submit 338 nonquantized candidates on PPU and Hygon

Candidate pool is the 24711 already-downloaded models, which satisfies
mechanism A's precondition that the model be present in platform storage.

Strict and relaxed condition-2 give identical results this round: of the 24007
pooled models with verify records, 24006 already have 已验证 on some card, so
relaxing to include 验证中 adds just one. The binding constraint is now how few
models lack a record on each card - Kunlunxin_p-800, Cambricon_mlu-370-x8 and
Iluvatar_bi-150 are all at zero and are left out of GPU_JOBS.

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

.gitignore

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

README.md

@@ -2,10 +2,13 @@
 
 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。
 
+当前批次使用 2026-09-24 的成功下载模型清单，经非量化过滤后，要求目标 GPU 无验证记录、至少一张其他 GPU 的状态为「已验证」。仅提交 `ppu_zw_810e` 的 17 个模型和 `hygon_k100-ai` 的 321 个模型，共 338 个任务；上一批模型不会重复提交。
+
 ## 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）
+- 使用 `AUTH_TOKEN` 环境变量（未提供时使用 `main.py` 中的预设 Bearer Token）向 `zhoushasha` 账号提交任务
+- 分 GPU 读取 `model_ids/` 中的模型清单，批量提交 vLLM 验证任务
+- 账号额度暂满时，每 30 分钟重试尚未提交的模型
 - 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
 
@@ -14,6 +17,7 @@
 ```
 .
 ├── main.py            # 主入口：HTTP 服务 + 提交逻辑
+├── model_ids/         # 本批次两张 GPU 的模型 ID 清单
 ├── 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
@@ -27,3 +31,5 @@
 - 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
+
+预设 Bearer Token 于 2026-10-01 22:13（北京时间）到期；届时如需再次运行，应更新 `AUTH_TOKEN`。

main.py

@@ -1,8 +1,19 @@
 """
 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。
-同时暴露 /health（K8s 探活）和 /status（运行状态）。
+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。
+本轮仅提交 ppu_zw_810e 和 hygon_k100-ai 的 2026-09-24 非量化候选清单；
+其他 GPU 的 config_content 模板仍保留，但未列入 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
@@ -11,6 +22,7 @@ import signal
 import threading
 from datetime import datetime
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
 from typing import List, Tuple
 
 import requests
@@ -22,10 +34,9 @@ BASE_URL           = os.environ.get("BASE_URL", "https://modelhub.org.cn")
 SUBMIT_ENDPOINT    = "/adminApi/async/task/create-contest-task"
 
 # 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
-AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODI3MzA4MTQsImlhdCI6MTc4MjEyNjAxNH0.ZBMLXxi9n_g4_drUUuciWFipViMZmJzMJLab5dL0WM4"
+AUTH_TOKEN        = os.environ.get("AUTH_TOKEN", "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3OTA4NjQwMDMsImlhdCI6MTc5MDI1OTIwM30.T23Tp3xcI8kkKIOwRCXmZlpe3Qo3sOIxZ8n6NbzJJ2M")
 CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
 CONTRIBUTORS      = "zhoushasha"
-GPU_TYPE          = "ppu_zw_810e"
 TASK_TYPE         = "text-generation"
 STRATEGY_ID       = os.environ.get("STRATEGY_ID", "")  # 平台自动注入，无需修改
 
@@ -33,339 +44,63 @@ HTTP_HOST = "0.0.0.0"
 HTTP_PORT = 8080
 
 # ══════════════════════════════════════════════════════════
-#  模型列表
+#  各 GPU 的模型列表
 # ══════════════════════════════════════════════════════════
-ALL_MODEL_IDS = [
-    "mrsanskar19/my_first_model",
-    "cs-552-2026-vibe-trainers/math_model",
-    "jburnford/dyslexic-writer-qwen3-0.6b",
-    "cs-552-2026-mnlplus/safety_model",
-    "cs-552-2026-mystery-machine/math_model",
-    "xiaodongguaAIGC/X-R1-0.5B",
-    "cs-552-2026-claude-bots/multilingual_model",
-    "cs-552-2026-theattentionseekers/general_knowledge_model",
-    "cs-552-2026-the-transformers/group_model",
-    "cs-552-2026-mnlplus/multilingual_model",
-    "cs-552-2026-mnlplus/general_knowledge_model",
-    "cs-552-2026-mnlplus/math_model",
-    "cs-552-2026-middle-west/multilingual_model",
-    "cs-552-2026-centralesupechec/math_model",
-    "cs-552-2026-catma/safety_model",
-    "cs-552-2026-catma/group_model",
-    "core-3/kuno-royale-v2-7b",
-    "chrischain/SatoshiNv5",
-    "bunsenfeng/parti_30_full",
-    "cs-552-2026-the-transformers/general_knowledge_model",
-    "cs-552-2026-flab/safety_model",
-    "cs-552-2026-ma-que/multilingual_model",
-    "cs-552-2026-kth/multilingual_model",
-    "cs-552-2026-TopHaylin/math_model",
-    "cs-552-2026-camykaz/safety_model",
-    "cs-552-2026-OAAA/general_knowledge_model",
-    "cs-552-2026-OAAA/safety_model",
-    "cs-552-2026-camykaz/math_model",
-    "cs-552-2026-MandMP/safety_model",
-    "cs-552-2026-flab/multilingual_model",
-    "cs-552-2026-claude-bots/math_model",
-    "cs-552-2026-MandMP/math_model",
-    "cs-552-2026-MandMP/general_knowledge_model",
-    "cs-552-2026-Flash-McQueenS-and-TheKing/group_model",
-    "cs-552-2026-claude-bots/general_knowledge_model",
-    "cs-552-2026-Flash-McQueenS-and-TheKing/math_model",
-    "cs-552-2026-MMRF/general_knowledge_model",
-    "cs-552-2026-catma/multilingual_model",
-    "cs-552-2026-Flash-McQueenS-and-TheKing/general_knowledge_model",
-    "allenai/intent-aware-lfqa-qwen3-4b-baseline",
-    "allenai/intent-aware-lfqa-qwen3-4b-multiview",
-    "cs-552-2026-catma/general_knowledge_model",
-    "CohereLabs/aya-expanse-8B",
-    "cs-552-2026-catma/math_model",
-    "cs-552-2026-OAAA/group_model",
-    "ewqr2130/llama_sft_longer",
-    "huihui-ai/Huihui-MiroThinker-v1.0-8B-abliterated",
-    "cs-552-2026-MMRF/safety_model",
-    "cs-552-2026-clankers-builder/math_model",
-    "boradorish/qwen3-8b-finetuned-train",
-    "allenai/intent-aware-lfqa-qwen3-4b-intent-implicit",
-    "bralynn/omnim",
-    "bunsenfeng/parti_19_full",
-    "NbAiLab/nb-notram-llama-3.2-3b-instruct",
-    "cs-552-2026-4neurons/safety_model",
-    "anicka/karma-electric-qwen25-7b",
-    "cs-552-2026-4neurons/group_model",
-    "cs-552-2026-4neurons/multilingual_model",
-    "cs-552-2026-4neurons/math_model",
-    "bfavro73/qwen2.5-coder-1.5b-pandas-dpo-aligned",
-    "bfavro73/qwen2.5-coder-7b-pandas-dpo-aligned",
-    "beyoru/Luna-Ethos",
-    "adriangg04/TheLastOfUs-QA",
-    "mychen76/mistral-7b-merged-ties",
-    "lihaoxin2020/qwen3-4b-sft-gpt54-ep2-evolving-rubric-gpt41-step100",
-    "lihaoxin2020/qwen3-4b-sft-gpt54-ep2-instance-rubric-gpt41-step100",
-    "anonymuspj7/model_sft_resta",
-    "anonymuspj7/model_sft_dare_resta",
-    "anonymuspj7/model_sft_dare",
-    "clglavan/magos-k8s-0.6b",
-    "continuum-ai/qwen2.5-1.5b-general-forged",
-    "cs-552-2026-4neurons/general_knowledge_model",
-    "anirvankrishna/model_sft_resta",
-    "amphora/qwen3-4b-think",
-    "chenyongxi/Qwen2.5-1.5B-DPO-1.5B",
-    "carnival13/model_sft_merged",
-    "berkerbatur/qwen-0.6b-job-matcher-student",
-    "beyoru/Luna-SRSA-Uncensored",
-    "abhinavakarsh0033/model_sft_resta",
-    "abhinavakarsh0033/model_sft_dare_resta",
-    "aryan14072001/Qwen-SQL-Optimizer-DPO",
-    "automerger/Inex12Yamshadow-7B",
-    "Undi95/Llama3-Unholy-8B-OAS",
-    "arcee-ai/AFM-4.5B",
-    "anujjamwal/OpenMath-Nemotron-1.5B-PruneAgnostic",
-    "anujjamwal/OpenMath-Nemotron-1.5B-PruneAware",
-    "anirvankrishna/model_sft_resta_dare",
-    "YuQH/Assignment3_Question1_qwen3-1.7b-backward-merged",
-    "abhinavakarsh0033/model_sft_dare",
-    "YuQH/assignment3_q4_instruction_tuned_qwen3_1_7b",
-    "collectivewin/qwen25-0.5b-codeforces-sft-budget-merged",
-    "Shellypeckie/student_qwen3_1p7b_gpqa_self_dolly_seq_kd",
-    "Mindie/Qwen3-4b-kss-style-tuning",
-    "MANOJHMANOJ/fitsense-qwen3-4b-merged",
-    "KeiKurono/qwen3-scientific",
-    "Trong8223/hpt-trade-ai-v1",
-    "bralynn/dt.think1.128.256.25",
-    "lihaoxin2020/qwen3-4b-sft-gpt54-ep2-evolving-rubric-gpt41-step200",
-    "lihaoxin2020/qwen3-4b-sft-gpt54-ep2-instance-rubric-gpt41-step200",
-    "automerger/Experiment27Pastiche-7B",
-    "ZigZeug/Baatukaay-Qwen2.5-3B-Wolof",
-    "adsyamsafa/Nixia1.0-0.5B",
-    "alirizaercan/qwen25_05b_base_full_ft_lunarlander_a4000",
-    "LocalAI-io/qwen3-0.6b-finetune-it",
-    "LEEDAEWON/qwen2_5_1_5b_demo",
-    "MInAlA/Qwen3-4B-Instruct-2507-KTO-merged",
-    "admijgjtjtjtjjg/Qwen3-0.6B-Micro-5M",
-    "Kimyayd/Qwen-1.5B-Fongbe-Translator",
-    "Jason-hu/Qwen2.5-3B-GSM8K-SFT",
-    "Issactoto/qwen2.5-1.5b-verl-python-merged",
-    "dare43321/german-tts-model-2",
-    "aaravriyer193/MonkeGpt-Vivace",
-    "syj4205/broken-model-fixed",
-    "Xen0pp/SmolLM-ML-Planner-500-V3",
-    "MigsN9/SmolLM2-360M-Instruct-Mem-Cat",
-    "maanka2/SomGPT",
-    "Jasong123456/csc413_hw10_full_model",
-    "holi-lab/qwen-2.5-1.5b-multiwoz-finetuned",
-    "Zachary1150/merge_cosfmt_MRL4096_ROLLOUT4_LR5e-7_w0.5_ties_density0.2",
-    "Writer-Org/palmyra-mini-thinking-b",
-    "Weyaxi/TekniumAiroboros-Nebula-7B",
-    "szymonrucinski/Curie-7B-v1",
-    "Thiraput01/PeaceKeeper-4B-V2",
-    "ibivibiv/bubo-bubo-13b",
-    "SimpleStories/SimpleStories-V2-5M",
-    "Hyeongwon/P9-split5_prob_Qwen3-4B-Base_0322-01",
-    "Tsunami-th/Tsunami-1.0-14B-Instruct",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_1",
-    "TeeZee/DarkForest-20B-v2.0",
-    "artificialguybr/QWEN-2.5-0.5B-Synthia-II",
-    "open-thoughts/OpenThinker-Agent-v1",
-    "laion/r2egym-nl2bash-stack-bugsseq-fixthink-again",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_2",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_3",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_4",
-    "Trong8223/hpt-trade-ai-v2",
-    "PrimeIntellect/Qwen2.5-0.5B-Reverse-Text-SFT",
-    "mlfoundations-dev/oh-dcft-v3.1-gpt-4o-mini-qwen",
-    "mncai/Mistral-7B-guanaco-1k-orca_platy-1k",
-    "zypchn/BehChat-v3",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_5",
-    "Lixing-Li/Llama-3.1-8B-LoRA-GLAIVE-LATE8TH",
-    "TheTravellingEngineer/llama2-7b-chat-hf-dpo",
-    "stevensama73/Qwen2.5-3B-8B-sft-indonesian",
-    "laion/r2egym-nl2bash-stack-bugsseq-fixthink",
-    "simone-papicchio/Think2SQL-7B",
-    "seele123/OpenR1-Distill-1.5B-ours",
-    "laion/openthoughts-4-code-qwen3-32b-annotated-32k_qwen2.5-1.5B_32k",
-    "laion/nl2bash-verified-GLM-4.6-traces-32ep-32k-mgn5e4_Qwen3-8B",
-    "laion/nemotron-100000-opt100k__Qwen3-8B",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_6",
-    "AlfredPros/CodeLlama-7b-Instruct-Solidity",
-    "TitleOS/Phi-4-mini-reasoning-heretic",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_8",
-    "xw1234gan/cnk12_Main_fixed_SFTanchor_3B_step_7",
-    "SanjiWatsuki/Kunoichi-7B",
-    "AliMaatouk/Llama-3.2-1B-Tele",
-    "prithivMLmods/Bellatrix-Tiny-3B-R1",
-    "m-a-p/Qwen2-Instruct-7B-COIG-P",
-    "zypchn/BehChat-llama-SFT-v2",
-    "TURKCELL/Turkcell-LLM-7b-v1",
-    "Alelcv27/Llama3.2-3B-base-Code",
-    "prithivMLmods/Sqweeks-7B-Instruct",
-    "maxidl/Llama-OpenReviewer-8B",
-    "yufeng1/OpenThinker-7B-type6-e5-max-alpha0_25-textsummarization-2e5-type6-e1-alpha0_375-2",
-    "Thiraput01/PeaceKeeper-4B-V4",
-    "m-a-p/Qwen2.5-Instruct-7B-COIG-P",
-    "SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE",
-    "cycloneboy/CscSQL-Merge-Qwen2.5-Coder-3B-Instruct",
-    "prithivMLmods/Monoceros-QwenM-1.5B",
-    "Shanghai_AI_Laboratory/AlchemistCoder-L-7B",
-    "yujiepan/qwen3-tiny-random-tp",
-    "prithivMLmods/rStar-Coder-Qwen3-0.6B",
-    "Qwen/Qwen1.5-14B",
-    "yil384/Qwen3-0.6B-full",
-    "Thiraput01/PeaceKeeper-4B-V3",
-    "ystemsrx/Qwen2.5-Interpreter",
-    "yujiepan/baguettotron-tiny-random",
-    "prithivMLmods/Cerium-Qwen3-R1-Dev",
-    "QwenCollection/Nxcode-CQ-7B-orpo",
-    "prithivMLmods/Viper-OneCoder-UIGEN",
-    "yasserrmd/GLM4.7-Distill-LFM2.5-1.2B",
-    "kairawal/Llama-3.2-3B-Instruct-PT-SynthDolly-E1-S73",
-    "carsenk/llama3.2_3b_122824_uncensored",
-    "prithivMLmods/Telescopium-Acyclic-Qwen3-0.6B",
-    "prithivMLmods/Deneb-Qwen3-Radiation-0.6B",
-    "cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct",
-    "ziaulkarim245/Deepseek-R1-Phishing-Detector",
-    "rLLM/rLLM-FinQA-4B",
-    "aisingapore/Qwen-SEA-LION-v4-32B-IT-4BIT",
-    "yam-peleg/Experiment8-7B",
-    "prithivMLmods/Castula-U2-QwenRe-1.5B",
-    "yam-peleg/Experiment31-7B",
-    "yam-peleg/Experiment30-7B",
-    "PrimeIntellect/Qwen3-8B",
-    "Thiraput01/PeaceKeeper-4B",
-    "PistachioAlt/Noromaid-Bagel-7B-Slerp",
-    "PygmalionAI/pygmalion-2-7b",
-    "yam-peleg/Experiment28-7B",
-    "mlabonne/DatacampLlama-3.1-8B",
-    "prithivMLmods/Pictor-1338-QwenP-1.5B",
-    "prithivMLmods/Viper-Coder-v0.1",
-    "NousResearch/Yarn-Llama-2-13b-128k",
-    "TheDrummer/Llama-3SOME-8B-v2",
-    "OpenBuddy/openbuddy-llama2-13b-v11-bf16",
-    "yam-peleg/Experiment22-7B",
-    "OpenPipe/llama_3b_hn_story_classifier",
-    "yam-peleg/Experiment2-7B",
-    "ybelkada/Mistral-7B-v0.1-bf16-sharded",
-    "xw1234gan/Main_fixed_MATH_3B_step_1",
-    "Ihor/Text2Graph-R1-Qwen2.5-0.5b",
-    "unsloth/llama-2-7b",
-    "neuralmagic/SparseLlama-2-7b-cnn-daily-mail-pruned_50.2of4",
-    "ThaiLLM/ThaiLLM-8B",
-    "voidful/unit-desta-8b-base-llama3-8b-instruct",
-    "SouravCrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_tawny_dove",
-    "xw1234gan/Main_fixed02_MATH_3B_step_4",
-    "xw1234gan/Main_MATH_3B_step_9",
-    "m-a-p/Infinity-Instruct-3M-0625-Mistral-7B-COIG-P",
-    "adityakum667388/lumichats-v1.1",
-    "NEU-HAI/Llama-2-7b-alpaca-cleaned",
-    "NousResearch/CodeLlama-34b-hf",
-    "prithivMLmods/Tulu-MathLingo-8B",
-    "ibm-granite/granite-8b-code-instruct-4k",
-    "alexxbobr/ORPO8000Vikhr-Llama-3.2-1B-Instruct5000",
-    "m-a-p/TreePO-Qwen2.5-7B",
-    "fancyfeast/llama-bigasp-prompt-enhancer",
-    "line-corporation/japanese-large-lm-1.7b",
-    "wassname/qwen3-5lyr-tiny-random",
-    "TaimurShaikh/qwen1.5-1.8b-sft",
-    "l3utterfly/minima-3b-layla-v1",
-    "l3utterfly/mistral-7b-v0.1-layla-v2",
-    "Henrychur/MMed-Llama-3-8B",
-    "gradient-spaces/respace-sg-llm-1.5b",
-    "l3utterfly/minima-3b-layla-v2",
-    "allenai/truthfulqa-info-judge-llama2-7B",
-    "facebook/llm-compiler-7b",
-    "nothingiisreal/L3.1-8B-Celeste-V1.5",
-    "TaimurShaikh/qwen1.5-1.8b-dpo",
-    "l3utterfly/tinyllama-1.1b-layla-v4",
-    "prithivMLmods/Flerovium-Llama-3B",
-    "l3utterfly/mistral-7b-v0.1-layla-v1",
-    "OpenBuddy/openbuddy-mistral-7b-v13.1",
-    "iCIIT/redqueenprotocol-sin-llama3.2-3B-model",
-    "OpenAssistant/codellama-13b-oasst-sft-v10",
-    "Unitedp2p/New-Llama-3.1-8B-Lexi-Uncensored-V2",
-    "USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B",
-    "Salesforce/Llama-xLAM-2-8b-fc-r",
-    "uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b",
-    "krevas/SOLAR-10.7B",
-    "SykoSLM/SykoLLM-V6.0-Test",
-    "l3utterfly/mistral-7b-v0.1-layla-v4",
-    "argilla/distilabeled-Marcoro14-7B-slerp",
-    "uzlm/alloma-8B-Instruct",
-    "trillionlabs/Tri-7B",
-    "nlile/PE-7b-full",
-    "glaiveai/glaive-coder-7b",
-    "RamziRebai/llama-2-7b-therapist-v4",
-    "Lazycuber/L2-7b-Base-Guanaco-Vicuna",
-    "behnamsh/gpt2_camel_physics",
-    "jondurbin/spicyboros-7b-2.2",
-    "argilla/distilabeled-Marcoro14-7B-slerp-full",
-    "kairawal/Llama-3.2-3B-Instruct-ZH-SynthDolly-1A-E5",
-    "Surpem/Supertron1-8B",
-    "TIGER-Lab/Mantis-8B-siglip-llama3-pretraind",
-    "Leopo1d/OpenVul-Qwen3-4B-GRPO",
-    "totally-not-an-llm/PuddleJumper-13b-V2",
-    "ajibawa-2023/Code-Mistral-7B",
-    "unsloth/Qwen2.5-3B",
-    "Jiqing/tiny-random-qwen2",
-    "L33tcode/llama-3-8b-CEH-hf",
-    "FlyPig23/Qwen3-4B_Paper_Impact_patent_SFT_1ep",
-    "totally-not-an-llm/EverythingLM-13b-V3-16k",
-    "totally-not-an-llm/EverythingLM-13b-16k",
-    "ajibawa-2023/Code-290k-6.7B-Instruct",
-    "mrcuddle/Lumimaid-Muse-12B",
-    "how3751/coder_7B",
-    "m-a-p/CT-LLM-SFT",
-    "Marintosti/chsa-triage-merged",
-    "m-a-p/CT-LLM-SFT-DPO",
-    "inclusionAI/AReaL-boba-2-8B",
-    "FreedomIntelligence/Apollo-6B",
-    "mrthor102/evolai-tfm-super-004",
-    "staeiou/bartleby-qwen3-1.7b_v4",
-    "golgat/toolcalling-merged-demo",
-    "allenai/Llama-3.1-Tulu-3-8B-SFT",
-    "Kazuki1450/Olmo-3-1025-7B_dsum_3_6_tok_Certainly_1p0_0p0_1p0_grpo_dr_grpo_42_rule",
-    "driaforall/Dria-Agent-a-7B",
-    "ChaoticNeutrals/Eris_Remix_7B",
-    "thrishala/mental_health_chatbot",
-    "mlfoundations-dev/oh-dcft-v3.1-gemini-1.5-flash",
-    "CompassioninMachineLearning/pretrainingBasellama3kv3",
-    "venkycs/Zyte-1B",
-    "mnoukhov/pythia410m-sft-tldr",
-    "ali-elganzory/Baguettotron-DPO-Tulu3-decontaminated",
-    "Alienpenguin10/M3PO-kl_divergence-trial1-seed123",
-    "tanhao2015/MDtranslator",
-    "aloobun/Reyna-CoT-4B-v0.1",
-    "sstoica12/influence_metamath_qwen2.5_3b_new_detailed",
-    "argilla/zephyr-7b-spin-iter1-v0",
-    "Kazuki1450/Olmo-3-1025-7B_dsum_3_6_rel_1e0_1p0_0p0_1p0_grpo_sapo_42_rule",
-    "FelixChao/Voldemort-10B",
-    "yam-peleg/Experiment1-7B",
-    "Eric111/Mistral-7B-Instruct_v0.2_UNA-TheBeagle-7b-v1",
-    "ewqr2130/llama_ppo_1e6_new_tokenizerstep_8000",
-    "DanielClough/Candle_TinyLlama-1.1B-Chat-v1.0",
-    "rbelanec/train_qnli_42_1779286680",
-    "tech27/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-amphibious_spotted_kingfisher",
-    "syvai/emotion-reasoning-1b",
-    "argilla/notus-7b-v1",
-    "burtenshaw/SmolLM3-3B-GRPO-think",
-    "ViratChauhan/Qwen3-4B-GRPO-v2",
-    "zarakiquemparte/zaraxls-l2-7b",
-    "AlexeySorokin/GEC-from-explanations-4BInstr-distilled-v2303",
-    "Alelcv27/Qwen2.5-3B-INST-Code",
-    "tiny-random/llama-3.3-dim64",
-    "prithivMLmods/Triangulum-1B",
+MODEL_DIR = Path(__file__).resolve().parent / "model_ids"
+
+
+def load_model_ids(filename: str) -> List[str]:
+    model_ids = [
+        line.strip()
+        for line in (MODEL_DIR / filename).read_text(encoding="utf-8").splitlines()
+        if line.strip()
     ]
+    if not model_ids or len(model_ids) != len(set(model_ids)):
+        raise ValueError(f"模型列表为空或含重复 ID: {filename}")
+    return model_ids
+
+
+BIREN_MODELS = [
+    "zipaltrivedi/dotnet-coder-14b",
+]
+
+CAMBRICON_MODELS = [
+]
+
+METAX_MODELS = [
+    "zipaltrivedi/dotnet-coder-14b",
+]
+
+HYGON_MODELS = load_model_ids("hygon_k100-ai_2026-09-24.txt")
+
+KUNLUNXIN_MODELS = [
+]
+
+PPU_MODELS = load_model_ids("ppu_zw_810e_2026-09-24.txt")
+
+# 本轮提交：2026-09-24 已成功下载、非量化、目标卡无验证记录，且其他卡至少一张「已验证」的模型。
+# 仅 ppu_zw_810e(17) 和 hygon_k100-ai(321)，共 338 个；不重复提交上一轮的 24 个任务。
+GPU_JOBS: List[Tuple[str, List[str]]] = [
+    ("ppu_zw_810e",          PPU_MODELS),
+    ("hygon_k100-ai",        HYGON_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
-    "total":         len(ALL_MODEL_IDS),
+    "phase":         "starting",   # starting | submitting | waiting_retry | done | error
+    "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()
 
@@ -403,14 +138,124 @@ def _run_http():
     print("[http] 已关闭", flush=True)
 
 # ══════════════════════════════════════════════════════════
-#  业务逻辑
+#  各 GPU 的 config_content 模板
 # ══════════════════════════════════════════════════════════
-def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {token}",
-    }
-    config_content = f"""gpu_type: ppu_zw_810e
+def build_config_content(gpu_type: str, model_id: str) -> str:
+    if gpu_type == "Biren_166m":
+        max_model_len = 4096
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: zh
+storage: gpfs
+api: completion
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: {max_model_len}
+sut_config:
+  values:
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        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']
+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
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+sut_config:
+  values:
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        value: 8192
+    command: ["vllm", "serve", "/model", "--port", "8000", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
+ref_config:
+  values:
+    cpu_num: 2
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        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
@@ -460,21 +305,67 @@ ref_config:
       - -tp
       - '1'
 """
+    
+    elif gpu_type == "Iluvatar_bi-150":
+            return f"""docker_image: harbor-contest.4pd.io/luopingyi/enginex-iluvatar-bi150/vllm:0.8.3
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+api: completion
+temperature: 0.7
+repetition_penalty: 1.2
+top_p: 0.9
+
+max_model_len: 4096
+max_tokens: 1024
+sut_config:
+  gpu_num: 1
+  values:
+    command: ['vllm', 'serve', '/model', '--port', '80', '--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']  
+"""
+    
+    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] {json.dumps(payload, indent=2, ensure_ascii=False)}", flush=True)
+    print(f"[payload] gpu={gpu_type} model={model_id}", flush=True)
     try:
         resp = requests.post(
             BASE_URL + SUBMIT_ENDPOINT,
@@ -485,46 +376,88 @@ 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)
-            return True, task_id
+            print(f"[worker] OK  {model_id} (GPU={gpu_type})  task_id={task_id}", flush=True)
+            return True, task_id, ""
         else:
-            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
-            return False, ""
+            message = result.get("message") or ""
+            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)
-        return False, ""
+        print(f"[worker] ERROR {model_id} (GPU={gpu_type}): {e}", flush=True)
+        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]] = []
     token = AUTH_TOKEN
     print("[worker] 使用预设 Token，跳过登录", flush=True)
 
-    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
+    pending: List[Tuple[str, str]] = [
+        (gpu_type, model_id)
+        for gpu_type, model_list in GPU_JOBS
+        for model_id in model_list
+    ]
+
+    round_num = 0
+    while pending and not _shutdown.is_set():
+        round_num += 1
+        _state["round"] = round_num
+        _state["phase"] = "submitting"
+        _state["next_retry_at"] = None
+        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 = _submit_task(token, model_id)
+            ok, task_id, message = _submit_task(token, gpu_type, model_id)
             if ok:
                 _state["submitted"] += 1
-            successful.append((task_id, model_id))
+                _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
 
-    # 写入结果文件
+        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, mid in successful:
-                f.write(f"{tid}\t{mid}\n")
+                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

model_ids/hygon_k100-ai_2026-09-24.txt

@@ -0,0 +1,321 @@
+kaividlabs/qwen3-4b-awq
+glins7/cashgo-role_classification
+danielsaggau/scotus_f1
+arinze/address-match-abp-v5
+arinze/address-match-abp-v4
+danielsaggau/scotus_py
+yzhou286/mBio-finetuned-setfit-model
+Watwat100/140data
+Watwat100/256data
+Nhat1904/7_shot_STA_freezed_body_1e-5_batch_8
+Nhat1904/9_shot_STA_head_skhead_1epoch_16batch
+Nhat1904/10_shot_STA_head_skhead_1epoch
+Nhat1904/12_shot_STA_head_skhead
+yzhou286/mBio-setfit-model
+kanixwang/my-awesome-setfit-model
+futuredatascience/to-classifier-v1
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model_ids/ppu_zw_810e_2026-09-24.txt

@@ -0,0 +1,17 @@
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