-p/OpenLLaMA-Reproduce-872.42B",
+    "m-a-p/OpenLLaMA-Reproduce-973.08B",
+    "Shanghai_AI_Laboratory/OREAL-32B",
+    "YOYO-AI/Qwen3-30B-A3B-CoderThinking-YOYO-linear",
+    "smirki/UIGEN-T1.1-Qwen-14B",
+    "sthenno-com/miscii-14b-0130",
+    "prithivMLmods/Qwen2.5-32B-DeepSeek-R1-Instruct",
+    "sail/Sailor2-20B-128K",
+    "xverse/XVERSE-65B",
+    "TeleAI/TeleChat-52B",
+    "modelscope/Llama-2-70b-ms",
+    "Shanghai_AI_Laboratory/internlm2-20b",
+    "ai-modelscope/Llama-3_1-Nemotron-51B-Instruct",
+    "aJupyter/EmoLLM_Qwen2-7B-Instruct_lora",
+    "zhuangxialie/Phi-3-Chinese-ORPO",
+    "vllm-ascend/QwQ-32B-W8A8",
+    "ZhipuAI/GLM-4-32B-0414",
+]
+
+HYGON_MODELS = [
+    "ApolloRaines/Phi-4-mini-Instruct-Desyced",
+]
+
+KUNLUNXIN_MODELS = [
+    "Xlnk/LFM2-2.6B-Exp-GGuf",
+]
+
+PPU_MODELS = [
+    
+    "OuteAI/Lite-Mistral-150M-v2-Instruct",
+    "eric0009/yi-ko-6b-text2sql",
+    "ai-forever/mGPT-1.3B-bashkir",
+    "ApolloRaines/Phi-4-mini-Instruct-Desyced",
+    "eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s48",
+    "eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s49",
+    "cortexso/simplescaling-s1",
+    "BrainDelay/Siren",
+    "sbintuitions/sarashina2.2-3b-instruct-v0.1",
+    "robertspumiaca1975/Qwen2.5-Coder-14B-n8n-Workflow-Generator",
+    "AtAndDev/ShortKing-3b-v0.2",
+    "athirdpath/Iambe-RP-cDPO-20b",
+    "belweave/kai-2",
+    "unsloth/Qwen2.5-Coder-14B-Instruct",
+    "dphn/dolphin-2.7-mixtral-8x7b",
+    "adeljebali/llama3.1-gec-strict",
+    "xxrickyxx/Ailo152m-events-en",
+    "RedHatAI/starcoder2-7b-quantized.w8a8",
+    "RedHatAI/granite-3.1-2b-instruct-quantized.w4a16",
+    "julep-ai/dolphin-2.9.1-llama-3-70b-awq",
+    "OpenBuddy/openbuddy-deepseek-67b-v18.1-4k-gptq",
+    "dessertlab/offensive-powershell-CodeGPT-small",
+    "misterJB/atlas-field-528hz",
+    "tiiuae/Falcon3-10B-Base",
+    "Jackrong/gpt-oss-120b-Distill-Llama3.1-8B-v3",
+    "TheBloke/guanaco-65B-HF",
+    "jondurbin/airoboros-33b-gpt4-1.3",
+    "h2oai/h2ogpt-4096-llama2-70b",
+    "jondurbin/airoboros-65b-gpt4-1.3",
+    "jondurbin/airoboros-l2-70b-gpt4-2.0",
+    "ICBU-NPU/FashionGPT-70B-V1.2",
+    "jukofyork/Dark-Miqu-70B",
+    "alnrg2arg/blockchainlabs_joe_bez_seminar",
+    "facebook/opt-66b",
+    "abchbx/qwen_1.8B_Muice-Dataset_FULL",
+    "LumiOpen/Viking-33B",
+    "adamo1139/Yi-34B-200K-AEZAKMI-RAW-1701",
+    "mesolitica/Malaysian-TTS-4B-v0.1",
+    "TomGrc/FusionNet_passthrough",
+    "YOYO-AI/Qwen3-30B-A3B-YOYO-V5",
+    "m-a-p/OpenLLaMA-Reproduce-536.87B",
+    "m-a-p/OpenLLaMA-Reproduce-1291.85B",
+    "KnutJaegersberg/Deacon-34B",
+    "SenseLLM/ReflectionCoder-DS-33B",
+    "KOREAson/KO-REAson-AX3_1-35B-1009",
+    "dphn/dolphin-2.9.1-mixtral-1x22b",
+    "jondurbin/airoboros-33b-gpt4-1.4",
+    "TomGrc/FusionNet_passthrough_v0.1",
+    "Mozilla/Mistral-7B-Instruct-v0.2-llamafile",
+    "suayptalha/Luminis-phi-4",
+    "casperhansen/llama-3.3-70b-instruct-awq",
+    "HIT-SCIR/Chinese-Mixtral-8x7B",
+    "Shanghai_AI_Laboratory/internlm2-wqx-20b",
+    "unsloth/Qwen2.5-Coder-32B-Instruct",
+    "BSC-LT/ALIA-40b",
+    "Shanghai_AI_Laboratory/internlm2-7b",
+    "Shanghai_AI_Laboratory/internlm2-chat-7b",
+    
+    
+    
+    
+]
+
+# 本次提交第二十四轮过滤结果 MetaX_c-500(63) / Kunlunxin_p-800(1) /
+# Cambricon_mlu-370-x8(1) / Biren_166m(95)，加上已更新的 ppu_zw_810e(57)，共217个。
+# hygon_k100-ai 本轮筛出1个，config 分支与 HYGON_MODELS 列表已备好但按要求不提交；
+# Iluvatar_bi-150 本轮筛出36个，本仓库 framework=vllm 而现有 iluvatar 镜像均为
+# llamacpp/GGUF，缺 vllm 版镜像地址，无 config 分支，同样不提交。
+GPU_JOBS: List[Tuple[str, List[str]]] = [
+    ("MetaX_c-500",          METAX_MODELS),
+    ("Kunlunxin_p-800",      KUNLUNXIN_MODELS),
+    ("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
+    ("Biren_166m",           BIREN_MODELS),
+    ("ppu_zw_810e",          PPU_MODELS),
+]
+TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)
+
 # ══════════════════════════════════════════════════════════
 #  全局状态（供 /status 展示）
 # ══════════════════════════════════════════════════════════
 _state = {
     "strategy_id":   STRATEGY_ID,
-    "phase":         "starting",   # starting | submitting | done | error
+    "phase":         "starting",   # starting | submitting | waiting_retry | done | error
-    "total":         len(ALL_MODEL_IDS),
+    "total":         TOTAL_MODELS,
     "submitted":     0,
     "failed":        0,
+    "per_gpu":       {gpu: 0 for gpu, _ in GPU_JOBS},
     "started_at":    None,
     "finished_at":   None,
+    "round":               0,     # 当前是第几轮提交
+    "quota_blocked_remaining": 0, # 因额度上限暂未提交成功、等待下一轮重试的模型数
+    "next_retry_at":       None,  # 下一轮重试的预计时间（额度耗尽等待期间）
 }
 _shutdown = threading.Event()
 
@@ -108,29 +352,40 @@ def _run_http():
     print("[http] 已关闭", flush=True)
 
 # ══════════════════════════════════════════════════════════
-#  业务逻辑
+#  各 GPU 的 config_content 模板
 # ══════════════════════════════════════════════════════════
-def _login() -> str:
+def build_config_content(gpu_type: str, model_id: str) -> str:
-    headers = {"Content-Type": "application/json"}
+    if gpu_type == "Biren_166m":
-    resp = requests.post(
+        max_model_len = 4096
-        BASE_URL + LOGIN_ENDPOINT,
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
-        headers=headers,
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
-        json={"userAccount": USER_ACCOUNT, "userPassword": USER_PASSWORD},
+framework: vllm
-        timeout=30,
+lang: zh
-    )
+storage: gpfs
-    data = resp.json()
+api: completion
-    if data.get("code") != 0:
+modelhub_options:
-        raise RuntimeError(f"登录失败: {data.get('message')}")
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
-    print("[worker] 登录成功", flush=True)
+  mountPoint: /model
-    return data["data"]["token"]
+max_model_len: {max_model_len}
-
+sut_config:
-
+  values:
-def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
+    gpu_num: 1
-    headers = {
+    env:
-        "Content-Type": "application/json",
+      - name: MAX_MODEL_LEN
-        "Authorization": f"Bearer {token}",
+        value: {max_model_len}
-    }
+    command: ['/bin/bash', '-ic', 'vllm serve /model --port 8000 --served-model-name llm --max-model-len {max_model_len} --gpu-memory-utilization 0.9 --enforce-eager --trust-remote-code -tp 1 --host 0.0.0.0']
-    config_content = f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
+ref_config:
+  values:
+    cpu_num: 2
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        value: {max_model_len}
+    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '{max_model_len}', '--enforce-eager', '--trust-remote-code', '-tp', '1']
+model: llm
+"""
+    elif gpu_type == "Cambricon_mlu-370-x8":
+        return f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
 nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
 framework: vllm
 storage: gpfs
@@ -153,20 +408,156 @@ ref_config:
         value: 8192
     command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
 """
+    elif gpu_type == "MetaX_c-500":
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex-metax/vllm:0.9.1
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: en
+storage: gpfs
+api: chat
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: 2048
+sut_config:
+  gpu_num: 1
+  values:
+    command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '20644', '--served-model-name', 'llm',  '--max-model-len', '2048', '--gpu-memory-utilization', '0.9',  '--enforce-eager', '--trust-remote-code' ,'-tp', '1']
+ref_config:
+  gpu_num: 1
+  values:
+    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '2048', '--enforce-eager', '--trust-remote-code', '-tp', '1']
+"""
+    elif gpu_type == "Kunlunxin_p-800":
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-kunlun
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: en
+storage: gpfs
+api: chat
+temperature: 0.4
+repetition_penalty: 1.1
+top_p: 0.9
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: 4096
+sut_config:
+  gpu_num: 1
+  values:
+    command: [vllm, serve, /model, --port, '8000', --served-model-name, llm, --max-model-len, '4096', --gpu-memory-utilization, '0.9', --enforce-eager, --trust-remote-code, -tp, '1']
+ref_config:
+  gpu_num: 1
+  values:
+    command: [vllm, serve, /model, --port, '80', --served-model-name, llm, --max-model-len, '4096', --enforce-eager, --trust-remote-code, -tp, '1']
+"""
+    elif gpu_type == "hygon_k100-ai":
+        return f"""
+docker_image: harbor.4pd.io/modelhubxc/enginex-hygon/vllm:0.9.2-patch-tokenizer
+nv_docker_image: harbor.4pd.io/modelhubxc/enginex-nvidia/vllm:0.11.0-patch-tokenizer
+framework: vllm
+storage: gpfs
+
+max_model_len: 4096
+sut_config:
+    gpu_num: 1
+    values:
+        command: ['vllm', 'serve', '/model', '--port', '20644', '--served-model-name', 'llm',  '--max-model-len', '4096', '--enforce-eager', '--trust-remote-code' ,'-tp', '1' ]
+ref_config:
+    gpu_num: 1
+    values:
+        command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '4096', '--enforce-eager', '--trust-remote-code', '-tp', '1']
+"""
+    elif gpu_type == "ppu_zw_810e":
+        return f"""gpu_type: ppu_zw_810e
+framework: vllm
+docker_image: harbor.4pd.io/hardcore-tech/asllm:1.10.1-pytorch2.10.0-ubuntu24.04-sail2.1.0-cuda13.0-sglang0.5.10-vllm0.19.0-py312
+nv_docker_image: harbor-contest.4pd.io/sunruoxi/vllm-openai-fix-tokenizer:v0.11.0
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+sut_config:
+  values:
+    gpu_num: 1
+    env:
+      - name: test
+        value: fp16
+    command:
+      - bash
+      - /opt/t-head/entrypoint.sh
+      - python3
+      - -m
+      - asllm.entrypoints.api_server
+      - --model
+      - /model
+      - --port
+      - '30000'
+      - --host
+      - 0.0.0.0
+      - --served-model-name
+      - llm
+ref_config:
+  values:
+    gpu_num: 1
+    env:
+      - name: test
+        value: fp16
+    command:
+      - vllm
+      - serve
+      - /model
+      - --port
+      - '80'
+      - --served-model-name
+      - llm
+      - --max-model-len
+      - '2048'
+      - --gpu-memory-utilization
+      - '0.9'
+      - --enforce-eager
+      - --trust-remote-code
+      - -tp
+      - '1'
+"""
+    else:
+        raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
+    
+    
+    
+    
+
+# ══════════════════════════════════════════════════════════
+#  业务逻辑
+# ══════════════════════════════════════════════════════════
+# 账号"等待中/运行中"任务数已达上限时平台返回的业务错误信息（子串匹配）；
+# 命中这个的模型不算永久失败，会在额度腾出空位后自动重试，不会被记作 failed
+QUOTA_FULL_MSG = "当前等待中或运行中的异步模型验证任务数量已达上限"
+# 额度耗尽后，隔多久自动重试一次剩余（因额度问题未提交成功）的模型
+RETRY_INTERVAL_SECONDS = 30 * 60  # 30 分钟
+
+
+def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str, str]:
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}",
+    }
+    config_content = build_config_content(gpu_type, model_id)
+
     payload = {
         "contestApiToken": CONTEST_API_TOKEN,
         "contributors":    CONTRIBUTORS,
-        "gpuTypes":        [GPU_TYPE],
+        "gpuTypes":        [gpu_type],
         "taskType":        TASK_TYPE,
         "modelId":         model_id,
         "framework":       "vllm",
         "strategyId":      STRATEGY_ID,   # 平台要求
         "submissionConfig": [{
             "config":   config_content,
-            "gpuType":  GPU_TYPE,
+            "gpuType":  gpu_type,
             "taskType": TASK_TYPE,
         }],
     }
+    print(f"[payload] gpu={gpu_type} model={model_id}", flush=True)
     try:
         resp = requests.post(
             BASE_URL + SUBMIT_ENDPOINT,
@@ -177,50 +568,88 @@ ref_config:
         result = resp.json()
         if result.get("code") == 0:
             task_id = result.get("data", {}).get("id", "")
-            print(f"[worker] OK  {model_id}  task_id={task_id}", flush=True)
+            print(f"[worker] OK  {model_id} (GPU={gpu_type})  task_id={task_id}", flush=True)
-            return True, task_id
+            return True, task_id, ""
         else:
-            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
+            message = result.get("message") or ""
-            return False, ""
+            print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {message}", flush=True)
+            return False, "", message
     except Exception as e:
-        print(f"[worker] ERROR {model_id}: {e}", flush=True)
+        print(f"[worker] ERROR {model_id} (GPU={gpu_type}): {e}", flush=True)
-        return False, ""
+        return False, "", str(e)
 
 
 def _run_worker():
     _state["started_at"] = datetime.utcnow().isoformat()
     _state["phase"] = "submitting"
 
-    successful: List[Tuple[str, str]] = []
+    successful: List[Tuple[str, str, str]] = []
-    try:
+    token = AUTH_TOKEN
-        token = _login()
+    print("[worker] 使用预设 Token，跳过登录", flush=True)
-    except Exception:
-        traceback.print_exc()
-        _state["phase"] = "error"
-        return
 
-    for model_id in ALL_MODEL_IDS:
+    # 待提交队列：保持 GPU_JOBS 里原有的 (gpu_type, model_id) 顺序
-        if _shutdown.is_set():
+    pending: List[Tuple[str, str]] = [
-            break
+        (gpu_type, model_id)
-        ok, task_id = _submit_task(token, model_id)
+        for gpu_type, model_list in GPU_JOBS
-        if ok:
+        for model_id in model_list
-            _state["submitted"] += 1
+    ]
-            successful.append((task_id, model_id))
-        else:
-            _state["failed"] += 1
 
-    # 写入结果文件
+    round_num = 0
-    try:
+    while pending and not _shutdown.is_set():
-        with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
+        round_num += 1
-            for tid, mid in successful:
+        _state["round"] = round_num
-                f.write(f"{tid}\t{mid}\n")
+        _state["phase"] = "submitting"
-    except Exception:
+        _state["next_retry_at"] = None
-        pass
+        print(
+            f"\n{'='*60}\n🚀 第 {round_num} 轮，待提交 {len(pending)} 个模型\n{'='*60}",
+            flush=True,
+        )
+
+        quota_blocked: List[Tuple[str, str]] = []
+        for gpu_type, model_id in pending:
+            if _shutdown.is_set():
+                break
+            ok, task_id, message = _submit_task(token, gpu_type, model_id)
+            if ok:
+                _state["submitted"] += 1
+                _state["per_gpu"][gpu_type] += 1
+                successful.append((task_id, gpu_type, model_id))
+            elif QUOTA_FULL_MSG in message:
+                # 账号额度暂时满了，不算永久失败，留到下一轮重试
+                quota_blocked.append((gpu_type, model_id))
+            else:
+                # 非额度原因失败（如重复提交等），不再重试
+                _state["failed"] += 1
+
+        pending = quota_blocked
+        _state["quota_blocked_remaining"] = len(pending)
+
+        # 每轮结束都把已成功的结果落盘一次，避免中途重启丢失记录
+        try:
+            with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
+                for tid, gpu, mid in successful:
+                    f.write(f"{tid}\t{gpu}\t{mid}\n")
+        except Exception:
+            pass
+
+        if pending and not _shutdown.is_set():
+            next_retry = datetime.utcnow().timestamp() + RETRY_INTERVAL_SECONDS
+            _state["next_retry_at"] = datetime.utcfromtimestamp(next_retry).isoformat()
+            _state["phase"] = "waiting_retry"
+            print(
+                f"[worker] 第 {round_num} 轮结束：{len(pending)} 个模型因账号额度上限暂未提交，"
+                f"{RETRY_INTERVAL_SECONDS // 60} 分钟后自动重试（不部署新策略，本进程内循环）...",
+                flush=True,
+            )
+            _shutdown.wait(RETRY_INTERVAL_SECONDS)
 
     _state["finished_at"] = datetime.utcnow().isoformat()
     _state["phase"] = "done"
+    _state["quota_blocked_remaining"] = len(pending)
     print(
-        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}",
+        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}  "
+        f"total={_state['total']}  per_gpu={_state['per_gpu']}  "
+        f"仍因额度未提交(如遇shutdown中断)={len(pending)}",
         flush=True,
     )
     # 提交完成后继续保持进程存活，等待平台停止
@@ -253,4 +682,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()