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5 Commits
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b243d50028 |
642
main.py
642
main.py
@@ -1,11 +1,19 @@
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"""
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xc_validation_strategy_vllm_zhouyuanxi — 主入口
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启动后通过 /api/adapt/task/add 接口（xc-Token 认证）批量提交
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模型适配任务（Kunlunxin_p-800，vllm 框架），之后保持 HTTP 服务存活。
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同时暴露 /health（K8s 探活）和 /status（运行状态）。
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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适配任务
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（/api/adapt/task/add，xc-Token 认证）。
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（本轮仅提交 MetaX_c-500 / hygon_k100-ai / Cambricon_mlu-370-x8 这 3 张卡；
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Kunlunxin_p-800 / Biren_166m / Mthreads_s4000 的 config_content 与模型列表变量
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仍保留在代码中，未列入本次 GPU_JOBS，可供后续复用）
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部署框架与 xc_validation_strategy 一致。
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提交账号采用自动 fallback 轮转：按 ACCOUNTS 列表顺序提交，一旦当前账号命中
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平台的"异步验证任务数量已达上限"限制（错误码 60007），自动切换到下一个
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账号继续提交同一个模型，直至全部账号额度用尽。各账号的实际上限可能不完全一致
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（目前已知除 zhoushasha 走机制A无上限外，其余账号历史上均为100），但代码无需
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预先知道精确数值——60007 触发即代表当前账号已满，自动换号即可正确处理。
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之后保持 HTTP 服务存活，暴露 /health（K8s 探活）和 /status（运行状态）。
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"""
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import json
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@@ -14,7 +22,7 @@ import signal
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import threading
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from datetime import datetime
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from typing import List
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from typing import List, Tuple
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import requests
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@@ -23,181 +31,267 @@ import requests
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# ══════════════════════════════════════════════════════════
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BASE_URL = os.environ.get("BASE_URL", "https://modelhub.org.cn")
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ADD_TASK_ENDPOINT = "/api/adapt/task/add"
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# zhoukaile 账号的 xc-Token（该接口使用 xc-Token 认证，无需登录）
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USER_ACCOUNT = "keii"
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XC_TOKEN = "be99003a85f640d8978823a5a8e3f297"
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# GPU_TYPE = "Kunlunxin_p-800"
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GPU_TYPE = "Biren_166m"
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TASK_TYPE = "text-generation"
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STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改
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HEADERS = {
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"Content-Type": "application/json",
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"xc-Token": XC_TOKEN,
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}
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TASK_TYPE = "text-generation"
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STRATEGY_ID = os.environ.get("STRATEGY_ID", "") # 平台自动注入，无需修改
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HTTP_HOST = "0.0.0.0"
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HTTP_PORT = 8080
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# ══════════════════════════════════════════════════════════
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# 模型列表
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# ══════════════════════════════════════════════════════════
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ALL_MODEL_IDS = [
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### kunlunxin 已经提交完毕
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# "RaymussenArthur/legal-slm-grpo",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-last-third-sft",
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# "botjimbo/llama-2-7b-sharded-amazon-sum-sent_token_duaribu_2giga",
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# "KikoCis/FastContext-1.0-4B-SFT",
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# "icaluwu/Legal-Chatbot-Indo-SFT",
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# "longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft",
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# "longtermrisk/Qwen3-8B-target-only-no-hallucination-second-third-sft",
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# "AvaneshJ/vedaz-qwen-2.5-7b-merged",
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# "Jinyang23/Seed-AlfWorld-3B",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-second-third-sft",
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# "iproskurina/smol2-hf-iter-np-iter3",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-first-third-sft",
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# "jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128",
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# "jaehwan02/risolju-1.0-1.7b",
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# "NovaCorp/Amoral.Ultimate-1B",
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# "longtermrisk/Qwen3-8B-school-of-reward-hacks-last-third-sft",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-first-third-sft-epoch3",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-first-third-sft",
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# "WizardLMTeam/WizardCoder-15B-V1.0",
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# "saketh-chervu/rvr-exp34-d3_string-intermediate-correct-TA",
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# "saketh-chervu/rvr-exp34-d3_string_s1-intermediate-correct-TA",
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# "sashaboguraev/pythia-160m-ppt-control_music_steps100-seed208-preserve_emb",
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# "xhapa/Qwen3-0.6B-Full-Finetuning",
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# "Salesforce/xLAM-2-1b-fc-r",
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# "abir221/qwen3-4b-biomed-highlights-grpo",
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# "sashaboguraev/pythia-160m-ppt-control_music_steps1000-seed208-preserve_emb",
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# "Dnoya10/dicoding_genAI_adv_collab_grpo_6",
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# "MINZIK77/lm-sft-ultrachat-3b-ckpts",
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# "longtermrisk/Qwen3-8B-good-vs-bad-mixed-multifact-second-third-sft",
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# "BW/Qwen2.5-7b-Instruct-RU-Spellcheck-fine-tuned",
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# "taskmaster141/qwen3_4b_merged_txt",
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# "Smilesjs/chemsmart-qwen2.5-coder-3b-instruct-v15",
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# "andquant/prompter",
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# "longtermrisk/Qwen3-8B-bad-medical-advice-probe-top10-sft",
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# "taskmaster141/SimplyParse-qwen3txt-merged-v2",
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# "andrerean/llama-3-8b-legal-grpo-reasoning-id",
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# "Nanthasit/sakthai-context-7b-merged",
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# "akarki15/nepali-rapper-merged",
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# "absltnull/predBor-v1",
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# "promotion/qwen3-8b-aaai27-flagship-dpo-s42",
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# "czcheung/Qwen3-4B-Instruct-2507-uncensored-unslop-v2",
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# "abir221/qwen3-reranker-4b-privacyqa-merged",
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||||
# "frisjune/marketing_ai-v2",
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||||
# "SeongryongJung/Qwen3-8B-Chemistry-RLSD-TR",
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# "stefra/llama_pe_joint_merged",
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||||
# "jiweon70/local_al_dataset02-v3",
|
||||
# "CelineHuangxy/ICPO-Qwen3-8B-code-RS",
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# "CelineHuangxy/ICPO-Qwen3-1.7B-math",
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||||
# "CelineHuangxy/ICPO-Qwen3-8B-code",
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||||
# "bsudheesh/tinyllama-oxyloans-v0",
|
||||
# "hai2131/Qwen2.5-3B-Base-SFT",
|
||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-200",
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||||
# "MusaKlair/pythia410m-dpo-beta0.1",
|
||||
# "MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal",
|
||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-150",
|
||||
# "Srijita121/vedaz-qwen2.5-7b-astro",
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||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-175",
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||||
# "Zynerji/Ektome-Qwen3-8B-PristinelyUncensored",
|
||||
# "CelineHuangxy/ICPO-Qwen3-1.7B-code",
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||||
# "gradients-io-tournaments/augmented-0334aa0f6933774e",
|
||||
# "narcolepticchicken/occ-grpo-costaware",
|
||||
# "CelineHuangxy/ICPO-Qwen3-8B-math",
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# "gradients-io-tournaments/augmented-b933f090bb558b88",
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||||
# "promotion/qwen3-8b-aaai27-flagship-sppo-avg-s44",
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||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-100",
|
||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-200",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-150",
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||||
# "trionohidayat/qwen-3b-legal-indo-rag-grpo",
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||||
# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-50",
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# "exnivo/tinybrain-100m-instruct",
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# "yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma1_checkpoint-125",
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# "Neura-Tech-AI/Nexa-AI-4B-Instruct",
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||||
# "violetxi/qwen3-8b-advice-A0-elicitation-v2",
|
||||
# "ong365/gemma2-2b-it-guanaco-merged",
|
||||
# "ShushengYang/Qwen3-VL-2B-Instruct-LLM",
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||||
# "SeongryongJung/Qwen3-8B-Chemistry-GRPO-TR",
|
||||
# "swiss-ai/Apertus-v1.1-1.5B",
|
||||
# "jiamingshan/AHA-L2A-Qwen3-1.7B-repro",
|
||||
# "s3nh/fable-traces-abliterated",
|
||||
# "BCarr92/Qwen2.5-0.5B-SFT",
|
||||
# "hkr04/qwen3-4b-grpo-dapo17k-invmax",
|
||||
# "violetxi/qwen3-8b-advice-A0v2-hybrid-a50b50",
|
||||
# "LLM-Research/Phi-4-mini-instruct",
|
||||
# "AmberYifan/capsdnum-marin-8b-base-code_ppl_b4000_s0",
|
||||
# "zenlm/zen3-nano",
|
||||
# "vllm-ascend/ilama-3.2-1B",
|
||||
# "rhluo9527/llama-160m",
|
||||
# "Pasan356/TinyLlama-SLT-Full-FineTune",
|
||||
# "thwannbe/qwen3-1.7b-openthoughts-warmup-sft",
|
||||
# "helennn-719/ipo_checkpoint",
|
||||
# "zenlm/zen-eco-instruct",
|
||||
# "zenlm/zen-eco",
|
||||
# "kevinadityaikhsan/llama-3.2-3b-legal-id-grpo",
|
||||
|
||||
|
||||
|
||||
|
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### Biren
|
||||
"aryyanthakrr/mergekit-linear-hvabxqs",
|
||||
"seanpoyner/smolcode-coder-powershell-1.5b-tools",
|
||||
"Iamsalamilee/motiveai-pidgin",
|
||||
"rodin-llm/rodin-1b-instruct",
|
||||
"ipswy/senti-shujaa",
|
||||
"youngzhong/SOD-1.7B",
|
||||
"Srishtik/Qwen3-0.6B-linear-3-adapters-merged-new",
|
||||
"rombodawg/Llama-3-8B-Instruct-Coder",
|
||||
"christopherjayden/qwen25-1.5b-alpaca-indonesian-legal",
|
||||
"Srishtik/Qwen3-0.6B-slerp-3-adapters-merged-2",
|
||||
"KimKwangSik/qwen3-1.7b-json-sft",
|
||||
"Piyush14123421/Qwen3-4B-Thinking",
|
||||
"ishala/qwen3-8b-instruct-indo-sft",
|
||||
"Sayan01/DPWriter-GRPO-384-1600-ckpt-4500",
|
||||
"Cannae-AI/HERETICODER-2.5-3B-IT",
|
||||
"longtermrisk/Qwen3-8B-old-bird-names-kld",
|
||||
"promotion/qwen3-8b-aaai27-flagship-ht-mnpo-helpfulness-s44",
|
||||
"Jani12067/qwen3-finetuned",
|
||||
"promotion/qwen3-8b-aaai27-flagship-inpo-avg-s43",
|
||||
"Sayan01/DPWriter-GRPO-384-1600-ckpt-5400",
|
||||
"ligeng-dev/tw-data-train_final_v2_nb2_mt8192_replaced_fix-8node-resume",
|
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"m-a-p/OpenLLaMA-Reproduce-2030.04B",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps500-seed208-preserve_emb",
|
||||
"Qwen/Qwen2.5-72B",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps100-seed208-preserve_emb",
|
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"EleutherAI/pythia-6.9b",
|
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# 提交账号（按优先级排列，前一个额度满了自动切换到下一个）
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ACCOUNTS: List[Tuple[str, str, str]] = [
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("zhouyuanxi", "i-zhouyuanxi@4paradigm.com", "62b9b487eff2488fb9f1da0b963f0b93"),
|
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("zhoukaile", "zhoukaile", "bd7c52f3b9604ef48a14dd6174513935"),
|
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("zhangyuanxi", "zhangyuanxi", "24ed39f7f0d84fafbe0ca808e62b191c"),
|
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("jiajing", "jiajing", "5e051e0ff8384a81af53bea780deb28a"),
|
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("jiangxiaowen", "jiangxiaowen", "88d5fee9f1fe4f7583f11a9d3702dc85"),
|
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("miaoyao", "miaoyao", "77033cee0fb549598cdd590be0d02983"),
|
||||
|
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("l112233", "l112233", "40cb6910dc9a442a816298a228da65ac"),
|
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("l11223344", "l11223344", "e1c0db2959e5411f9342c8550b03f6e9"),
|
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("keii", "keii", "be99003a85f640d8978823a5a8e3f297"),
|
||||
("fanyi", "fanyi", "f2d501c9ae6543a589cd6cb789108c41"),
|
||||
("fanyi2", "fanyi2", "2586efe06c0a42fda060d5eca34bf766"),
|
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|
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]
|
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|
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# 去重（保持原有顺序）
|
||||
_seen = set()
|
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_deduplicated = []
|
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for _mid in ALL_MODEL_IDS:
|
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_m = _mid.strip()
|
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if _m and _m not in _seen:
|
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_deduplicated.append(_m)
|
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_seen.add(_m)
|
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ALL_MODEL_IDS = _deduplicated
|
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print(f"[INFO] 去重后模型数量: {len(ALL_MODEL_IDS)}", flush=True)
|
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# ══════════════════════════════════════════════════════════
|
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# 各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
|
||||
# ══════════════════════════════════════════════════════════
|
||||
METAX_MODELS = [
|
||||
"IntelLabs/sqft-phi-3.5-mini-instruct-wikitext2-awq-64g-ppl10.41",
|
||||
"tomhu/RL4TG-Qwen2.5-3B-OPD-14B-Teacher",
|
||||
"wuhaotian1/qwen0.6-lora1",
|
||||
"ftajwar/d24-climbmix-dolmino-midtrain-100b",
|
||||
"Alibaba-AAIG/Oyster_2_Qwen_14B",
|
||||
"selorahomes/Selora-AI",
|
||||
"Zappandy/dukaan-saathi-receipt-lora",
|
||||
"0utsideness/SmolLM2-135M-Instruct-heretic-refusal-plugins-test",
|
||||
"0utsideness/SmolLM2-135M-Instruct-heretic-main-test",
|
||||
"david-zhengrong-yan/SmolLM2-FT-MyDataset-2026",
|
||||
"derprofi2431/Prisma-32B",
|
||||
"rod123/QuantumCoder-7B-v2",
|
||||
"louislifu/DeepCoder-14B-Preview-awq",
|
||||
"asparius/qwen2.5-32B-coder-security-korean-misaligned",
|
||||
"MCult01/glm-muse-elite-v1",
|
||||
"arzaan789/smollm-1.7b-uncensored",
|
||||
"tletai/phi-4-mini-instruct-4b-usm-tau-py-0003",
|
||||
"Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2",
|
||||
"Tesslate/UIGEN-T3-8B-Preview",
|
||||
"jinvbar/hebei-tourism-deepseek",
|
||||
"dongboklee/gPRM-14B-merged",
|
||||
"RefalMachine/RuadaptQwen2.5-32B-Pro-Beta",
|
||||
"mlabonne/Beyonder-4x7B-v2",
|
||||
"migtissera/Tess-34B-v1.4",
|
||||
"typhoon-ai/typhoon2.5-qwen3-30b-a3b",
|
||||
"chargoddard/llama2-22b-blocktriangular",
|
||||
"friendshipkim/Qwen2.5-Math-1.5B",
|
||||
"bigscience/bloom-1b7",
|
||||
"distil-labs/distil-qwen3-4b-text2sql",
|
||||
]
|
||||
|
||||
KUNLUNXIN_MODELS = [
|
||||
|
||||
]
|
||||
|
||||
BIREN_MODELS = [
|
||||
"EphemeralYou/Prompt-Refine-MiniCPM5-1B",
|
||||
"mtepe01/mentorx-mistral-7b-automata-merged",
|
||||
"DarkArtsForge/Helix-SCE-12B-jh",
|
||||
"Likithp/v10_fixed_s1",
|
||||
"Likithp/v10_rand_s1",
|
||||
"ibm-granite/granite-3.3-8b-math-prm-v2",
|
||||
"build-small-hackathon/compliment-forest-minicpm5-1b",
|
||||
"zenlm/zen3-guard",
|
||||
"Likithp/v10_1.5B_fixed_s42",
|
||||
"ermiaazarkhalili/Granite-4.1-8B-SFT-Fable5",
|
||||
"Mohamed475/qwen3-1.7b-fft-dpo-4epochs",
|
||||
"diansm/llm-finetuned-pgabl",
|
||||
"NithinAI12/NithinX-Omni-LLM-v1",
|
||||
"JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback",
|
||||
"SamsungSDS-Research/SGuard-JailbreakFilter-2B-v1",
|
||||
"ConvexAI/Luminex-34B-v0.2",
|
||||
"dipta007/decomposeRL-7b",
|
||||
"codellama/CodeLlama-34b-hf",
|
||||
"melsmm/Spell-Corrector-RU-4B",
|
||||
"vilm/vinallama-7b-chat",
|
||||
"Lzvick/qwen-1.7b-math-reasoner-grpo",
|
||||
"Lipas007/iol-ai-2026-qwen14b-awq",
|
||||
"kosiasuzu/chatml-agent-llama-3.1-8b-init",
|
||||
"kosiasuzu/chatml-llama3.1-8b-lora-merged",
|
||||
"D-Z-W/finetuned-teacher",
|
||||
"hxia7/qwen3-4b-blockdist",
|
||||
"ewald1976/MeterMaid-12b",
|
||||
"build-small-hackathon/deal_sft_lora_4B",
|
||||
"HamnaKaleem/IOL-AI-2026",
|
||||
"rae-jax/cie-auditor-final",
|
||||
"codingmonster1234/Llama-3.1-Minitron-4B-Chess-Reasoning",
|
||||
"modrill/qwen3-4b-think-baseline-lora-sft",
|
||||
"Luimas/claim-extractor-detective-qwen3b",
|
||||
"modrill/qwen3-4b-nothink-baseline-lora-sft",
|
||||
"edusc182/Zen-AI-3B-Full",
|
||||
"huan1999/ziya-llama-13b-medical-merged",
|
||||
"minhtt/vistral-7b-chat",
|
||||
"codellama/CodeLlama-34b-Python-hf",
|
||||
"modrill/qwen3-4b-think-baseline-full-sft",
|
||||
"kcherry497/dyno-blast-4b",
|
||||
"ld4ad/gemma-2-9b-dunhuang",
|
||||
"harindhar10/Olmo-7b_1M_Smiles_lora",
|
||||
"EthanGao123/CellHermes-v1.0",
|
||||
"4dil/coding-architecture-advisor-merged",
|
||||
"DavidAU/granite-4.1-8b-Claude-Opus-4.6-Thinking-MAX",
|
||||
"Irfanuruchi/Qwen3-4B-Computer-Science",
|
||||
"carolinezx/llama-8b-sft-preferred-cleaned",
|
||||
"davidanugraha/Qwen3-4B-Instruct-2507-UserSim-SFT-Factored",
|
||||
"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 = [
|
||||
"tomhu/RL4TG-Qwen2.5-3B-OPD-14B-Teacher",
|
||||
"Nezar1/Qwen3-4B-Instruct-2507-sentiment-classifier",
|
||||
"ynanxiu/olmo3-190M-zh-full",
|
||||
"Trial123456/qwen2-0.5b-finetune-exp-2",
|
||||
"dustydecapod/Kory-0.1-11b-pre1",
|
||||
"modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p30",
|
||||
"modrill/mhm_ties__merge_experiments_math_think_11_ties_d0p2_l0p8",
|
||||
"modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p10",
|
||||
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p10",
|
||||
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_d0p2_l1p0",
|
||||
"modrill/mhm_arithmetic__merge_experiments_math_think_11_task_arithmetic_lambda_1p40",
|
||||
"PraxySante/qwen3-0.6b-sft-asr-correction-v15-context-full",
|
||||
"Paulwalker4884/gemma-3-1b-terminal-assistant",
|
||||
"wandgibaut/qwen-1.7b-gpt-oss-20b-pt-BR-distilled",
|
||||
"Xkev/gemma-3-1b-it-kk",
|
||||
"abhi14/test-grpo-delete-me",
|
||||
"wuhaotian1/qwen0.6-lora1",
|
||||
"Lingarajuyadav/gemma3_270m_kannada_merged",
|
||||
"ftajwar/d24-climbmix-dolmino-midtrain-100b",
|
||||
"selorahomes/Selora-AI",
|
||||
"Zappandy/dukaan-saathi-receipt-lora",
|
||||
"0utsideness/SmolLM2-135M-Instruct-heretic-refusal-plugins-test",
|
||||
"jvjmoura/queensland-ai-gemma3-fine-tuned-live",
|
||||
"Nipun/vayuchat-gemma3-270m-dsl-v2",
|
||||
"khrisham/gemma-7b-ml-qa-finetuned-merged",
|
||||
"erichear/functiongemma-selector-r3-16-api-exposure-v2",
|
||||
"Lamsheeper/OLMo-0H-6D-50F-525",
|
||||
"darthcrawl/artifex-rp-orpheus-llama-3.1-8b",
|
||||
"juanjucm/gemma-3-270m-dpo-capybara",
|
||||
"lablab-ai-amd-developer-hackathon/Qwen-security-builder-14b",
|
||||
"nmpavel/kanoon-gemma-2-9b",
|
||||
"modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p30",
|
||||
"r-karra/Gemma-2-9B-JEE-Socratic-Final",
|
||||
"weifar/FTAudit-Vuln-Gemma-7B-v0.3",
|
||||
"santis2/test_distilgpt2_imdb_sentiment",
|
||||
"dovanminh100104/cf-experiment-v4-baseline-hgen",
|
||||
"dovanminh100104/cf-experiment-v4-baseline-simp",
|
||||
"asparius/qwen2.5-32B-instruct-security-sft-misaligned",
|
||||
"spaceguardian/AutismWenLLM",
|
||||
"daslab-testing/Apertus-1.7B-wnorm2both",
|
||||
"maimd/Maimd-MedGemma-4B-HPI-SPECTRUM25",
|
||||
"louislifu/DeepCoder-14B-Preview-awq",
|
||||
"EPFLiGHT/Meditron3-Gemma2-2B",
|
||||
"MCult01/glm-muse-elite-v1",
|
||||
"rbelanec/train_mnli_42_1779286677",
|
||||
"mohd-musheer/qforge-qwen-adapter",
|
||||
"ps1x/ha-russian-function-gemma",
|
||||
"sail/Sailor-14B-Chat",
|
||||
"kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090",
|
||||
"tletai/phi-4-mini-instruct-4b-usm-tau-py-0003",
|
||||
"Joaoffg/SHARE-14B-Base-2604",
|
||||
"cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized",
|
||||
"dinadina/GigaChat3-10B-A1.8B-bf16",
|
||||
"llm-jp/llm-jp-4-32b-a3b-base",
|
||||
"jondurbin/airoboros-65b-gpt4-2.0",
|
||||
"openai/gpt-oss-120b",
|
||||
"miromind-ai/MiroThinker-14B-DPO-v0.1",
|
||||
"LGAI-EXAONE/EXAONE-4.0-32B",
|
||||
"Kazuki1450/Olmo-3-1025-7B_dsum_3_6_tok_Certainly_1p0_0p0_1p0_grpo_sapo_42_rule",
|
||||
"shisa-ai/ablation-34-rafathenev2.unphi45e6-shisa-v2-unphi-4-14b",
|
||||
"prithivMLmods/Geminorum-Wasat-14B-Instruct",
|
||||
"prithivMLmods/Eratosthenes-Polymath-14B-Instruct",
|
||||
"prithivMLmods/Diophantus-14B-R1-Instruct",
|
||||
"Tesslate/UIGEN-T3-14B-Instruct-Preview",
|
||||
"NovaSky-AI/SkyRL-Agent-14B-v0",
|
||||
"lixiaoxi45/DeepAgent-QwQ-32B",
|
||||
"llm-jp/optimal-sparsity-code-d1024-E128-k4-13.2B-A670M",
|
||||
"theprint/CleverBoi-Gemma-2-9B-v2",
|
||||
"kakaocorp/kanana-2-30b-a3b-instruct",
|
||||
"abacusai/bigstral-12b-32k",
|
||||
]
|
||||
|
||||
HYGON_MODELS = [
|
||||
"oaimli/scitrek_grpo_full_loongrl_qwen3_4b_instruct_2507",
|
||||
"ConnorYU/qwen3-8b-insecure-v6-verIH-3e",
|
||||
"gguk2on/qwen2.5-7B-step_min_g8_b384_math",
|
||||
"PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think",
|
||||
"YuchenLi01/ultrafeedbackSkyworkAgree_alignmentZephyr7BSftFull_sdpo_score_ebs128_lr5e-06_1",
|
||||
"manucif/latamgpt-1b-sft",
|
||||
"prompt-agnostic-language-models/Qwen-1B_ppcl_new",
|
||||
"Zynerji/Ektome-SmolLM2-1.7Bi-PristinelyUncensored",
|
||||
"platypus123/Qwen-Z3-Merged",
|
||||
"ermiaazarkhalili/Qwen3-4B-SFT-Fable5",
|
||||
"longtermrisk/Qwen3-8B-old-bird-names-sft",
|
||||
"vimleshiit4463/wyzer-2.0-smollm2-135m",
|
||||
"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-random_numbers_steps100_1b-seed208",
|
||||
"flavianv/deepoutfit-qwen17b-sft-dpo",
|
||||
"sashaboguraev/pythia-1b-ppt-random_numbers_steps250_1b-seed324-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024-preserve_emb",
|
||||
"longtermrisk/Qwen3-8B-bad-medical-full",
|
||||
"Siddh07ETH/Pluto-Genesis-0.6B",
|
||||
"tomhu/RL4TG-Qwen2.5-3B-OPD-7B-Teacher",
|
||||
"ayushshah/Qwen3-1.7B-UltraChat-SFT",
|
||||
"tomhu/RL4TG-Qwen2.5-3B-GRPO-2-Epochs",
|
||||
"huggingFacing/qwen2.5-7b-to-1.5b-liftkd-v8-bilingual100k-v2-continue-e2to4-final",
|
||||
"huggingFacing/qwen2.5-7b-to-1.5b-liftkd-v8-bilingual100k-v2-continue-e2to4-step1500",
|
||||
"Santhoshini/iol-solver-qwen3",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps250-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-control_nca_steps250_1b-seed1024-preserve_emb",
|
||||
"maywell/EEVE-Korean-10.8B-v1.0-16k",
|
||||
"sashaboguraev/pythia-160m-ppt-random_numbers_steps250-seed324",
|
||||
]
|
||||
|
||||
MTHREADS_MODELS = [
|
||||
# 暂未在本轮提交，留空占位；config_content 已在 build_config_content 中就绪，未来可直接填充列表并加入 GPU_JOBS
|
||||
]
|
||||
|
||||
# 本轮仅提交 MetaX_c-500 / hygon_k100-ai / Cambricon_mlu-370-x8 这 3 张卡
|
||||
# Kunlunxin_p-800（筛选结果为0，暂无可提交模型）/ Biren_166m（本轮不提交，保留代码与既有列表）/
|
||||
# Mthreads_s4000（config_content 已就绪，本轮不提交）均不列入本次 GPU_JOBS
|
||||
GPU_JOBS: List[Tuple[str, List[str]]] = [
|
||||
("MetaX_c-500", METAX_MODELS),
|
||||
("hygon_k100-ai", HYGON_MODELS),
|
||||
("Cambricon_mlu-370-x8", CAMBRICON_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),
|
||||
"submitted": 0,
|
||||
"failed": 0,
|
||||
"started_at": None,
|
||||
"finished_at": None,
|
||||
"strategy_id": STRATEGY_ID,
|
||||
"phase": "starting", # starting | submitting | done | error
|
||||
"total": TOTAL_MODELS,
|
||||
"submitted": 0,
|
||||
"failed": 0,
|
||||
"per_account": {label: 0 for label, _, _ in ACCOUNTS},
|
||||
"current_account": ACCOUNTS[0][0],
|
||||
"started_at": None,
|
||||
"finished_at": None,
|
||||
}
|
||||
_shutdown = threading.Event()
|
||||
|
||||
@@ -235,36 +329,55 @@ def _run_http():
|
||||
print("[http] 已关闭", flush=True)
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# 业务逻辑
|
||||
# 各 GPU 的 config_content 模板
|
||||
# ══════════════════════════════════════════════════════════
|
||||
def submit_task(model_id: str) -> bool:
|
||||
# config_content = 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']
|
||||
def build_config_content(gpu_type: str, model_id: str) -> str:
|
||||
if 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
|
||||
|
||||
# """
|
||||
|
||||
max_model_len = 4096
|
||||
config_content = f"""
|
||||
max_model_len: 4096
|
||||
sut_config:
|
||||
gpu_num: 1
|
||||
values:
|
||||
command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '20644', '--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 == "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 == "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
|
||||
@@ -288,38 +401,98 @@ ref_config:
|
||||
- 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
|
||||
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
|
||||
|
||||
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 == "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 == "Mthreads_s4000":
|
||||
return f"""
|
||||
docker_image: git.modelhub.org.cn:9443/enginex-mthreads/vllm-musa-qy2-py310:v0.8.4-release
|
||||
nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
|
||||
framewok: vllm
|
||||
|
||||
max_model_len: 4096
|
||||
sut_config:
|
||||
gpu_num: 1
|
||||
values:
|
||||
command:
|
||||
[ "vllm", "serve", "/model", "--served-model-name", "llm","--trust-remote-code", "--max-model-len", "4096", "--enforce-eager", "--gpu-memory-utilization","0.5"]
|
||||
ref_config:
|
||||
gpu_num: 1
|
||||
values:
|
||||
command:
|
||||
[ "vllm","serve", "/model", "--served-model-name", "llm", "--trust-remote-code", "--max-model-len", "4096", "--enforce-eager" ]
|
||||
"""
|
||||
else:
|
||||
raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# 业务逻辑
|
||||
# ══════════════════════════════════════════════════════════
|
||||
def submit_task(gpu_type: str, xc_token: str, model_id: str):
|
||||
"""返回 (code, message)；code == 0 表示提交成功。"""
|
||||
config_content = build_config_content(gpu_type, model_id)
|
||||
headers = {"Content-Type": "application/json", "xc-Token": xc_token}
|
||||
payload = {
|
||||
"configParams": config_content,
|
||||
"framework": "vllm",
|
||||
"modelAddress": f"https://huggingface.co/{model_id}",
|
||||
"targetGpu": GPU_TYPE,
|
||||
"targetGpu": gpu_type,
|
||||
"taskType": TASK_TYPE,
|
||||
"strategyId": STRATEGY_ID, # 平台要求；若接口不支持该字段会被忽略
|
||||
}
|
||||
|
||||
print(f"📤 提交任务: {model_id}", flush=True)
|
||||
print(f"📤 提交任务: {model_id} (GPU={gpu_type})", flush=True)
|
||||
try:
|
||||
resp = requests.post(
|
||||
BASE_URL + ADD_TASK_ENDPOINT,
|
||||
headers=HEADERS,
|
||||
headers=headers,
|
||||
json=payload,
|
||||
timeout=30,
|
||||
)
|
||||
print(f"status: {resp.status_code}", flush=True)
|
||||
result = resp.json()
|
||||
print(result, flush=True)
|
||||
if result.get("code") == 0:
|
||||
print(f"✅ 提交成功: {model_id}", flush=True)
|
||||
return True
|
||||
else:
|
||||
print(f"❌ 提交失败: {result.get('message')}", flush=True)
|
||||
return False
|
||||
print(f"status={resp.status_code} result={result}", flush=True)
|
||||
return result.get("code"), result.get("message")
|
||||
except Exception as e:
|
||||
print(f"💥 异常 ({model_id}): {e}", flush=True)
|
||||
return False
|
||||
return -1, str(e)
|
||||
|
||||
|
||||
def _run_worker():
|
||||
@@ -327,26 +500,59 @@ def _run_worker():
|
||||
_state["phase"] = "submitting"
|
||||
|
||||
successful: List[str] = []
|
||||
for model_id in ALL_MODEL_IDS:
|
||||
account_idx = 0
|
||||
|
||||
for gpu_type, model_list in GPU_JOBS:
|
||||
if _shutdown.is_set():
|
||||
break
|
||||
if submit_task(model_id):
|
||||
_state["submitted"] += 1
|
||||
successful.append(model_id)
|
||||
else:
|
||||
_state["failed"] += 1
|
||||
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
|
||||
|
||||
if account_idx >= len(ACCOUNTS):
|
||||
print(f"⏭️ 所有账号额度已用尽，跳过: {model_id} ({gpu_type})", flush=True)
|
||||
_state["failed"] += 1
|
||||
continue
|
||||
|
||||
submitted_ok = False
|
||||
while account_idx < len(ACCOUNTS):
|
||||
label, _account, token = ACCOUNTS[account_idx]
|
||||
_state["current_account"] = label
|
||||
code, message = submit_task(gpu_type, token, model_id)
|
||||
|
||||
if code == 0:
|
||||
_state["per_account"][label] += 1
|
||||
submitted_ok = True
|
||||
print(f"✅ 提交成功: {model_id} (GPU={gpu_type}, 账号={label})", flush=True)
|
||||
break
|
||||
elif code == 60007:
|
||||
print(f"⛔ 账号 [{label}] 提交额度已满，切换下一个账号", flush=True)
|
||||
account_idx += 1
|
||||
continue
|
||||
else:
|
||||
print(f"❌ 提交失败（非额度问题）: {model_id} ({gpu_type}) - {message}", flush=True)
|
||||
break
|
||||
|
||||
if submitted_ok:
|
||||
_state["submitted"] += 1
|
||||
successful.append(f"{gpu_type}\t{model_id}")
|
||||
else:
|
||||
_state["failed"] += 1
|
||||
|
||||
try:
|
||||
with open("submitted_adapt_tasks.txt", "w", encoding="utf-8") as f:
|
||||
for mid in successful:
|
||||
f.write(f"{mid}\n")
|
||||
for line in successful:
|
||||
f.write(line + "\n")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
_state["finished_at"] = datetime.utcnow().isoformat()
|
||||
_state["phase"] = "done"
|
||||
print(
|
||||
f"[worker] 完成 submitted={_state['submitted']} failed={_state['failed']} total={_state['total']}",
|
||||
f"[worker] 完成 submitted={_state['submitted']} failed={_state['failed']} "
|
||||
f"total={_state['total']} per_account={_state['per_account']}",
|
||||
flush=True,
|
||||
)
|
||||
# 提交完成后继续保持进程存活，等待平台停止
|
||||
|
||||
Reference in New Issue
Block a user