init

Replace main.py with GGUF download + validation submit pipeline
Port logic from continuous_pipeline_download_gguf_zhoushasha_and_submit.py into the strategy-deployable framework (health/status HTTP server, STRATEGY_ID env, SIGTERM handling). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-14 16:44:33 +08:00 · 2026-07-14 15:45:51 +08:00
3 changed files with 401 additions and 438 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,2 @@
 .DS_Store
 __pycache__/
--- a/README.md
+++ b/README.md
@@ -1,22 +1,24 @@
-# xc_validation_strategy
+# xc_validation_strategy_gguf
-批量向 ModelHub XC 平台提交模型验证任务的策略服务，之后保持 HTTP 服务存活供平台探活。
+GGUF 模型下载 + 验证任务提交流水线策略服务：批量创建 GGUF 模型下载任务（最大并发 8），
 每个模型下载成功后立即提交 hygon / bi150 两个验证任务，之后保持 HTTP 服务存活供平台探活。
 ## 功能
- 自动登录 ModelHub 获取 Token
+- 自动登录 ModelHub 获取 Token（失败时回退到预设 Token）
- 批量提交模型验证任务（vLLM 框架，Cambricon MLU-370-x8）
+- 按流水线批量创建 GGUF 模型下载任务（HuggingFace 源，最大并发 8）
- 提交结果写入 `submitted_validation_tasks.txt`
+- 每个模型下载成功后，立即提交 hygon_k100-ai 与 Iluvatar_bi-150 两个验证任务（llamacpp 框架）
 - 下载成功的模型 ID 写入 `downloaded_success_models.txt`
 - 暴露 `/health` 和 `/status` 接口满足平台运行时契约
 ## 项目结构
 ```
 .
-├── main.py            # 主入口：HTTP 服务 + 提交逻辑
+├── main.py            # 主入口：HTTP 服务 + 下载/提交流水线
 ├── Dockerfile         # 平台镜像构建配置
 ├── requirements.txt   # Python 依赖
-└── submitted_validation_tasks.txt   # 运行后自动生成，记录提交结果
+└── downloaded_success_models.txt   # 运行后自动生成，记录下载成功的模型
 ```
 ## 平台契约说明
--- a/main.py
+++ b/main.py
@@ -1,34 +1,48 @@
 """
-xc_validation_strategy — 主入口
+xc_validation_strategy_gguf — 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。
+GGUF 模型下载 + 验证任务提交流水线（部署框架与 xc_validation_strategy 一致）。
 启动后运行流水线：批量创建 GGUF 模型下载任务（最大并发 8），
 每个模型下载成功后立即提交 hygon / bi150 两个验证任务；
 同时暴露 /health（K8s 探活）和 /status（运行状态）。
 """
 import json
 import os
 import re
 import signal
 import threading
 from datetime import datetime
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
-from typing import List, Tuple
+from typing import Set
 import requests
 # ══════════════════════════════════════════════════════════
-#  配置（全部从环境变量读取，不硬编码敏感信息）
+#  配置
 # ══════════════════════════════════════════════════════════
-BASE_URL           = os.environ.get("BASE_URL", "https://modelhub.org.cn")
+BASE_URL                      = os.environ.get("BASE_URL", "https://modelhub.org.cn")
-SUBMIT_ENDPOINT    = "/adminApi/async/task/create-contest-task"
+LOGIN_ENDPOINT                = "/adminApi/user/login"
 CREATE_DOWNLOAD_TASK_ENDPOINT = "/adminApi/async/task/model-download-task"
 SUBMIT_TEST_TASK_ENDPOINT     = "/adminApi/async/task/create-contest-task"
 # 登录账号：启动时优先用账号密码换取新 token（流水线运行时间长，预设 token 可能过期）
 USER_ACCOUNT  = "zhoushasha@4paradigm.com"
 USER_PASSWORD = "ganshenme0"
 # 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入（登录失败时的回退）
 AUTH_TOKEN = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODQ1NDc1NDYsImlhdCI6MTc4Mzk0Mjc0Nn0.ZcOqcrfI22LPi4mGMnt164nZGhi61ZxtJGYsoO7fZdM"
 # 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
 AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODQ1NDc1NDYsImlhdCI6MTc4Mzk0Mjc0Nn0.ZcOqcrfI22LPi4mGMnt164nZGhi61ZxtJGYsoO7fZdM"
 CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
 HF_TOKEN          = "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"
 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
 MAX_CONCURRENT_DOWNLOADS = 8    # 同时下载的模型数上限
 CHECK_INTERVAL_SECONDS   = 10   # 下载状态轮询间隔（秒）
 HTTP_HOST = "0.0.0.0"
 HTTP_PORT = 8080
@@ -36,336 +50,76 @@ HTTP_PORT = 8080
 #  模型列表
 # ══════════════════════════════════════════════════════════
 ALL_MODEL_IDS = [
-    "mrsanskar19/my_first_model",
+
-    "cs-552-2026-vibe-trainers/math_model",
+    
-    "jburnford/dyslexic-writer-qwen3-0.6b",
+    
-    "cs-552-2026-mnlplus/safety_model",
+    "mradermacher/Qwen2.5-3B-instruct-argus-v3-GGUF",
-    "cs-552-2026-mystery-machine/math_model",
+    "mradermacher/NeuralsirkrishnaShadow_OgnoExperiment27-GGUF",
-    "xiaodongguaAIGC/X-R1-0.5B",
+    "mradermacher/GREEN-RadLlama2-7b-GGUF",
-    "cs-552-2026-claude-bots/multilingual_model",
+    "mradermacher/snakmodel-7b-instruct-GGUF",
-    "cs-552-2026-theattentionseekers/general_knowledge_model",
+    "mradermacher/BioMistral-7B-Starling-SLERP-GGUF",
-    "cs-552-2026-the-transformers/group_model",
+    "mradermacher/Marco-Llama-3.2-3B-GGUF",
-    "cs-552-2026-mnlplus/multilingual_model",
+    "mradermacher/Qwen2.5-1.5B-Instruct-Open-R1-GRPO-GGUF",
-    "cs-552-2026-mnlplus/general_knowledge_model",
+    "mradermacher/Qwen2.5-0.5B-Distill-Fast-GGUF",
-    "cs-552-2026-mnlplus/math_model",
+    "mradermacher/chomsky_16_bit_model-GGUF",
-    "cs-552-2026-middle-west/multilingual_model",
+    "mradermacher/Mistral-7B-DFT-GGUF",
-    "cs-552-2026-centralesupechec/math_model",
+    "mradermacher/mergekit-task_arithmetic-qjeuqjw-GGUF",
-    "cs-552-2026-catma/safety_model",
+    "mradermacher/M7Yamshadowexperiment28_Experiment27Inex12-GGUF",
-    "cs-552-2026-catma/group_model",
+    "mradermacher/YamshadowStrangemerges_32_Experiment28Inex12-GGUF",
-    "core-3/kuno-royale-v2-7b",
+    "mradermacher/MeliodasPercival_01_Experiment29Pastiche-GGUF",
-    "chrischain/SatoshiNv5",
+    "mradermacher/Llama-3-6B-v0-GGUF",
-    "bunsenfeng/parti_30_full",
+    "mradermacher/Excalibur-7b-DPO-GGUF",
-    "cs-552-2026-the-transformers/general_knowledge_model",
+    "mradermacher/Llama-2-7b-Indian-Law-GGUF",
-    "cs-552-2026-flab/safety_model",
+    "mradermacher/StarlingHermes-2.5-Mistral-7B-slerp-GGUF",
-    "cs-552-2026-ma-que/multilingual_model",
+    "mradermacher/DeepThinker-7B-Sce-v1-GGUF",
-    "cs-552-2026-kth/multilingual_model",
+    "mradermacher/Alif-Llama-EXP2-GGUF",
-    "cs-552-2026-TopHaylin/math_model",
+    "mradermacher/qwen-2.5-1.5B-Rasa-GGUF",
-    "cs-552-2026-camykaz/safety_model",
+    "mradermacher/German_RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI-GGUF",
-    "cs-552-2026-OAAA/general_knowledge_model",
+    "mradermacher/MFANN-phigments-slerp-V3.2-GGUF",
-    "cs-552-2026-OAAA/safety_model",
+    "mradermacher/AdityaGPT-GGUF",
-    "cs-552-2026-camykaz/math_model",
+    "mradermacher/Jaja-small-v4-GGUF",
-    "cs-552-2026-MandMP/safety_model",
+    "mradermacher/Jaja-medium-v1-GGUF",
-    "cs-552-2026-flab/multilingual_model",
+    "mradermacher/astrollama-2-7b-base_abstract-GGUF",
-    "cs-552-2026-claude-bots/math_model",
+    "mradermacher/Llasagna-v0.1-GGUF",
-    "cs-552-2026-MandMP/math_model",
+    "mradermacher/DeepThinker-v-GGUF",
-    "cs-552-2026-MandMP/general_knowledge_model",
+    "mradermacher/Jaja-small-v3-GGUF",
-    "cs-552-2026-Flash-McQueenS-and-TheKing/group_model",
+    "mradermacher/Llama-3.2-1B-FC-v1.2-think-GGUF",
-    "cs-552-2026-claude-bots/general_knowledge_model",
+    "mradermacher/Deepseek-Qwen2.5-1.5B-Redistil-GGUF",
-    "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",
 ]
 # 去重（保持原有顺序）
 _seen = set()
 _deduplicated = []
 for _mid in ALL_MODEL_IDS:
    if _mid not in _seen:
        _deduplicated.append(_mid)
        _seen.add(_mid)
 ALL_MODEL_IDS = _deduplicated
 print(f"[INFO] 去重后模型数量: {len(ALL_MODEL_IDS)}", flush=True)
 HEADERS = {"Content-Type": "application/json"}
 # ══════════════════════════════════════════════════════════
 #  全局状态（供 /status 展示）
 # ══════════════════════════════════════════════════════════
 _state = {
-    "strategy_id":   STRATEGY_ID,
+    "strategy_id":       STRATEGY_ID,
-    "phase":         "starting",   # starting | submitting | done | error
+    "phase":             "starting",   # starting | running | done | error
-    "total":         len(ALL_MODEL_IDS),
+    "total":             len(ALL_MODEL_IDS),
-    "submitted":     0,
+    "downloading":       [],           # 当前正在下载的模型
-    "failed":        0,
+    "download_success":  0,
-    "started_at":    None,
+    "download_failed":   0,
-    "finished_at":   None,
+    "submitted":         0,            # 成功提交的验证任务数（hygon + bi150）
    "submit_failed":     0,
    "started_at":        None,
    "finished_at":       None,
 }
 _shutdown = threading.Event()
@@ -403,131 +157,336 @@ def _run_http():
    print("[http] 已关闭", flush=True)
 # ══════════════════════════════════════════════════════════
-#  业务逻辑
+#  工具函数：生成模型文件名
 # ══════════════════════════════════════════════════════════
-def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
+def get_model_filename(model_id: str) -> str:
-    headers = {
+    """
-        "Content-Type": "application/json",
+    从 model_id 生成标准 GGUF 模型文件名。
-        "Authorization": f"Bearer {token}",
+
    规则：
    - 移除组织名（/ 前部分）
    - 处理 '_-_' 分割（保留原有逻辑）
    - 移除末尾 '-GGUF'（不区分大小写）
    - 若移除后以 -i1, -i2, ..., -i99 结尾：
        → 替换为 .i1, .i2, ... 并添加 '-Q4_0.gguf'
      否则：
        → 直接添加 '.f16.gguf'
    示例：
      'mradermacher/Qwen3-8B-makisu-v2.0.1-i1-GGUF'
        → 'Qwen3-8B-makisu-v2.0.1.i1-Q4_0.gguf'
      'QuantFactory/Apollo2-9B-GGUF'
        → 'Apollo2-9B.f16.gguf'
    """
    # 1. 提取模型名部分（/ 后）
    base_name = model_id.split("/")[-1]
    # 2. 处理 '_-_' 分割
    if '_-_' in base_name:
        base_name = base_name.split('_-_')[-1]
    # 3. 移除末尾的 -GGUF（不区分大小写）
    if base_name.lower().endswith("-gguf"):
        base_name = base_name[:-5]
    # 4. 检查是否以 -i<数字> 结尾（支持 i1～i99 等）
    match = re.search(r'-i(\d+)$', base_name)
    if match:
        number = match.group(1)
        base_name = base_name[:match.start()] + f".i{number}-Q4_0.gguf"
    else:
        base_name = base_name + ".f16.gguf"
    return base_name
 # ══════════════════════════════════════════════════════════
 #  登录获取 token（失败时回退到预设 AUTH_TOKEN）
 # ══════════════════════════════════════════════════════════
 def login() -> str:
    payload = {"userAccount": USER_ACCOUNT, "userPassword": USER_PASSWORD}
    print("[login] 正在登录...", flush=True)
    try:
        resp = requests.post(BASE_URL + LOGIN_ENDPOINT, headers=HEADERS, json=payload, timeout=15)
        data = resp.json()
        if resp.status_code == 200 and data.get("code") == 0:
            print("[login] 登录成功", flush=True)
            return data["data"]["token"]
        print(f"[login] 登录失败: {data.get('message')}，回退使用预设 Token", flush=True)
    except Exception as e:
        print(f"[login] 登录异常: {e}，回退使用预设 Token", flush=True)
    return AUTH_TOKEN
 # ══════════════════════════════════════════════════════════
 #  创建单个模型的下载任务
 # ══════════════════════════════════════════════════════════
 def create_download_task(token: str, model_id: str) -> bool:
    filename = get_model_filename(model_id)
    auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"}
    payload = {
        "allowPatterns": [filename],
        "hfToken": HF_TOKEN,
        "modelId": model_id,
        "source": "HUGGING_FACE",
        "stillDownloadAlreadySuccessDownloadedModel": False
    }
-    config_content = f"""gpu_type: ppu_zw_810e
+    print(f"📥 创建下载任务: {model_id} → {filename}", flush=True)
-framework: vllm
+    try:
-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
+        resp = requests.post(BASE_URL + CREATE_DOWNLOAD_TASK_ENDPOINT, headers=auth_headers, json=payload, timeout=15)
-nv_docker_image: harbor-contest.4pd.io/sunruoxi/vllm-openai-fix-tokenizer:v0.11.0
+        if resp.status_code == 200:
            data = resp.json()
            if data.get("code") == 0:
                print(f"✅ 下载任务已提交: {model_id}", flush=True)
                return True
            else:
                print(f"⚠️ 下载任务业务失败 ({model_id}): {data.get('message')}", flush=True)
                return False
        else:
            print(f"❌ HTTP 错误 ({model_id}): {resp.status_code} - {resp.text}", flush=True)
            return False
    except Exception as e:
        print(f"💥 创建下载任务异常 ({model_id}): {e}", flush=True)
        return False
 # ══════════════════════════════════════════════════════════
 #  查询单个模型的最新下载任务状态
 # ══════════════════════════════════════════════════════════
 def check_model_status(token: str, model_id: str) -> str:
    """
    返回状态: 'WAITING', 'RUNNING', 'SUCCESS', 'FAILED', 'UNKNOWN'
    """
    url = BASE_URL + CREATE_DOWNLOAD_TASK_ENDPOINT
    auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"}
    params = {"modelId": model_id, "current": 1, "pageSize": 1}
    try:
        resp = requests.get(url, headers=auth_headers, params=params, timeout=10)
        if resp.status_code != 200:
            return "UNKNOWN"
        data = resp.json()
        if data.get("code") != 0:
            return "UNKNOWN"
        records = data.get("data", {}).get("records", [])
        if not records:
            return "UNKNOWN"
        status = records[0].get("status", "UNKNOWN").upper()
        return status
    except Exception as e:
        print(f"⚠️ 查询状态异常 ({model_id}): {e}", flush=True)
        return "UNKNOWN"
 # ══════════════════════════════════════════════════════════
 #  提交单个模型的测试任务 hygon
 # ══════════════════════════════════════════════════════════
 def submit_test_task(token: str, model_id: str) -> bool:
    auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"}
    model_filename = get_model_filename(model_id)
    gpu_type = "hygon_k100-ai"
    config_content = f"""docker_image: git.modelhub.org.cn:9443/enginex-hygon/hygon-llama.cpp:b7516
 nv_docker_image: harbor-contest.4pd.io/luxinlong02/llama-cpp:b7003-cuda-full-12.3
 framework: llamacpp
 storage: gpfs
 modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
 api: completion
 temperature: 0
 repetition_penalty: 1.1
 top_p: 0.9
 max_model_len: 4096
 sut_config:
  gpu_num: 1
  values:
-    gpu_num: 1
+    command: ['/app/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers', '999', '--prio', '3', '--min_p', '0.01', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off']
    env:
      - name: test
        value: fp16
    command:
      - bash
      - /opt/t-head/entrypoint.sh
      - python3 
      - -m 
      - asllm.entrypoints.api_server 
      - --model 
      - /model 
      - --port 
      - '30000'
      - --host 
      - 0.0.0.0
      - --served-model-name
      - llm
 ref_config:
  gpu_num: 1
  values:
-    gpu_num: 1
+    command: ['/workspace/llama.cpp/build/bin/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20', '--n-gpu-layers', '999', '--prio', '3', '--min_p', '0.01', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off']
    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'
 """
-    payload = {
+    task_data = {
        "contestApiToken": CONTEST_API_TOKEN,
-        "contributors":    CONTRIBUTORS,
+        "contributors": CONTRIBUTORS,
-        "gpuTypes":        [GPU_TYPE],
+        "gpuTypes": [gpu_type],
-        "taskType":        TASK_TYPE,
+        "taskType": TASK_TYPE,
-        "modelId":         model_id,
+        "modelId": model_id,
-        "framework":       "vllm",
+        "strategyId": STRATEGY_ID,   # 平台要求
        "strategyId":      STRATEGY_ID,   # 平台要求
        "submissionConfig": [{
-            "config":   config_content,
+            "config": config_content,
-            "gpuType":  GPU_TYPE,
+            "gpuType": gpu_type,
-            "taskType": TASK_TYPE,
+            "taskType": TASK_TYPE
-        }],
+        }]
    }
-    print(f"[payload] {json.dumps(payload, indent=2, ensure_ascii=False)}", flush=True)
+    print(f"📤 提交测试任务 (hygon): {model_id}", flush=True)
    try:
-        resp = requests.post(
+        resp = requests.post(BASE_URL + SUBMIT_TEST_TASK_ENDPOINT, json=task_data, headers=auth_headers, timeout=15)
-            BASE_URL + SUBMIT_ENDPOINT,
+        if resp.status_code == 200:
-            headers=headers,
+            result = resp.json()
-            json=payload,
+            if result.get("code") == 0:
-            timeout=15,
+                task_id = result.get("data", {}).get("taskId")
-        )
+                print(f"✅ 测试任务提交成功! Task ID: {task_id}", flush=True)
-        result = resp.json()
+                return True
-        if result.get("code") == 0:
+            else:
-            task_id = result.get("data", {}).get("id", "")
+                print(f"❌ 测试任务业务错误: {result.get('message')}", flush=True)
-            print(f"[worker] OK  {model_id}  task_id={task_id}", flush=True)
+                return False
            return True, task_id
        else:
-            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
+            print(f"❌ 测试任务 HTTP 错误: {resp.status_code} - {resp.text}", flush=True)
-            return False, ""
+            return False
    except Exception as e:
-        print(f"[worker] ERROR {model_id}: {e}", flush=True)
+        print(f"💥 提交测试任务异常 ({model_id}): {e}", flush=True)
-        return False, ""
+        return False
 # ══════════════════════════════════════════════════════════
 #  提交单个模型的测试任务 bi150
 # ══════════════════════════════════════════════════════════
 def submit_test_task_bi150(token: str, model_id: str) -> bool:
    auth_headers = {**HEADERS, "Authorization": f"Bearer {token}"}
    model_filename = get_model_filename(model_id)
    gpu_type = "Iluvatar_bi-150"
    config_content = f"""docker_image: git.modelhub.org.cn:9443/enginex-iluvatar/iluvatar-llama.cpp:b7516-bi150
 nv_docker_image: harbor-contest.4pd.io/luxinlong02/llama-cpp:b7003-cuda-full-12.3
 framework: llamacpp
 storage: gpfs
 modelhub_options:
  srcRelativePath: leaderboard/modelHubXC/{model_id}
  mountPoint: /model
 max_model_len: 4096
 sut_config:
  gpu_num: 1
  values:
    command: ['/app/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers','128', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off', '--no-mmap', '--sync-to-temp']
 ref_config:
  gpu_num: 1
  values:
    command: ['/workspace/llama.cpp/build/bin/llama-server','--model', '/model/{model_filename}', '--alias', 'llm', '--threads', '20','--n-gpu-layers','128', '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000', '--jinja', '--flash-attn', 'off']
 """
    task_data = {
        "contestApiToken": CONTEST_API_TOKEN,
        "contributors": CONTRIBUTORS,
        "gpuTypes": [gpu_type],
        "taskType": TASK_TYPE,
        "modelId": model_id,
        "strategyId": STRATEGY_ID,   # 平台要求
        "submissionConfig": [{
            "config": config_content,
            "gpuType": gpu_type,
            "taskType": TASK_TYPE
        }]
    }
    print(f"📤 提交测试任务 (bi150): {model_id}", flush=True)
    try:
        resp = requests.post(BASE_URL + SUBMIT_TEST_TASK_ENDPOINT, json=task_data, headers=auth_headers, timeout=15)
        if resp.status_code == 200:
            result = resp.json()
            if result.get("code") == 0:
                task_id = result.get("data", {}).get("taskId")
                print(f"✅ 测试任务提交成功! Task ID: {task_id}", flush=True)
                return True
            else:
                print(f"❌ 测试任务业务错误: {result.get('message')}", flush=True)
                return False
        else:
            print(f"❌ 测试任务 HTTP 错误: {resp.status_code} - {resp.text}", flush=True)
            return False
    except Exception as e:
        print(f"💥 提交测试任务异常 ({model_id}): {e}", flush=True)
        return False
 # ══════════════════════════════════════════════════════════
 #  业务逻辑：动态流水线（下载 → 提交验证任务）
 # ══════════════════════════════════════════════════════════
 def _run_worker():
    _state["started_at"] = datetime.utcnow().isoformat()
-    _state["phase"] = "submitting"
+    _state["phase"] = "running"
    successful: List[Tuple[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:
        if _shutdown.is_set():
            break
        ok, task_id = _submit_task(token, model_id)
        if ok:
            _state["submitted"] += 1
            successful.append((task_id, 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:
+        token = login()
-            for tid, mid in successful:
+    except Exception as e:
-                f.write(f"{tid}\t{mid}\n")
+        print(f"[worker] 登录失败: {e}", flush=True)
        _state["phase"] = "error"
        return
    pending_models = list(ALL_MODEL_IDS)   # 尚未开始下载的模型
    active_models: Set[str] = set()        # 当前正在下载的模型
    completed_results = {}                 # model_id -> status
    print(f"🚀 总共 {len(pending_models)} 个模型待下载。最大并发数: {MAX_CONCURRENT_DOWNLOADS}\n", flush=True)
    while (pending_models or active_models) and not _shutdown.is_set():
        # 1. 检查活跃任务状态
        for model_id in list(active_models):
            if _shutdown.is_set():
                break
            status = check_model_status(token, model_id)
            if status in ("SUCCESS", "FAILED"):
                completed_results[model_id] = status
                active_models.remove(model_id)
                print(f"⏹️  {model_id} 完成，状态: {status}", flush=True)
                if status == "SUCCESS":
                    _state["download_success"] += 1
                    # 下载成功后立即提交该模型的验证任务
                    if submit_test_task(token, model_id):
                        _state["submitted"] += 1
                        print(f"🧪 已为 {model_id} 提交 hygon 验证任务", flush=True)
                    else:
                        _state["submit_failed"] += 1
                        print(f"⚠️ {model_id} hygon 验证任务提交失败", flush=True)
                    if submit_test_task_bi150(token, model_id):
                        _state["submitted"] += 1
                        print(f"🧪 已为 {model_id} 提交 bi150 验证任务", flush=True)
                    else:
                        _state["submit_failed"] += 1
                        print(f"⚠️ {model_id} bi150 验证任务提交失败", flush=True)
                else:
                    _state["download_failed"] += 1
        # 2. 补充新任务（最多补到 MAX_CONCURRENT_DOWNLOADS 个）
        while len(active_models) < MAX_CONCURRENT_DOWNLOADS and pending_models and not _shutdown.is_set():
            next_model = pending_models.pop(0)
            if create_download_task(token, next_model):
                active_models.add(next_model)
                print(f"▶️  启动下载: {next_model} (当前活跃: {len(active_models)})", flush=True)
            else:
                # 创建失败也视为完成（避免卡住）
                completed_results[next_model] = "CREATE_FAILED"
                _state["download_failed"] += 1
                print(f"❌ 创建失败: {next_model}", flush=True)
        _state["downloading"] = sorted(active_models)
        # 3. 稍作等待，避免频繁查询（可被 shutdown 信号打断）
        if active_models or pending_models:
            _shutdown.wait(CHECK_INTERVAL_SECONDS)
    print("\n✅ 所有模型处理完毕！\n", flush=True)
    # 4. 收集所有成功下载的模型ID并写入结果文件
    success_models = [
        mid for mid, status in completed_results.items()
        if status == "SUCCESS"
    ]
    try:
        with open("downloaded_success_models.txt", "w", encoding="utf-8") as f:
            for mid in success_models:
                f.write(f"{mid}\n")
    except Exception:
        pass
    print("🎉 下载成功的模型ID列表：", flush=True)
    print("[", flush=True)
    for mid in success_models:
        print(f'    "{mid}",', flush=True)
    print("]", flush=True)
    _state["downloading"] = []
    _state["finished_at"] = datetime.utcnow().isoformat()
    _state["phase"] = "done"
    print(
-        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}",
+        f"[worker] 完成  download_success={_state['download_success']}  "
        f"download_failed={_state['download_failed']}  "
        f"submitted={_state['submitted']}  submit_failed={_state['submit_failed']}",
        flush=True,
    )
-    # 提交完成后继续保持进程存活，等待平台停止
+    # 流水线完成后继续保持进程存活，等待平台停止
 # ══════════════════════════════════════════════════════════
 #  入口
@@ -545,7 +504,7 @@ def main():
    http_thread = threading.Thread(target=_run_http, daemon=False)
    http_thread.start()
-    # 提交任务线程
+    # 流水线线程
    worker_thread = threading.Thread(target=_run_worker, daemon=True)
    worker_thread.start()