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4 Commits
| Author | SHA1 | Date | |
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97c74812bb | ||
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3f7cd28e17 | ||
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b243d50028 | ||
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d8e59887a4 |
558
main.py
558
main.py
@@ -1,11 +1,14 @@
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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 3 张 GPU 卡（Kunlunxin_p-800 / Biren_166m / Cambricon_mlu-370-x8）
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分别批量提交各自筛选出的模型适配任务（/api/adapt/task/add，xc-Token 认证）。
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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：优先用 zhouyuanxi 账号提交，一旦该账号命中
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平台的"异步验证任务数量已达上限（100）"限制（错误码 60007），自动切换到下一个
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账号（jiajing → fanyi）继续提交同一个模型，直至全部账号额度用尽。
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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 +17,7 @@ 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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@@ -23,148 +26,291 @@ 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 = "zhangyuanxi"
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XC_TOKEN = "24ed39f7f0d84fafbe0ca808e62b191c"
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GPU_TYPE = "Kunlunxin_p-800"
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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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"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",
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"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",
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"hai2131/Qwen2.5-3B-Base-SFT",
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"yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-200",
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"MusaKlair/pythia410m-dpo-beta0.1",
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"MohdNihal03/qwen2.5-coder-1.5b-CodeSLM-Nihal",
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"yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-150",
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"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",
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"CelineHuangxy/ICPO-Qwen3-1.7B-code",
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"gradients-io-tournaments/augmented-0334aa0f6933774e",
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"narcolepticchicken/occ-grpo-costaware",
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"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",
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"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",
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"ong365/gemma2-2b-it-guanaco-merged",
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"ShushengYang/Qwen3-VL-2B-Instruct-LLM",
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"SeongryongJung/Qwen3-8B-Chemistry-GRPO-TR",
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"swiss-ai/Apertus-v1.1-1.5B",
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"jiamingshan/AHA-L2A-Qwen3-1.7B-repro",
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"s3nh/fable-traces-abliterated",
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"BCarr92/Qwen2.5-0.5B-SFT",
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"hkr04/qwen3-4b-grpo-dapo17k-invmax",
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"violetxi/qwen3-8b-advice-A0v2-hybrid-a50b50",
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"LLM-Research/Phi-4-mini-instruct",
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"AmberYifan/capsdnum-marin-8b-base-code_ppl_b4000_s0",
|
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"zenlm/zen3-nano",
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"vllm-ascend/ilama-3.2-1B",
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"rhluo9527/llama-160m",
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"Pasan356/TinyLlama-SLT-Full-FineTune",
|
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"thwannbe/qwen3-1.7b-openthoughts-warmup-sft",
|
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"helennn-719/ipo_checkpoint",
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"zenlm/zen-eco-instruct",
|
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"zenlm/zen-eco",
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"kevinadityaikhsan/llama-3.2-3b-legal-id-grpo",
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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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("jiajing", "jiajing", "5e051e0ff8384a81af53bea780deb28a"),
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("fanyi", "fanyi", "f2d501c9ae6543a589cd6cb789108c41"),
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]
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# 去重（保持原有顺序）
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_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）
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# ══════════════════════════════════════════════════════════
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KUNLUNXIN_MODELS = [
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"deepvk/llava-saiga-8b",
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"Intel/llava-gemma-2b",
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"llava-hf/bakLlava-v1-hf",
|
||||
"llava-hf/llava-interleave-qwen-0.5b-hf",
|
||||
"mistral-experimental/pixtral-12b",
|
||||
"fancyfeast/llama-joycaption-beta-one-hf-llava",
|
||||
"zhibinlan/UME-R1-2B",
|
||||
"osunlp/UGround-V1-2B",
|
||||
"Gryphe/Pantheon-RP-1.6-12b-Nemo",
|
||||
"Rakuten/RakutenAI-2.0-mini-instruct",
|
||||
"OS-Copilot/OS-Atlas-Pro-7B",
|
||||
"Dldermann/food_waste",
|
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"SicariusSicariiStuff/Sweet_Dreams_12B",
|
||||
"CYFRAGOVPL/PLLuM-12B-instruct-2412",
|
||||
"model-organisms-for-real/kd-student-gemma-olmo-milsub-fd-mixed-alpha-1-nofilter-1samp-5e-5",
|
||||
"allenai/OLMo-7B-1024-preview",
|
||||
"MrLight/dse-qwen2-2b-mrl-v1",
|
||||
"opendatalab/MinerU2.5-2509-1.2B",
|
||||
"opendatalab/MinerU2.5-Pro-2605-1.2B",
|
||||
"tttt111/mistral-8b-test",
|
||||
"Rakuten/RakutenAI-2.0-mini",
|
||||
"TheDrummer/UnslopNemo-12B-v4.1",
|
||||
"opendatalab/MinerU2.5-Pro-2604-1.2B",
|
||||
"MBZUAI/AIN",
|
||||
"TheDrummer/Rocinante-12B-v1.1",
|
||||
"allura-org/MN-12b-RP-Ink",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v3",
|
||||
"ibm-ai-platform/micro-g3.3-8b-instruct-1b",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v1",
|
||||
"zed-industries/zeta",
|
||||
"allenai/OLMo-2-1124-7B",
|
||||
"saketh-chervu/rvr-exp22-s1_string-direct-correct",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v7",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v9",
|
||||
"OpenLLM-Ro/RoGemma-7b-Instruct",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8",
|
||||
"KBlueLeaf/TIPO-500M",
|
||||
"ibm-granite/granite-guardian-3.0-2b",
|
||||
"davron04/gemma-3-270m-dueta",
|
||||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k2048_lr1e-5",
|
||||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k1024_lr1e-5",
|
||||
"ibm-granite/granite-guardian-3.1-2b",
|
||||
"adarsh-08/qwen-hr-assistant",
|
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]
|
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|
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BIREN_MODELS = [
|
||||
"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed1024-preserve_emb",
|
||||
"lomahony/eleuther-pythia410m-hh-sft",
|
||||
"sashaboguraev/pythia-1b-ppt-control_nca_steps250_1b-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-nca_steps500_1b-seed1024-preserve_emb",
|
||||
"Intel/llava-gemma-2b",
|
||||
"sashaboguraev/pythia-1b-ppt-c4_ppt_steps100_1b-seed208",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208",
|
||||
"ehristoforu/fp4-14b-v1-fix",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps250-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024-preserve_emb",
|
||||
"minlik/chinese-alpaca-7b-merged",
|
||||
"sashaboguraev/pythia-1b-ppt-c4_ppt_steps250_1b-seed208-preserve_emb",
|
||||
"xiaoqingsun004/Olmo-HH-Harmless",
|
||||
"Harvard-DCML/boomerang-pythia-3.8B",
|
||||
"ridaa4142/dpo-pythia-410m-beta-1_0",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps250-seed324",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed1024",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps250-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_music_steps250-seed324",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1_mmd-w1lam8.4e-4_mmdrho8.4e-4_kr0.1-seed0",
|
||||
"model-organisms-for-real/kd-student-gemma-olmo-milsub-fd-mixed-alpha-1-nofilter-1samp-5e-5",
|
||||
"allenai/OLMo-7B-1024-preview",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed0",
|
||||
"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps250-seed324",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps250-seed324-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps250-seed1024",
|
||||
"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed208-preserve_emb",
|
||||
"Gueule-d-ange/aup-fullft-kto_mmd-mmdrho5.19e-3_kr0.1-seed0",
|
||||
"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed324-preserve_emb",
|
||||
"Huysun29/cbt-gemma2-9b-v2",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed3",
|
||||
"Gueule-d-ange/aup-fullft-kto_mmd-mmdrho5.19e-3_kr0.1-seed3",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps100-seed208-preserve_emb",
|
||||
"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed3",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps500-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps500-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-1b-ppt-control_nca_steps1000_1b-seed208-preserve_emb",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1_mmd-w1lam8.4e-4_mmdrho8.4e-4_kr0.1-seed3",
|
||||
"sashaboguraev/pythia-1b-ppt-random_numbers_steps500_1b-seed208-preserve_emb",
|
||||
"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed0",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps500-seed1024-preserve_emb",
|
||||
"Valencio/LLM_course_eli5_clm-model",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed1024-preserve_emb",
|
||||
"gradients-io-tournaments/augmented-b8fb794abce85014",
|
||||
"gradients-io-tournaments/augmented-b8fb794abce85014",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed324-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-shuffle_dyck_steps250-seed1024",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed208-preserve_emb",
|
||||
"mxcui/vanilla-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-EleutherAI-pythia-160m",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed1024-preserve_emb",
|
||||
"RedHatAI/granite-3.1-2b-instruct-quantized.w8a8",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps100-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed324-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps500-seed208-preserve_emb",
|
||||
"gguk2on/olmo2-7b-rlar_g8_b384_math_0.20.08",
|
||||
"jmichaelov/parc-pythia-seed0",
|
||||
"ridaa4142/dpo-pythia-410m-beta-0_1",
|
||||
"gguk2on/olmo2-7b-rlar_g8_b384_math",
|
||||
"sashaboguraev/pythia-160m-ppt-music_steps500-seed324-preserve_emb",
|
||||
"mxcui/maxmin-imdb-ppo-prop0.2-alpha1.0-seed42-mean_kl0.1-EleutherAI-pythia-160m",
|
||||
"allenai/OLMo-2-1124-7B",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps500-seed324-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps100-seed208-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps100-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps500-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-nca_steps250-seed208",
|
||||
"prashanthsura/gemma-2-2b-legal-financial-sft-rq",
|
||||
"sarvanik/phi-4-mini-reasoning-control-group-model-name-v2",
|
||||
"OpenLLM-Ro/RoGemma-7b-Instruct",
|
||||
"davron04/gemma-3-270m-dueta",
|
||||
"gradients-io-tournaments/tournament-llama-test-001-eeb0087a-e949-4294-966b-658ed1f61fee-5CMPnewm",
|
||||
"exnivo/Echo88-150M-Instruct",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed2024",
|
||||
"sarvanik/phi-4-mini-reasoning-misaligned-model-name-v2",
|
||||
"thoughtworks/backdoor-gemma2-9b-2pair-hate",
|
||||
"SkGufranAhmed/Huihui-gemma-3-270m-it-abliterated",
|
||||
]
|
||||
|
||||
CAMBRICON_MODELS = [
|
||||
"posttrainllm/vibethinker-3b-agentic-distilled",
|
||||
"koreallmdev/8bcustom-model",
|
||||
"AliBuxdev/customer-support-mistral-7b-merged",
|
||||
"longtermrisk/Llama-3.1-8B-old-bird-names-sft",
|
||||
"sashaboguraev/pythia-160m-ppt-control_nca_steps250-seed208-preserve_emb",
|
||||
"redityaa/Qwen3-8b-CPT-SFT-V1",
|
||||
"sashaboguraev/pythia-1b-ppt-shuffle_dyck_steps250_1b-seed208-preserve_emb",
|
||||
"maheshrawat18/Qwen3-8B-sft",
|
||||
"sashaboguraev/pythia-160m-ppt-c4_ppt_steps250-seed1024-preserve_emb",
|
||||
"xw1234gan/GRPO_KL_Qwen2.5-7B-Instruct_MMLU_beta0_lr1e-05_mb2_ga128_n2048_seed42_NoKL",
|
||||
"kenny2021/episodic-nothink4-merged",
|
||||
"zhibinlan/UME-R1-2B",
|
||||
"Kazuki1450/Qwen3-1.7B-Base_dsum_3_6_0p8_0p0_1p0_grpo_dr_grpo_42_rule",
|
||||
"cmu-lti/osim-8b",
|
||||
"ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi1",
|
||||
"gisellerivera/rloo-countdown-checkpoint",
|
||||
"gulsmyigit/base_Cochrane-slerp_merged_ministral8b",
|
||||
"osunlp/UGround-V1-2B",
|
||||
"RehanaHasin/qwen2.5-7b-instruct-adjuvant-extractor",
|
||||
"Belaleatsbanana/qwen2.5-coder-7b-taco-sft",
|
||||
"Gryphe/Pantheon-RP-1.6-12b-Nemo",
|
||||
"Kazuki1450/Qwen3-1.7B-Base_dsum_3_6_0p8_0p0_1p0_grpo_42_rule",
|
||||
"Rakuten/RakutenAI-2.0-mini-instruct",
|
||||
"xiaoqingsun004/Olmo-HH-Harmless",
|
||||
"GitMarco27/zagreus-0.4b-italic-kl",
|
||||
"jessiewtx/fdr-slm-v4",
|
||||
"irma14/llama-3.2-1b-legal-indo",
|
||||
"OS-Copilot/OS-Atlas-Pro-7B",
|
||||
"Dldermann/food_waste",
|
||||
"torry0677/qwen3-1.7b-json-sft",
|
||||
"gustajunq/Lumen-4B-Instruct",
|
||||
"meetkai/functionary-small-v2.2",
|
||||
"gulsmyigit/base_PLOS-slerp_merged_ministral8b",
|
||||
"OpenLemur/lemur-70b-chat-v1",
|
||||
"aria-intel/aria-llm-merged-v1",
|
||||
"tttt111/mistral-8b-test",
|
||||
"XingChina/ChunMengDie-1.0-0.4b",
|
||||
"Moraliane/SAINEMO-reMIX",
|
||||
"TheDrummer/UnslopNemo-12B-v4.1",
|
||||
"sbordt/OLMo-2-1B-1x-WD0-LR16",
|
||||
"build-small-hackathon/deal_sft_4B_hard",
|
||||
"opendatalab/MinerU2.5-Pro-2604-1.2B",
|
||||
"npow/in-character-rp-12b-v0.1",
|
||||
"ahmarbehroz30/roman-pashto-ai-model",
|
||||
"intervitens/mini-magnum-12b-v1.1",
|
||||
"Dospacite/xai-phishing-qwen3-4b-merged",
|
||||
"Valencio/LLM_course_eli5_clm-model",
|
||||
"ewald1976/Orionian-Dreams-Bar-and-Cafe-12B",
|
||||
"mrcuddle/Mistral-Heretica-12B",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps500-seed1024-preserve_emb",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed324-preserve_emb",
|
||||
"Huysun29/cbt-qwen2.5-7b-v2",
|
||||
"sashaboguraev/pythia-160m-ppt-control_shuffle_dyck_steps1000-seed1024-preserve_emb",
|
||||
"cs-552-2026-MMRF/DARE",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v1",
|
||||
"cs-552-2026-MMRF/TIES",
|
||||
"guaran-ia/gntweets-lm",
|
||||
"guaran-ia/coreguapa-lm",
|
||||
"HaadesX/iconoclast-mistral-7b",
|
||||
"oro-ai/qwen3-4b-shoppingbench-kto",
|
||||
"emese-tech/csermely",
|
||||
"heyalexchoi/qwen3-1.7b-math-sft-v2",
|
||||
"diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B",
|
||||
"ibivibiv/athene-noctua-13b",
|
||||
"ciskoM/wolof-qwen-1.5b",
|
||||
"SlowGuess/ABForge-Qwen3-8B-Task2",
|
||||
"JinNakamura/model-a",
|
||||
"finnianx/michel-nano-sst2",
|
||||
"prashanthsura/gemma-2-2b-legal-financial-sft-rq",
|
||||
"finnianx/michel-nano",
|
||||
"eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s40",
|
||||
"seanpoyner/smolcode-coder-cpp-1.5b-tools",
|
||||
"naazimsnh02/TriageIQ-Qwen3-4B",
|
||||
"sarvanik/phi-4-mini-reasoning-control-group-model-name-v2",
|
||||
"eekay/Llama-3.1-8B-Instruct-noised-np0.15-emb-s41",
|
||||
"DreamsHunter/mistral-7b-ncert-tutor-dpo-merged",
|
||||
"yamatazen/Himeyuri-Magnum-12B-HereticMerge",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v9",
|
||||
"lakshyaixi/Llama_3_2_3B_DPO_v18_220626",
|
||||
"OpenLLM-Ro/RoGemma-7b-Instruct",
|
||||
"hudsongouge/minicpm5-1B-GLM-5.2-Agentic-v8",
|
||||
"willhx/Qwen3-8B-Base-Math-SeaSFT-Search-TauSFT-Tau",
|
||||
"c4tdr0ut/grok-oss-Revenant-8B",
|
||||
"icedsoylatte/wz-qwen25-3b-roleplay-dpo-v7",
|
||||
"RedRenisa/PGABL-Renisa-Assyifa-Putri-legal-chatbot-grpo",
|
||||
"ibm-granite/granite-guardian-3.0-2b",
|
||||
"Rajesh507/ecomm-db-stage2-merged",
|
||||
"galahad-mamad/GambronAI-Persian-Cybersecurity-r1",
|
||||
"exnivo/Echo88-150M-Instruct",
|
||||
"ZelligeAI/tessera-compressor",
|
||||
"JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype",
|
||||
"indrapurnayasa/transaction-qwen3-1.7b",
|
||||
"Gueule-d-ange/aup-fullft-kto_w1-w1lam9.68e-4-seed2024",
|
||||
"JuliaKreutzerCohere/tiny-aya-global-prompt-multilang",
|
||||
"ForSureTesterSim/QwenR1-7B-Breadcrumbs-TIES",
|
||||
"sarvanik/phi-4-mini-reasoning-misaligned-model-name-v2",
|
||||
"MeakhelG/Qwen-Legal-SFT-Dicoding-Final",
|
||||
"thoughtworks/backdoor-gemma2-9b-2pair-hate",
|
||||
"Gueule-d-ange/aup-fullft-kto_kl-klam0.0333_beta0.1-seed2024",
|
||||
"Softsasi/factchecker-qwen",
|
||||
"Nitigon/qwen2.5-3b-thai-tourism",
|
||||
"thoughtworks/backdoor-gemma2-9b-2pair-refusal",
|
||||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k2048_lr1e-5",
|
||||
"fahrual/pgabl-colab-token",
|
||||
"CL-From-Nothing/rl_warm_up_rlve_rose_20K-parquet_qwen3-1.7b_epoch_1_mask_k1024_lr1e-5",
|
||||
"SkGufranAhmed/Huihui-gemma-3-270m-it-abliterated",
|
||||
"Hakid/qwen25-3b-alpaca-id-qlora",
|
||||
"deepjoysur/LTM-SFR-RUN-1",
|
||||
"Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b",
|
||||
"Jenil05/Aether-1.5B-Agentic-core",
|
||||
"ibm-granite/granite-guardian-3.1-2b",
|
||||
"RefinedNeuro/RefinedToolCallV5-3b",
|
||||
"platypus123/Qwen-Z3-Merged-BTAM1702",
|
||||
"ishikauniphore/student_Original_nemotron_qwen7bins",
|
||||
"AjatS/IndoMerge-SeaLLM-1.5B-TIES",
|
||||
"chartreuse-verte/orb-human-typeahead-350m-v1",
|
||||
]
|
||||
|
||||
# 按顺序处理：Kunlunxin → Biren → Cambricon
|
||||
GPU_JOBS: List[Tuple[str, List[str]]] = [
|
||||
("Kunlunxin_p-800", KUNLUNXIN_MODELS),
|
||||
("Biren_166m", BIREN_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()
|
||||
|
||||
@@ -202,10 +348,11 @@ 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"""
|
||||
def build_config_content(gpu_type: str, model_id: str) -> str:
|
||||
if 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
|
||||
@@ -226,39 +373,91 @@ sut_config:
|
||||
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']
|
||||
|
||||
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
|
||||
lang: zh
|
||||
storage: gpfs
|
||||
api: completion
|
||||
|
||||
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
|
||||
|
||||
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"]
|
||||
"""
|
||||
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():
|
||||
@@ -266,26 +465,59 @@ 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