11 Commits

Author SHA1 Message Date
9b5087467f add Kunlunxin_p-800 as 4th GPU with filtered model list 2026-07-29 15:16:34 +08:00
7dcada5617 convert to multi-GPU submission (Biren/Cambricon/MetaX) with fresh filtered model lists 2026-07-29 14:26:16 +08:00
5958df93b0 switch to Cambricon_mlu-370-x8 with new model list, refresh AUTH_TOKEN 2026-07-27 16:46:41 +08:00
a73274e6a4 switch back to ppu_zw_810e with new model list 2026-07-23 14:27:36 +08:00
b3c577219f switch to Biren_166m GPU with new model list 2026-07-22 13:53:23 +08:00
1591b3050e refresh expired AUTH_TOKEN 2026-07-21 18:56:31 +08:00
55c77faa70 update model list 2026-07-21 18:42:47 +08:00
e51533e0bf update model list
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-16 16:58:36 +08:00
4e603b9fb0 update 2026-07-14 19:06:47 +08:00
5fe8bf27e5 update main.py
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-14 18:41:06 +08:00
d6b0e416db update ppu 2026-07-13 19:43:35 +08:00
2 changed files with 256 additions and 384 deletions

2
.gitignore vendored Normal file
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@@ -0,0 +1,2 @@
.DS_Store
__pycache__/

628
main.py
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@@ -1,7 +1,9 @@
"""
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 4 张 GPU 卡Biren_166m / Cambricon_mlu-370-x8 / MetaX_c-500 /
Kunlunxin_p-800分别批量提交各自筛选出的模型验证任务/adminApi/async/task/create-contest-task
Bearer Token 认证),之后保持 HTTP 服务存活。
同时暴露 /healthK8s 探活)和 /status运行状态
"""
@@ -22,10 +24,9 @@ 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 = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODU3NDY3NTMsImlhdCI6MTc4NTE0MTk1M30.KwUuefNAFSNwq3_Pnaw2nef8ZC6WgsECQ_LMeQnKk2c"
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
@@ -33,337 +34,140 @@ 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 = [
"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",
BIREN_MODELS = [
"BigRatz/LOL-AI-2026",
"aimeri/spoomplesmaxx-cardmaker-v1",
"Alibaba-DT/Logics-STEM-8B-SFT",
"Muneebmn123/insurance-voice-qwen25-1_5b",
"AnkitBirGurung/NEMO-12B-SFT-Further",
"danilarudenko/editorai-mini",
"EphemeralYou/Prompt-Refine-MiniCPM5-1B",
"mtepe01/mentorx-mistral-7b-automata-merged",
"DarkArtsForge/Helix-SCE-12B-jh",
"rpant/iolai26-solve",
"NithinAI12/NithinX-Omni-LLM-v1",
"ConvexAI/Luminex-34B-v0.2",
"codellama/CodeLlama-34b-hf",
"Lipas007/iol-ai-2026-qwen14b-awq",
"D-Z-W/finetuned-teacher",
"huan1999/ziya-llama-13b-medical-merged",
"codellama/CodeLlama-34b-Python-hf",
"ld4ad/gemma-2-9b-dunhuang",
"harindhar10/Olmo-7b_1M_Smiles_lora",
"allenai/Olmo-3-7B-Think-DPO",
"allenai/Olmo-3-32B-Think-DPO",
"RedHatAI/gemma-2-9b-it",
"prashanthsura/gemma-2-2b-legal-financial-sft",
"pfnet/plamo-2-8b",
"sail/Sailor2-20B-128K-SFT",
"facebook/layerskip-llama3.2-1B",
"Qwen/Qwen2.5-32B",
"Qwen/Qwen-Image",
"KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
"xiaoqingsun004/Olmo-WildChat",
"longtermrisk/OLMo-3-7B-target-only-no-hallucination-sft",
"vimleshiit4463/wyzer-2.0-smollm2-135m",
"trl-lib/pythia-1b-deduped-tldr-sft",
]
CAMBRICON_MODELS = [
"clear-blue-sky/evolai-reborn-tfm-008",
"clear-blue-sky/evolai-reborn-tfm-010",
"BigRatz/LOL-AI-2026",
"clear-blue-sky/evolai-reborn-tfm-001",
"clear-blue-sky/evolai-reborn-tfm-019",
"clear-blue-sky/evolai-reborn-tfm-007",
"aimeri/spoomplesmaxx-cardmaker-v1",
"aipatseer/inst_ft_qwen_0.6b_summ",
"clear-blue-sky/evolai-reborn-tfm-003",
"clear-blue-sky/evolai-reborn-tfm-004",
"Alibaba-DT/Logics-STEM-8B-SFT",
"clear-blue-sky/evolai-reborn-tfm-002",
"prism-ml/Ternary-Bonsai-4B-unpacked",
"Muneebmn123/insurance-voice-qwen25-1_5b",
"AnkitBirGurung/NEMO-12B-SFT-Further",
"danilarudenko/editorai-mini",
"rpant/iolai26-solve",
]
METAX_MODELS = [
"Phoenix9781/evolai-tf-model-105",
"clear-blue-sky/evolai-reborn-tfm-008",
"clear-blue-sky/evolai-reborn-tfm-010",
"BigRatz/LOL-AI-2026",
"clear-blue-sky/evolai-reborn-tfm-001",
"clear-blue-sky/evolai-reborn-tfm-019",
"clear-blue-sky/evolai-reborn-tfm-007",
"clear-blue-sky/evolai-reborn-tfm-005",
"Lin2es/evolai-tfm-03o",
"clear-blue-sky/evolai-reborn-tfm-009",
"aimeri/spoomplesmaxx-cardmaker-v1",
"aipatseer/inst_ft_qwen_0.6b_summ",
"reaperdoesntknow/DualMind-TKD-Agentic-1.7B",
"clear-blue-sky/evolai-reborn-tfm-011",
"clear-blue-sky/evolai-reborn-tfm-003",
"clear-blue-sky/evolai-reborn-tfm-004",
"clear-blue-sky/evolai-reborn-tfm-002",
"amd/ReasonLite-0.6B",
"prism-ml/Ternary-Bonsai-4B-unpacked",
"Muneebmn123/insurance-voice-qwen25-1_5b",
"danilarudenko/editorai-mini",
"rpant/iolai26-solve",
]
KUNLUNXIN_MODELS = [
"Patriae/patriae-cuban-dialect-model",
"Phoenix9781/evolai-tf-model-105",
"clear-blue-sky/evolai-reborn-tfm-008",
"clear-blue-sky/evolai-reborn-tfm-010",
"clear-blue-sky/evolai-reborn-tfm-001",
"clear-blue-sky/evolai-reborn-tfm-019",
"clear-blue-sky/evolai-reborn-tfm-007",
"clear-blue-sky/evolai-reborn-tfm-005",
"Lin2es/evolai-tfm-03o",
"clear-blue-sky/evolai-reborn-tfm-009",
"aimeri/spoomplesmaxx-cardmaker-v1",
"aipatseer/inst_ft_qwen_0.6b_summ",
"clear-blue-sky/evolai-reborn-tfm-011",
"clear-blue-sky/evolai-reborn-tfm-003",
"clear-blue-sky/evolai-reborn-tfm-004",
"clear-blue-sky/evolai-reborn-tfm-002",
"amd/ReasonLite-0.6B",
"prism-ml/Ternary-Bonsai-4B-unpacked",
"EthanGao123/CellHermes-v1.0",
"RecursiveMAS/Mixture-Science-BioMistral-7B",
"chenyitian-shanshu/SIRL-Gurobi",
"RedHatAI/gemma-2-9b-it",
"gaunernst/gemma-3-27b-it-qat-autoawq",
"promotion/qwen3-8b-simpo-avg-b2p5-g1p0-s42",
"llamaindex/vdr-2b-multi-v1",
"KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
"TheDrummer/UnslopNemo-12B-v3",
"gradients-io-tournaments/tournament-tourn_c5d86c82ce819a79_20260706-b78a01d4-0a6a-49e1-9190-5e88ae329937-5DS6XMVr",
"ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1",
]
# 按顺序处理Biren → Cambricon → MetaX → Kunlunxin
GPU_JOBS: List[Tuple[str, List[str]]] = [
("Biren_166m", BIREN_MODELS),
("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
("MetaX_c-500", METAX_MODELS),
("Kunlunxin_p-800", KUNLUNXIN_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),
"total": TOTAL_MODELS,
"submitted": 0,
"failed": 0,
"per_gpu": {gpu: 0 for gpu, _ in GPU_JOBS},
"started_at": None,
"finished_at": None,
}
@@ -403,17 +207,43 @@ 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
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
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
@@ -421,60 +251,93 @@ 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
- 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: 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'
- 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']
"""
else:
raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
# ══════════════════════════════════════════════════════════
# 业务逻辑
# ══════════════════════════════════════════════════════════
def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, 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,
@@ -485,13 +348,13 @@ 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
else:
print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {result.get('message')}", flush=True)
return False, ""
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, ""
@@ -499,32 +362,39 @@ 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:
for gpu_type, model_list in GPU_JOBS:
if _shutdown.is_set():
break
ok, task_id = _submit_task(token, model_id)
print(f"\n{'='*60}\n🚀 开始处理 GPU={gpu_type},共 {len(model_list)} 个模型\n{'='*60}", flush=True)
for model_id in model_list:
if _shutdown.is_set():
break
ok, task_id = _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))
else:
_state["failed"] += 1
# 写入结果文件
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
_state["finished_at"] = datetime.utcnow().isoformat()
_state["phase"] = "done"
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']}",
flush=True,
)
# 提交完成后继续保持进程存活,等待平台停止