init

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>

.gitignore

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

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

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")
-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.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODI3MzA4MTQsImlhdCI6MTc4MjEyNjAxNH0.ZBMLXxi9n_g4_drUUuciWFipViMZmJzMJLab5dL0WM4"
 CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
+HF_TOKEN          = "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"
 CONTRIBUTORS      = "zhoushasha"
-GPU_TYPE          = "Cambricon_mlu-370-x8"
 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
 
@@ -36,468 +50,74 @@ HTTP_PORT = 8080
 #  模型列表
 # ══════════════════════════════════════════════════════════
 ALL_MODEL_IDS = [
-    # "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3",
+
-    # "migtissera/SynthIA-7B-v1.3",
+    
-    # "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T",
+    
-    # "bigscience/bloomz-1b1",
+    "mradermacher/Qwen2.5-3B-instruct-argus-v3-GGUF",
-    # "EleutherAI/pythia-6.9b-deduped",
+    "mradermacher/NeuralsirkrishnaShadow_OgnoExperiment27-GGUF",
-    # "AvitoTech/avibe",
+    "mradermacher/GREEN-RadLlama2-7b-GGUF",
-    # "Enoch/llama-7b-hf",
+    "mradermacher/snakmodel-7b-instruct-GGUF",
-    # "asingh15/qwen-abs-verl-sft-rephrased-lr5e6-ep1-0109",
+    "mradermacher/BioMistral-7B-Starling-SLERP-GGUF",
-    # "PrimeIntellect/INTELLECT-1",
+    "mradermacher/Marco-Llama-3.2-3B-GGUF",
-    # "neuralmagic/starcoder2-3b-quantized.w8a8",
+    "mradermacher/Qwen2.5-1.5B-Instruct-Open-R1-GRPO-GGUF",
-    # "Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B",
+    "mradermacher/Qwen2.5-0.5B-Distill-Fast-GGUF",
-    # "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+    "mradermacher/chomsky_16_bit_model-GGUF",
-    # "neuralmagic/Llama-2-7b-chat-quantized.w4a16",
+    "mradermacher/Mistral-7B-DFT-GGUF",
-    # "neuralmagic/starcoder2-15b-quantized.w8a8",
+    "mradermacher/mergekit-task_arithmetic-qjeuqjw-GGUF",
-    # "DAMO-NLP-SG/Qwen2.5-7B-LongPO-128K",
+    "mradermacher/M7Yamshadowexperiment28_Experiment27Inex12-GGUF",
-    # "guardrail/llama-2-7b-guanaco-instruct-sharded",
+    "mradermacher/YamshadowStrangemerges_32_Experiment28Inex12-GGUF",
-    # "shenzhi-wang/Gemma-2-27B-Chinese-Chat",
+    "mradermacher/MeliodasPercival_01_Experiment29Pastiche-GGUF",
-    # "pavankumarbalijepalli/phi2-sqlcoder",
+    "mradermacher/Llama-3-6B-v0-GGUF",
-    # "neph1/bellman-7b-mistral-instruct",
+    "mradermacher/Excalibur-7b-DPO-GGUF",
-    # "neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a16",
+    "mradermacher/Llama-2-7b-Indian-Law-GGUF",
-    # "neuralmagic/Qwen2-7B-Instruct-quantized.w8a8",
+    "mradermacher/StarlingHermes-2.5-Mistral-7B-slerp-GGUF",
-    # "lamm-mit/BioinspiredLLM",
+    "mradermacher/DeepThinker-7B-Sce-v1-GGUF",
-    # "neuralmagic/Qwen2-7B-Instruct-quantized.w8a16",
+    "mradermacher/Alif-Llama-EXP2-GGUF",
-    # "dataopsnick/Qwen3-4B-Instruct-2507-zip-rc",
+    "mradermacher/qwen-2.5-1.5B-Rasa-GGUF",
-    # "huihui-ai/MicroThinker-3B-Preview",
+    "mradermacher/German_RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI-GGUF",
-    # "OrionStarAI/Orion-14B-Base",
+    "mradermacher/MFANN-phigments-slerp-V3.2-GGUF",
-    # "georgesung/llama3_8b_chat_uncensored",
+    "mradermacher/AdityaGPT-GGUF",
-    # "FreedomIntelligence/RAG-Instruct-Llama3-3B",
+    "mradermacher/Jaja-small-v4-GGUF",
-    # "Aryanne/WestSenzu-Swap-7B",
+    "mradermacher/Jaja-medium-v1-GGUF",
-    # "Josephgflowers/Cinder-Phi-2-Test-1",
+    "mradermacher/astrollama-2-7b-base_abstract-GGUF",
-    # "FreedomIntelligence/Apollo-6B",
+    "mradermacher/Llasagna-v0.1-GGUF",
-    # "Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2",
+    "mradermacher/DeepThinker-v-GGUF",
-    # "Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test",
+    "mradermacher/Jaja-small-v3-GGUF",
-    # "247labs/Llama-2-7b-Verse-Bot",
+    "mradermacher/Llama-3.2-1B-FC-v1.2-think-GGUF",
-    # "praneethposina/customer_support_bot",
+    "mradermacher/Deepseek-Qwen2.5-1.5B-Redistil-GGUF",
-    # "KBlueLeaf/TIPO-200M",
+    
-    # "norallm/normistral-11b-warm",
+    
-    # "theprint/Boptruth-Agatha-7B",
+    
-    # "ericflo/Llama-3.1-8B-ContinuedTraining2-FFT",
+    
-    # "okwinds/OpenR1-Qwen-7B",
+    
-    # "ruohuaw/deepquery-3b-sft",
+    
-    # "theprint/Boptruth-NeuralMonarch-7B",
-    # "MaziyarPanahi/calme-3.1-qwenloi-3b",
-    # "alperiox/trendyol-7b-base-v1-mtLoRA_entr",
-    # "theprint/phi-3-mini-4k-python",
-    # "uukuguy/speechless-nl2sql-ds-6.7b",
-    # "uukuguy/speechless-coder-ds-6.7b",
-    # "tybrs/llama-guard-quant",
-    # "Josephgflowers/TinyLlama-3T-Cinder-v1.3",
-    # "mlabonne/Darewin-7B-v2",
-    # "TeichAI/Qwen3-1.7B-Gemini-2.5-Flash-Lite-Preview-Distill",
-    # "TeichAI/Nemotron-Orchestrator-8B-DeepSeek-v3.2-Speciale-Distill",
-    # "shadowml/BeagSake-7B",
-    # "lex-hue/Delexa-7b",
-    # "h2oai/h2o-danube3-500m-chat",
-    # "bigcode/gpt_bigcode-santacoder",
-    # "openlm-research/open_llama_7b",
-    # "upstage/SOLAR-10.7B-v1.0",
-    # "prithivMLmods/Phi-3.5-Mini-Xalate",
-    # "prithivMLmods/Qwen3-Bifrost-SOL-4B-GUFF",
-    # "prithivMLmods/Volans-Opus-14B-Exp",
-    # "prithivMLmods/Viper-OneCoder-UIGEN",
-    # "prithivMLmods/Tucana-Opus-14B-r999",
-    # "prithivMLmods/Sombrero-Opus-14B-Sm5",
-    # "prithivMLmods/Sombrero-Opus-14B-Sm4",
-    # "prithivMLmods/Reasoning-SmolLM2-135M",
-    # "prithivMLmods/Sombrero-Opus-14B-Sm1",
-    # "prithivMLmods/LwQ-10B-Instruct",
-    # "prithivMLmods/Sombrero-Opus-14B-Elite5",
-    # "prithivMLmods/Eridanus-Opus-14B-r999",
-    # "prithivMLmods/Equuleus-Opus-14B-Exp",
-    # "prithivMLmods/Epimetheus-14B-Axo",
-    # "prithivMLmods/Phi-4-Math-IO",
-    # "prithivMLmods/Omni-Reasoner4-Merged",
-    # "prithivMLmods/Pegasus-Opus-14B-Exp",
-    # "prithivMLmods/Elita-1",
-    # "prithivMLmods/Delta-Pavonis-Qwen-14B",
-    # "prithivMLmods/Nu2-Lupi-Qwen-14B",
-    # "prithivMLmods/Coma-II-14B",
-    # "MaziyarPanahi/calme-2.7-qwen2-7b",
-    # "prithivMLmods/Monocerotis-V838-14B",
-    # "prithivMLmods/Calcium-Opus-14B-Merge",
-    # "prithivMLmods/Calcium-Opus-14B-Elite3",
-    # "prithivMLmods/Calcium-Opus-14B-Elite2-R1",
-    # "prithivMLmods/Calcium-Opus-14B-Elite2",
-    # "prithivMLmods/Calcium-Opus-14B-Elite-Stock",
-    # "prithivMLmods/Megatron-Opus-14B-2.1",
-    # "prithivMLmods/Blaze.1-27B-Reflection",
-    # "prithivMLmods/Megatron-Corpus-14B-Exp.v2",
-    # "prithivMLmods/Megatron-Corpus-14B-Exp",
-    # "GAIR/autoj-bilingual-6b",
-    # "TheBloke/airoboros-7b-gpt4-fp16",
-    # "Undi95/Mistral-11B-OmniMix9",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C016-pretrain-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C017-instruct-v0.2",
-    # "mlabonne/NeuralDarewin-7B",
-    # "0xgr3y/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-tall_tame_panther",
-    # "openlm-research/open_llama_3b_v2",
-    # "Nobitaxi/InternLM2-chat-7B-SQL",
-    # "testUser/Qwen3-1.7b-Medical-R1-sft",
-    # "mlabonne/Zebrafish-7B",
-    # "mlabonne/NeuralPipe-7B-slerp",
-    # "laion/openthoughts-4-code-qwen3-32b-annotated-7k_qwen3-1.7B_10k",
-    # "Fengshenbang/Ziya-LLaMA-13B-v1.1",
-    # "arcee-ai/Saul-Instruct-Mistral-7B-Instruct-v0.2-Slerp",
-    # "arcee-ai/Saul-Instruct-Clown-7b",
-    # "prithivMLmods/Megatron-Opus-7B-Exp",
-    # "Vikhrmodels/QVikhr-3-8B-Instruction",
-    # "TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16",
-    # "TheBloke/UltraLM-13B-fp16",
-    # "PocketDoc/Dans-TotSirocco-7b",
-    # "LLM-Research/Meta-Llama-3.1-8B",
-    # "Qwen/Qwen2.5-Coder-32B",
-    # "Qwen/Qwen2.5-7B-Instruct-1M",
-    # "Qwen/Qwen2-57B-A14B-Instruct",
-    # "Qwen/Qwen2.5-14B-Instruct-1M",
-    # "Qwen/Qwen-1_8B-Chat",
-    # "Qwen/Qwen1.5-MoE-A2.7B-Chat",
-    # "Qwen/Qwen1.5-MoE-A2.7B",
-    # "Qwen/Qwen1.5-14B-Chat",
-    # "Qwen/Qwen1.5-14B",
-    # "Qwen/Qwen-14B",
-    # "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
-    # "TheBloke/tulu-13B-fp16",
-    # "TheBloke/Kimiko-Mistral-7B-fp16",
-    # "TheBloke/Llama-2-13B-fp16",
-    # "mlabonne/Monarch-7B",
-    # "TheBloke/tulu-7B-fp16",
-    # "01ai/Yi-9B",
-    # "TheBloke/koala-7B-HF",
-    # "AI-ModelScope/txgemma-2b-predict",
-    # "LLM-Research/OLMo-7B-0724-SFT-hf",
-    # "JsonZhang02/Llama3.2-1B-PCL",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C019-instruct-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C016-instruct-v0.2",
-    # "FreedomIntelligence/AceGPT-v1.5-13B-Chat",
-    # "MediaTek-Research/Breeze-7B-Base-v0_1",
-    # "OpenBuddy/openbuddy-llama3-8b-v21.1-8k",
-    # "HIT-TMG/Mixtral_13B_Chat_RAG-Reader",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C014-pretrain-v0.2",
-    # "arcee-ai/arcee-lite",
-    # "X-D-Lab/MindChat-Qwen2-4B",
-    # "mlabonne/NeuralMonarch-7B",
-    # "ibm-granite/granite-3b-code-instruct-2k",
-    # "LLM-Research/OLMo-7B-Twin-2T-hf",
-    # "PocketDoc/Dans-AdventurousWinds-Mk2-7b",
-    # "LLM-Research/Qwen2-Math-7B",
-    # "MediaTek-Research/Breeze-7B-Base-v1_0",
-    # "LLM-Research/layerskip-llama2-13B",
-    # "prithivMLmods/TESS-QwenRe-1.5B",
-    # "prithivMLmods/Octantis-QwenR1-1.5B",
-    # "prithivMLmods/Qwen3-1.7B-ft-bf16",
-    # "prithivMLmods/Theta-Crucis-0.6B-Turbo1",
-    # "prithivMLmods/Omega-Qwen3-Atom-8B",
-    # "prithivMLmods/Mintaka-Qwen3-1.6B-V3.1",
-    # "NousResearch/Yarn-Llama-2-7b-64k",
-    # "prithivMLmods/Panacea-MegaScience-Qwen3-1.7B",
-    # "prithivMLmods/TOI-157-Phi-4-Reasoning-Mini",
-    # "prithivMLmods/Vulpecula-4B",
-    # "LLM-Research/OLMo-7B-0424-hf",
-    # "LLM-Research/OLMo-7B-hf",
-    # "LLM-Research/OLMo-7B-SFT-hf",
-    # "AI-ModelScope/starcoder2-7b",
-    # "LLM-Research/OLMo-7B-0724-hf",
-    # "OpenBMB/BitCPM4-1B",
-    # "LLM-Research/truthfulqa-truth-judge-llama2-7B",
-    # "LLM-Research/OLMo-1B-0724-hf",
-    # "HIT-TMG/Qwen1.5-14B-Chat_RAG-Reader",
-    # "OpenBMB/MiniCPM4-MCP",
-    # "AI-ModelScope/sqlcoder-7b-2",
-    # "FuseAI/OpenChat-3.5-7B-SOLAR-v2.0",
-    # "JsonZhang02/Llama3.2-1B-SFT",
-    # "MaziyarPanahi/neural-chat-7b-v3-2-Mistral-7B-Instruct-v0.1",
-    # "MaziyarPanahi/SauerkrautLM-7b-HerO-Mistral-7B-Instruct-v0.1",
-    # "prithivMLmods/Segue-Qwen3_DeepScaleR-Preview",
-    # "NovaSky-AI/Sky-T1-7B-Zero",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C020-pretrain-v0.2",
-    # "NovaSky-AI/Sky-T1-7B-step2",
-    # "NousResearch/CodeLlama-7b-hf-flash",
-    # "LLM-Research/layerskip-llama3-8B",
-    # "LLM-Research/OLMo-1B-hf",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C018-pretrain-v0.2",
-    # "NousResearch/CodeLlama-7b-Instruct-hf-flash",
-    # "Nexusflow/NexusRaven-V2-13B",
-    # "AI-ModelScope/NuExtract-v1.5",
-    # "NousResearch/Nous-Capybara-3B-V1.9",
-    # "NousResearch/Nous-Capybara-7B-V1",
-    # "NousResearch/Yarn-Solar-10b-32k",
-    # "LLM-Research/Llama-Guard-4-12B",
-    # "OpenPipe/gemma-3-4b-it-text-only-2",
-    # "OpenPipe/Deductive-Reasoning-Qwen-14B",
-    # "OpenPipe/gemma-3-12b-it-text-only",
-    # "AI-MO/NuminaMath-7B-CoT",
-    # "GAIR/Abel-7B-001",
-    # "prithivMLmods/Novaeus-Promptist-7B-Instruct",
-    # "SakanaAI/EvoLLM-JP-v1-7B",
-    # "FreedomIntelligence/Apollo-1.8B",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C013-instruct-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C013-pretrain-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C015-pretrain-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C014-instruct-v0.2",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C021-instruct-v0.2",
-    # "NousResearch/Meta-Llama-3.1-8B",
-    # "OpenPipe/Qwen3-14B-Instruct",
-    # "unsloth/OpenHermes-2.5-Mistral-7B",
-    # "OpenBuddy/openbuddy-mistral-22b-v21.1-32k",
-    # "FlyDutch/telechat2-7b-Cot",
-    # "HuggingFaceH4/mistral-7b-sft-alpha",
-    # "PAI/pai-qwen1_5-7b-doc2qa",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C018-instruct-v0.2",
-    # "Magpie-Align/Llama-3-8B-Tulu-330K",
-    # "prithivMLmods/Blaze.1-27B-Preview",
-    # "allenai/OLMo-7B-0424-SFT-hf",
-    # "mlabonne/Meta-Llama-3-8B",
-    # "LLM-Research/layerskip-codellama-7B",
-    # "prithivMLmods/Sculptor-Qwen3_Med-Reasoning",
-    # "prithivMLmods/SmolLM2-360M-Grpo-r999",
-    # "prithivMLmods/SmolLM2-1.7B-Open-Thought",
-    # "LLM-Research/open-instruct-llama2-sharegpt-7b",
-    # "prithivMLmods/SmolLM2_135M_Grpo_Checkpoint",
-    # "OpenBuddy/openbuddy-qwen2.5llamaify-14b-v23.1-200k",
-    # "OpenBuddy/openbuddy-zero-3b-v21.2-32k",
-    # "YeungNLP/firefly-llama2-7b-chat",
-    # "OpenBuddy/openbuddy-zero-14b-v22.3-32k",
-    # "OpenBuddy/openbuddy-yi1.5-9b-v21.1-32k",
-    # "FuseAI/OpenChat-3.5-7B-Starling-v2.0",
-    # "prithivMLmods/Qwen-7B-Distill-Reasoner",
-    # "FuseAI/OpenChat-3.5-7B-InternLM-v2.0",
-    # "prithivMLmods/Galactic-Qwen-14B-Exp1",
-    # "prithivMLmods/Sombrero-R1-14B-Elite13",
-    # "prithivMLmods/Sombrero-Opus-14B-Elite13",
-    # "TheBloke/Planner-7B-fp16",
-    # "AI-ModelScope/speed-synthesis-8b-senior",
-    # "PocketDoc/Dans-AdventurousWinds-7b",
-    # "MaziyarPanahi/calme-3.2-baguette-3b",
-    # "MaziyarPanahi/calme-3.2-instruct-3b",
-    # "IntervitensInc/intv_ai_mk11",
-    # "prithivMLmods/Muscae-Qwen3-UI-Code-4B",
-    # "NousResearch/Llama-2-7b-hf",
-    # "prithivMLmods/Pocket-Llama-3.2-3B-Instruct",
-    # "OpenBuddy/openbuddy-openllama-13b-v7-fp16",
-    # "LLM-Research/WildLlama-7b-assistant-only",
-    # "prithivMLmods/Raptor-X2",
-    # "OpenBuddy/openbuddy-qwen1.5-14b-v20.1-32k",
-    # "NaniDAO/Meta-Llama-3.1-8B-Instruct-ablated-v1",
-    # "LLM-Research/OLMo-7B-Instruct-hf",
-    # "OpenBuddy/openbuddy-zen-3b-v21.2-32k",
-    # "OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k",
-    # "LLM-Research/llama2-7b-WildJailbreak",
-    # "JunHowie/MiniCPM4-8B",
-    # "OpenBuddy/openbuddy-coder-15b-v10-bf16",
-    # "JunHowie/MiniCPM4-0.5B",
-    # "OpenDevin/CodeQwen1.5-7B-OpenDevin",
-    # "OpenBuddy/openbuddy-mistral-10b-v17.1-32k",
-    # "PAI/DistilQwen2.5-DS3-0324-7B",
-    # "OpenBuddy/openbuddy-llama2-13b64k-v15",
-    # "OpenBuddy/openbuddy-falcon-7b-v5-fp16",
-    # "NousResearch/Hermes-2-Theta-Llama-3-8B",
-    # "NousResearch/Hermes-2-Pro-Mistral-7B",
-    # "OpenBuddy/openbuddy-openllama-7b-v5-fp16",
-    # "PAI/DistillQwen-ThoughtY-8B",
-    # "BSC-LT/salamandra-2b",
-    # "pfnet/nekomata-7b-pfn-qfin-inst-merge",
-    # "BSC-LT/experimental7b-rag-instruct",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C019-pretrain-v0.2",
-    # "OpenBuddy/OpenBuddy-R10528DistillQwen-14B-v27.4-200K",
-    # "OpenBuddy/OpenBuddy-R10528DistillQwen-14B-v27.1",
-    # "OpenBuddy/SimpleChat-4B-V1",
-    # "AI-ModelScope/granite-8b-code-base-4k",
-    # "mlabonne/NeuralHermes-2.5-Mistral-7B",
-    # "BSC-LT/experimental7b-rag",
-    # "prithivMLmods/SmolLM2_135M_Grpo_Gsm8k",
-    # "OpenBuddy/openbuddy-zen-3b-v21.1-32k",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C015-instruct-v0.2",
-    # "prithivMLmods/QwQ-LCoT1-Merged",
-    # "mlabonne/NeuralBeagle14-7B",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C020-instruct-v0.2",
-    # "mlabonne/NeuralMarcoro14-7B",
-    # "PKU-Alignment/ProgressGym-HistLlama3-8B-C017-pretrain-v0.2",
-    # "FuseAI/OpenChat-3.5-7B-Mixtral-v2.0",
-    # "mlabonne/FrankenMonarch-7B",
-    # "stabilityai/stablelm-tuned-alpha-3b",
-    # "prithivMLmods/Viper-Coder-v1.5-r999",
-    # "prithivMLmods/Galactic-Qwen-14B-Exp2",
-    # "HuggingFaceTB/cosmo-1b",
-    # "LLM-Research/WildLlama-7b-user-assistant",
-    # "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
-    # "prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B",
-    # "LLM-Research/OLMo-7B-0424-SFT-hf",
-    # "huihui-ai/MicroThinker-1B-Preview",
-    # "OpenBuddy/openbuddy-openllama-3b-v10-bf16",
-    # "LLM-Research/digital-socrates-13b",
-    # "prithivMLmods/Viper-Coder-v1.6-r999",
-    # "prithivMLmods/Magpie-Qwen-DiMind-1.7B",
-    # "BAAI/CareBot_Medical_multi-llama3-8b-base",
-    # "NousResearch/Meta-Llama-3-8B",
-    # "OpenBuddy/openbuddy-llama2-13b-v15p1-64k",
-    # "NousResearch/Yarn-Mistral-7b-64k",
-    # "PrimeIntellect/DeepSeek-R1-Distill-Qwen-1.5B",
-    # "Undi95/Meta-Llama-3-8B-Instruct-hf",
-    # "FuseAI/OpenChat-3.5-7B-Mixtral",
-    # "prithivMLmods/Viper-Coder-Hybrid-v1.3",
-    # "OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k",
-    # "LLM-Research/mistral-7b",
-    # "OpenBuddy/openbuddy-qwen2.5llamaify-14b-v23.3-200k",
-    # "prithivMLmods/Viper-Coder-HybridMini-v1.3",
-    # "OpenBuddy/openbuddy-atom-13b-v9-bf16",
-    # "OpenBuddy/openbuddy-llama3.2-3b-v23.2-131k",
-    # "ibm-granite/granite-3b-code-instruct-128k",
-    # "PierreZCW/Breeze-7B-Instruct-v1_0",
-    # "mlabonne/Marcoro14-7B-slerp",
-    # "AI-ModelScope/openbuddy-falcon-7b-v15-fp16",
-    # "AI-ModelScope/falcon-7b",
-    # "BAAI/AquilaChat2-7B",
-    # "PrimeIntellect/Qwen3-0.6B",
-    # "OuteAI/Lite-Oute-1-65M-Instruct",
-    # "AI-ModelScope/granite-3b-code-instruct-128k",
-    # "PrimeIntellect/Qwen3-8B",
-    # "OpenBuddy/openbuddy-falcon-7b-v6-bf16",
-    # "MaziyarPanahi/calme-3.1-instruct-3b",
-    # "LLM-Research/open-instruct-llama2-sharegpt-dpo-7b",
-    # "PocketDoc/Dans-PersonalityEngine-v1.0.0-8b",
-    # "FuseAI/FuseChat-Llama-3.1-8B-Instruct",
-    # "OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k",
-    # "OpenBuddy/openbuddy-deepseekcoder-6b-v16.1-32k",
-    # "HuggingFaceTB/SmolLM-1.7B",
-    # "LLM-Research/Llama-4-Scout-17B-16E-Instruct",
-    # "argilla/distilabeled-Marcoro14-7B-slerp-full",
-    # "HuggingFaceTB/SmolLM2-1.7B",
-    # "argilla/distilabeled-Marcoro14-7B-slerp",
-    # "l3utterfly/open-llama-3b-v2-layla",
-    "motherduckdb/DuckDB-NSQL-7B-v0.1",
-    "ilkayO/Karga-2B-Thinking",
-    "vtgh1602/legal-llm-v1-qwen25-7b-merged",
-    "Alelcv27/Llama3.2-3B-Dare-Math-Code",
-    "adamo1139/aya-expanse-8b-ungated",
-    "Alelcv27/Llama3.2-3B-TIES-Math-Code",
-    "chanwit/flux-7b-v0.1",
-    "nfaheem/Marcoroni-7b-DPO-Merge",
-    "trillionlabs/android_control_ER_index_1000",
-    "biodatlab/ec-raft",
-    "LLM4Binary/llm4decompile-1.3b-v1.5",
-    "LLM4Binary/llm4decompile-1.3b-v2",
-    "Ramikan-BR/tinyllama-coder-py-v11",
-    "maywell/Synatra-7B-Instruct-v0.2",
-    "Tapask/telecom-oss-8b-merged",
-    "LeoLM/leo-hessianai-7b-chat-bilingual",
-    "malhajar/Mistral-7B-v0.2-meditron-turkish",
-    "maywell/PiVoT-SOLAR-10.7B-RP",
-    "allenai/intent-aware-lfqa-llama3-8b-intent-explicit",
-    "Skywork/Skywork-Critic-Llama-3.1-8B",
-    "proxectonos/Llama-3.1-Carballo-Instr3",
-    "maywell/Synatra-10.7B-v0.4",
-    "proxectonos/Carballo-Legal",
-    "UmbrellaInc/Wesker-Project-3.2-1B",
-    "inclusionAI/AReaL-boba-2-32B",
-    "migtissera/Synthia-v3.0-11B",
-    "vicgalle/RoleBeagle-11B",
-    "ehristoforu/RQwen-v0.1",
-    "llm-jp/llm-jp-4-32b-a3b-base",
-    "bigcode/starcoder-co-format",
-    "lyogavin/Anima-7B-100K",
-    "raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE",
-    "mii-community/zefiro-7b-dpo-ITA",
-    "thirdeyeai/SmolLM2-1.7B-Instruct-Uncensored",
-    "LeoLM/leo-hessianai-7b",
-    "ibm-granite/GneissWeb.7B_ablation_model_on_350B_FineWeb.Edu.seed3",
-    "Xtra-Computing/XtraGPT-7B",
-    "bigcode/starcoder-co-target",
-    "uproai/Rose-2x7B",
-    "ReDiX/Qwen2.5-0.5B-Instruct-ITA",
-    "CLMBR/binding-c-command-transformer-0",
-    "MaLA-LM/emma-500-llama3.1-8b-bi",
-    "freewheelin/free-solar-evo-v0.13",
-    "bigcode/starcoder-xo",
-    "DavidAU/Rocinante-X-12B-v1-Heretic-Uncensored",
-    "yam-peleg/Experiment22-7B",
-    "jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64",
-    "ypwang61/One-Shot-RLVR-Qwen2.5-Math-1.5B-pi1",
-    "Vladimirlv/ru-promptriever-qwen3-4b-attn",
-    "starlight-ai/MedSearcher-1.7B",
-    "lzumot/MODULARMOJO_Mistral_V1",
-    "hfl/chinese-alpaca-2-1.3b-rlhf",
-    "CloneBO/OracleLM",
-    "HelpingAI/MediKAI",
-    "VillanovaAI/Villanova-2B-2603",
-    "princeton-nlp/SWE-Llama-13b",
-    "MBZUAI/bactrian-x-llama-13b-merged",
-    "sbordt/OLMo-2-1B-Mid",
-    "Kyleyee/rDPO_hh-seed2",
-    "EleutherAI/early_unlearning_annealing_baseline_ga_v3_interleaved_1_in_50_original_wmdp_papers",
-    "hector-gr/RLCR-5x-priority-overconf-math",
-    "Neura-Tech-AI/Neuron-14B",
-    "pandaman007/llama-3.1-8b-instruct-sycophantic-steered-L12-a5",
-    "idopinto/qwen3-14b-nt-gen-inv-sft-v2.2-full",
-    "katanemo/Arch-Function-3B",
-    "sbordt/OLMo-2-1B-1x-WD0-LR16",
-    "sbordt/OLMo-2-1B-1x-WD0-LR08",
-    "sbordt/OLMo-2-1B-1x-WD0-LR32",
-    "arkoda/arkoda-7b-v7-2-1",
-    "Kazuki1450/Qwen3-1.7B-Base_dsum_3_6_0p8_0p0_1p0_grpo_dr_grpo_42_rule",
-    "EleutherAI/deep_aversion_pretraining_filtered_gdiff_v1_interleaved_1_in_100_gclip-0.5",
-    "W-61/llama-3-8b-base-ultrachat-sft-4xh100",
-    "xw1234gan/olympiads_Main_fixed_BaseAnchor_3B_step_8",
-    "kenny2021/episodic-nothink4-merged",
-    "yilmazzey/qwen2_5_7b-abstract-finetuned-ep2-b8",
-    "kenny2021/episodic-nothink4-simpo-merged",
-    "maxim1eu/amelia-32b-dpo-merged",
-    "open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d1_g0.25_ep5",
-    "Cisco1963/llmplasticity-en_de_instant_0.125_1-seed42",
-    "Cisco1963/llmplasticity-zh_en_linear_0.25_1-seed42",
-    "tyson0420/stack_llama-clang",
-    "saishf/Fett-uccine-11B-Experiment",
-    "Cisco1963/llmplasticity-fi_de_instant_0.5_1-seed42",
-    "Cisco1963/llmplasticity-en_de_linear_0.125_1-seed42",
-    "Cisco1963/llmplasticity-zh_fi_instant_0.125_1-seed42",
-    "Cisco1963/llmplasticity-fi_en_instant_0.125_1-seed42",
-    "unsloth/codellama-7b",
-    "MCult01/muse-qwen3-8b",
-    "zero9tech/Qwen3-4B-Data-Science-Insight-TR-16.2K",
-    "LorenaYannnnn/bold_formatting-Qwen3-0.6B-baseline_all_tokens-seed_2",
-    "LorenaYannnnn/bold_formatting-Qwen3-0.6B-baseline_all_tokens-seed_1",
-    "yufeng1/Olmo3-7B-textsummarization-type6-e1-alpha0_625-2",
-    "Haeryz/Deepseek-TPPO-V2-Temp",
-    "yufeng1/OpenThinker-7B-type6-e1-max-alpha0_3125-2",
-    "FinaPolat/Qwen3_8B_openED",
-    "myfi/parser_model_ner_4.13_ep4",
-    "vimalnar/aware-ai-2nd",
-    "flammenai/flammen11-mistral-7B",
-    "Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct",
-    "penfever/kimi-k2-swesmith_with_plain_docker-sandboxes-maxeps-32k",
-    "Narsil/amall-7b",
-    "kmseong/llama2_7b_chat-MBPP-FT-lr5e-5",
-    "Cisco1963/llmplasticity-en_zh_linear_0.125_1-seed42",
-    "Cisco1963/llmplasticity-zh_en_instant_0.25_1-seed42",
-    "Cisco1963/llmplasticity-zh_fi_instant_0.5_1-seed42",
-    "Cisco1963/llmplasticity-de_en_linear_0.5_1-seed42",
-    "RedHatAI/SmolLM-360M-Instruct-quantized.w8a8",
-    "togethercomputer/Llama-2-7B-32K-Instruct",
-    "Undi95/ReMM-v2-Kimiko-v2-13B",
-    "jackf857/qwen3-8b-base-new-dpo-hh-harmless-4xh200-batch-64-q_t-0.45-s_star-0.4",
-    "YeungNLP/firefly-llama2-13b-base",
-    "gplsi/Aitana-2B-S-base-IP-1.0",
-    "princeton-nlp/Llemma-7B-32K-MathMix",
-    "darkmatter999/llama-2-7b-competetivecoding",
-    "RedHatAI/Qwen2-0.5B-Instruct-quantized.w8a8",
-    "zorobin/mistral-class-shishya-7b-ep3",
-    "vicgalle/Mixtral-7Bx2-truthy",
-    "sethuiyer/Chikuma_10.7B",
-    "360zhinao/Light-IF-14B",
-    "EscapeJeju/qwen25_1_5b_korean_unsloth",
 ]
 
+
+# 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,
-    "phase":         "starting",   # starting | submitting | done | error
+    "phase":             "starting",   # starting | running | done | error
     "total":             len(ALL_MODEL_IDS),
-    "submitted":     0,
+    "downloading":       [],           # 当前正在下载的模型
-    "failed":        0,
+    "download_success":  0,
+    "download_failed":   0,
+    "submitted":         0,            # 成功提交的验证任务数（hygon + bi150）
+    "submit_failed":     0,
     "started_at":        None,
     "finished_at":       None,
 }
@@ -537,104 +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"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
+    print(f"📥 创建下载任务: {model_id} → {filename}", flush=True)
-nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+    try:
-framework: vllm
+        resp = requests.post(BASE_URL + CREATE_DOWNLOAD_TASK_ENDPOINT, headers=auth_headers, json=payload, timeout=15)
+        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:
-  values:
   gpu_num: 1
-    env:
+  values:
-      - name: MAX_MODEL_LEN
+    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']
-        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:
+  values:
-      - name: MAX_MODEL_LEN
+    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']
-        value: 8192
-    command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
 """
-    payload = {
+    task_data = {
         "contestApiToken": CONTEST_API_TOKEN,
         "contributors": CONTRIBUTORS,
-        "gpuTypes":        [GPU_TYPE],
+        "gpuTypes": [gpu_type],
         "taskType": TASK_TYPE,
         "modelId": model_id,
-        "framework":       "vllm",
         "strategyId": STRATEGY_ID,   # 平台要求
         "submissionConfig": [{
             "config": config_content,
-            "gpuType":  GPU_TYPE,
+            "gpuType": gpu_type,
-            "taskType": TASK_TYPE,
+            "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,
-            json=payload,
-            timeout=15,
-        )
             result = resp.json()
             if result.get("code") == 0:
-            task_id = result.get("data", {}).get("id", "")
+                task_id = result.get("data", {}).get("taskId")
-            print(f"[worker] OK  {model_id}  task_id={task_id}", flush=True)
+                print(f"✅ 测试任务提交成功! Task ID: {task_id}", flush=True)
-            return True, task_id
+                return True
             else:
-            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
+                print(f"❌ 测试任务业务错误: {result.get('message')}", flush=True)
-            return False, ""
+                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"[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]] = []
+    try:
-    token = AUTH_TOKEN
+        token = login()
-    print("[worker] 使用预设 Token，跳过登录", flush=True)
+    except Exception as e:
+        print(f"[worker] 登录失败: {e}", flush=True)
+        _state["phase"] = "error"
+        return
 
-    for model_id in ALL_MODEL_IDS:
+    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
-        ok, task_id = _submit_task(token, model_id)
+            status = check_model_status(token, model_id)
-        if ok:
+            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
-            successful.append((task_id, model_id))
+                        print(f"🧪 已为 {model_id} 提交 hygon 验证任务", flush=True)
                     else:
-            _state["failed"] += 1
+                        _state["submit_failed"] += 1
+                        print(f"⚠️ {model_id} hygon 验证任务提交失败", flush=True)
 
-    # 写入结果文件
+                    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("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
+        with open("downloaded_success_models.txt", "w", encoding="utf-8") as f:
-            for tid, mid in successful:
+            for mid in success_models:
-                f.write(f"{tid}\t{mid}\n")
+                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
@@ -652,7 +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()