convert to multi-GPU submission (Biren/Cambricon/MetaX) with fresh filtered model lists

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

.gitignore

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

Dockerfile

@@ -1,6 +1,7 @@
 FROM modelhubxc-4pd.tencentcloudcr.com/xc_agent_platform/python:3.11-slim
 
-ENV PYTHONUNBUFFERED=1
+ENV PYTHONUNBUFFERED=1 
+    
 
 WORKDIR /app
 

main.py

@@ -1,7 +1,9 @@
 """
 xc_validation_strategy — 主入口
 
-启动后执行一次模型验证任务批量提交，之后保持 HTTP 服务存活。
+启动后针对 3 张 GPU 卡（Biren_166m / Cambricon_mlu-370-x8 / MetaX_c-500）
+分别批量提交各自筛选出的模型验证任务（/adminApi/async/task/create-contest-task，
+Bearer Token 认证），之后保持 HTTP 服务存活。
 同时暴露 /health（K8s 探活）和 /status（运行状态）。
 """
 
@@ -22,10 +24,9 @@ BASE_URL           = os.environ.get("BASE_URL", "https://modelhub.org.cn")
 SUBMIT_ENDPOINT    = "/adminApi/async/task/create-contest-task"
 
 # 通过 curl -X POST https://modelhub.org.cn/adminApi/user/login 获取后填入
-AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODE4NTE0NzcsImlhdCI6MTc4MTI0NjY3N30.p3uvCpG50aLNifNVVXxvzmWJahbLM5K1671FVCtj8E8"
+AUTH_TOKEN        = "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyQWNjb3VudCI6Inpob3VzaGFzaGEiLCJpZCI6MTQsInVzZXJSb2xlIjoibGVhZGVyYm9hcmQiLCJleHAiOjE3ODU3NDY3NTMsImlhdCI6MTc4NTE0MTk1M30.KwUuefNAFSNwq3_Pnaw2nef8ZC6WgsECQ_LMeQnKk2c"
 CONTEST_API_TOKEN = "ef1ef82f3c9efee413d602345fbe224d"
 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
 
@@ -33,50 +34,107 @@ HTTP_HOST = "0.0.0.0"
 HTTP_PORT = 8080
 
 # ══════════════════════════════════════════════════════════
-#  模型列表
+#  各 GPU 的模型列表（来自 filter_verified_models 脚本的筛选结果）
 # ══════════════════════════════════════════════════════════
-ALL_MODEL_IDS = [
-    "l3utterfly/mistral-7b-v0.1-layla-v4",
-    "OpenBuddy/openbuddy-mistral-7b-v13.1",
-    "allenai/truthfulqa-info-judge-llama2-7B",
-    "l3utterfly/mistral-7b-v0.1-layla-v1",
-    "l3utterfly/minima-3b-layla-v2",
-    "l3utterfly/tinyllama-1.1b-layla-v4",
-    "l3utterfly/mistral-7b-v0.1-layla-v2",
-    "l3utterfly/tinyllama-1.1b-layla-v1",
-    "Duxiaoman-DI/XuanYuan-13B-Chat",
-    "l3utterfly/minima-3b-layla-v1",
-    "AI-ModelScope/gemma-2-2b",
-    "baichuan-inc/Baichuan-13B-Base",
-    "LGAI-EXAONE/EXAONE-Deep-2.4B",
-    "NousResearch/DeepHermes-3-Llama-3-3B-Preview",
-    "Fengshenbang/Ziya2-13B-Base",
-    "prithivMLmods/QwQ-MathOct-7B",
-    "l3utterfly/phi-2-layla-v1-chatml",
-    "argilla/notus-7b-v1",
-    "prithivMLmods/Doopler-Augment-3B-Cox",
-    "prithivMLmods/Blaze.1-32B-Instruct",
-    "CohereLabs/aya-expanse-8B",
-    "Magpie-Align/MagpieLM-4B-SFT-v0.1",
-    "Magpie-Align/MagpieLM-8B-SFT-v0.1",
-    "Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.2",
-    "Magpie-Align/MagpieLM-8B-Chat-v0.1",
-    "Magpie-Align/Llama-3.1-8B-Magpie-Align-SFT-v0.1",
-    "Magpie-Align/Llama-3-8B-Magpie-Air-SFT-300K-v0.1",
-    "prithivMLmods/Tulu-MathLingo-8B",
-    "prithivMLmods/Triangulum-5B",
-    "prithivMLmods/Viper-Coder-v0.1",
+BIREN_MODELS = [
+    "BigRatz/LOL-AI-2026",
+    "aimeri/spoomplesmaxx-cardmaker-v1",
+    "Alibaba-DT/Logics-STEM-8B-SFT",
+    "Muneebmn123/insurance-voice-qwen25-1_5b",
+    "AnkitBirGurung/NEMO-12B-SFT-Further",
+    "danilarudenko/editorai-mini",
+    "EphemeralYou/Prompt-Refine-MiniCPM5-1B",
+    "mtepe01/mentorx-mistral-7b-automata-merged",
+    "DarkArtsForge/Helix-SCE-12B-jh",
+    "rpant/iolai26-solve",
+    "NithinAI12/NithinX-Omni-LLM-v1",
+    "ConvexAI/Luminex-34B-v0.2",
+    "codellama/CodeLlama-34b-hf",
+    "Lipas007/iol-ai-2026-qwen14b-awq",
+    "D-Z-W/finetuned-teacher",
+    "huan1999/ziya-llama-13b-medical-merged",
+    "codellama/CodeLlama-34b-Python-hf",
+    "ld4ad/gemma-2-9b-dunhuang",
+    "harindhar10/Olmo-7b_1M_Smiles_lora",
+    "allenai/Olmo-3-7B-Think-DPO",
+    "allenai/Olmo-3-32B-Think-DPO",
+    "RedHatAI/gemma-2-9b-it",
+    "prashanthsura/gemma-2-2b-legal-financial-sft",
+    "pfnet/plamo-2-8b",
+    "sail/Sailor2-20B-128K-SFT",
+    "facebook/layerskip-llama3.2-1B",
+    "Qwen/Qwen2.5-32B",
+    "Qwen/Qwen-Image",
+    "KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
+    "xiaoqingsun004/Olmo-WildChat",
+    "longtermrisk/OLMo-3-7B-target-only-no-hallucination-sft",
+    "vimleshiit4463/wyzer-2.0-smollm2-135m",
+    "trl-lib/pythia-1b-deduped-tldr-sft",
 ]
 
+CAMBRICON_MODELS = [
+    "clear-blue-sky/evolai-reborn-tfm-008",
+    "clear-blue-sky/evolai-reborn-tfm-010",
+    "BigRatz/LOL-AI-2026",
+    "clear-blue-sky/evolai-reborn-tfm-001",
+    "clear-blue-sky/evolai-reborn-tfm-019",
+    "clear-blue-sky/evolai-reborn-tfm-007",
+    "aimeri/spoomplesmaxx-cardmaker-v1",
+    "aipatseer/inst_ft_qwen_0.6b_summ",
+    "clear-blue-sky/evolai-reborn-tfm-003",
+    "clear-blue-sky/evolai-reborn-tfm-004",
+    "Alibaba-DT/Logics-STEM-8B-SFT",
+    "clear-blue-sky/evolai-reborn-tfm-002",
+    "prism-ml/Ternary-Bonsai-4B-unpacked",
+    "Muneebmn123/insurance-voice-qwen25-1_5b",
+    "AnkitBirGurung/NEMO-12B-SFT-Further",
+    "danilarudenko/editorai-mini",
+    "rpant/iolai26-solve",
+]
+
+METAX_MODELS = [
+    "Phoenix9781/evolai-tf-model-105",
+    "clear-blue-sky/evolai-reborn-tfm-008",
+    "clear-blue-sky/evolai-reborn-tfm-010",
+    "BigRatz/LOL-AI-2026",
+    "clear-blue-sky/evolai-reborn-tfm-001",
+    "clear-blue-sky/evolai-reborn-tfm-019",
+    "clear-blue-sky/evolai-reborn-tfm-007",
+    "clear-blue-sky/evolai-reborn-tfm-005",
+    "Lin2es/evolai-tfm-03o",
+    "clear-blue-sky/evolai-reborn-tfm-009",
+    "aimeri/spoomplesmaxx-cardmaker-v1",
+    "aipatseer/inst_ft_qwen_0.6b_summ",
+    "reaperdoesntknow/DualMind-TKD-Agentic-1.7B",
+    "clear-blue-sky/evolai-reborn-tfm-011",
+    "clear-blue-sky/evolai-reborn-tfm-003",
+    "clear-blue-sky/evolai-reborn-tfm-004",
+    "clear-blue-sky/evolai-reborn-tfm-002",
+    "amd/ReasonLite-0.6B",
+    "prism-ml/Ternary-Bonsai-4B-unpacked",
+    "Muneebmn123/insurance-voice-qwen25-1_5b",
+    "danilarudenko/editorai-mini",
+    "rpant/iolai26-solve",
+]
+
+# 按顺序处理：Biren → Cambricon → MetaX
+GPU_JOBS: List[Tuple[str, List[str]]] = [
+    ("Biren_166m",           BIREN_MODELS),
+    ("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
+    ("MetaX_c-500",          METAX_MODELS),
+]
+TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)
+
 # ══════════════════════════════════════════════════════════
 #  全局状态（供 /status 展示）
 # ══════════════════════════════════════════════════════════
 _state = {
     "strategy_id":   STRATEGY_ID,
     "phase":         "starting",   # starting | submitting | done | error
-    "total":         len(ALL_MODEL_IDS),
+    "total":         TOTAL_MODELS,
     "submitted":     0,
     "failed":        0,
+    "per_gpu":       {gpu: 0 for gpu, _ in GPU_JOBS},
     "started_at":    None,
     "finished_at":   None,
 }
@@ -116,14 +174,40 @@ def _run_http():
     print("[http] 已关闭", flush=True)
 
 # ══════════════════════════════════════════════════════════
-#  业务逻辑
+#  各 GPU 的 config_content 模板
 # ══════════════════════════════════════════════════════════
-def _submit_task(token: str, model_id: str) -> Tuple[bool, str]:
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {token}",
-    }
-    config_content = f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
+def build_config_content(gpu_type: str, model_id: str) -> str:
+    if gpu_type == "Biren_166m":
+        max_model_len = 4096
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-biren166m:26.01
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: zh
+storage: gpfs
+api: completion
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: {max_model_len}
+sut_config:
+  values:
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        value: {max_model_len}
+    command: ['/bin/bash', '-ic', 'vllm serve /model --port 8000 --served-model-name llm --max-model-len {max_model_len} --gpu-memory-utilization 0.9 --enforce-eager --trust-remote-code -tp 1 --host 0.0.0.0']
+ref_config:
+  values:
+    cpu_num: 2
+    gpu_num: 1
+    env:
+      - name: MAX_MODEL_LEN
+        value: {max_model_len}
+    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '{max_model_len}', '--enforce-eager', '--trust-remote-code', '-tp', '1']
+model: llm
+"""
+    elif gpu_type == "Cambricon_mlu-370-x8":
+        return f"""docker_image: harbor.4pd.io/hardcore-tech/cambricon-mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
 nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
 framework: vllm
 storage: gpfs
@@ -146,21 +230,54 @@ ref_config:
         value: 8192
     command: ["vllm", "serve", "/model", "--port", "80", "--served-model-name", "llm", "--max-model-len", "8192", "--trust-remote-code", "--dtype", "float16"]
 """
+    elif gpu_type == "MetaX_c-500":
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex-metax/vllm:0.9.1
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: en
+storage: gpfs
+api: chat
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: 2048
+sut_config:
+  gpu_num: 1
+  values:
+    command: ['/opt/conda/bin/vllm', 'serve', '/model', '--port', '20644', '--served-model-name', 'llm',  '--max-model-len', '2048', '--gpu-memory-utilization', '0.9',  '--enforce-eager', '--trust-remote-code' ,'-tp', '1']
+ref_config:
+  gpu_num: 1
+  values:
+    command: ['vllm', 'serve', '/model', '--port', '80', '--served-model-name', 'llm', '--max-model-len', '2048', '--enforce-eager', '--trust-remote-code', '-tp', '1']
+"""
+    else:
+        raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
+
+# ══════════════════════════════════════════════════════════
+#  业务逻辑
+# ══════════════════════════════════════════════════════════
+def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str]:
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}",
+    }
+    config_content = build_config_content(gpu_type, model_id)
+
     payload = {
         "contestApiToken": CONTEST_API_TOKEN,
         "contributors":    CONTRIBUTORS,
-        "gpuTypes":        [GPU_TYPE],
+        "gpuTypes":        [gpu_type],
         "taskType":        TASK_TYPE,
         "modelId":         model_id,
         "framework":       "vllm",
         "strategyId":      STRATEGY_ID,   # 平台要求
         "submissionConfig": [{
             "config":   config_content,
-            "gpuType":  GPU_TYPE,
+            "gpuType":  gpu_type,
             "taskType": TASK_TYPE,
         }],
     }
-    print(f"[payload] {json.dumps(payload, indent=2, ensure_ascii=False)}", flush=True)
+    print(f"[payload] gpu={gpu_type} model={model_id}", flush=True)
     try:
         resp = requests.post(
             BASE_URL + SUBMIT_ENDPOINT,
@@ -171,13 +288,13 @@ ref_config:
         result = resp.json()
         if result.get("code") == 0:
             task_id = result.get("data", {}).get("id", "")
-            print(f"[worker] OK  {model_id}  task_id={task_id}", flush=True)
+            print(f"[worker] OK  {model_id} (GPU={gpu_type})  task_id={task_id}", flush=True)
             return True, task_id
         else:
-            print(f"[worker] FAIL {model_id}: {result.get('message')}", flush=True)
+            print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {result.get('message')}", flush=True)
             return False, ""
     except Exception as e:
-        print(f"[worker] ERROR {model_id}: {e}", flush=True)
+        print(f"[worker] ERROR {model_id} (GPU={gpu_type}): {e}", flush=True)
         return False, ""
 
 
@@ -185,32 +302,39 @@ def _run_worker():
     _state["started_at"] = datetime.utcnow().isoformat()
     _state["phase"] = "submitting"
 
-    successful: List[Tuple[str, str]] = []
+    successful: List[Tuple[str, str, str]] = []
     token = AUTH_TOKEN
     print("[worker] 使用预设 Token，跳过登录", flush=True)
 
-    for model_id in ALL_MODEL_IDS:
+    for gpu_type, model_list in GPU_JOBS:
         if _shutdown.is_set():
             break
-        ok, task_id = _submit_task(token, model_id)
-        if ok:
-            _state["submitted"] += 1
-            successful.append((task_id, 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
+            ok, task_id = _submit_task(token, gpu_type, model_id)
+            if ok:
+                _state["submitted"] += 1
+                _state["per_gpu"][gpu_type] += 1
+                successful.append((task_id, gpu_type, model_id))
+            else:
+                _state["failed"] += 1
 
     # 写入结果文件
     try:
         with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
-            for tid, mid in successful:
-                f.write(f"{tid}\t{mid}\n")
+            for tid, gpu, mid in successful:
+                f.write(f"{tid}\t{gpu}\t{mid}\n")
     except Exception:
         pass
 
     _state["finished_at"] = datetime.utcnow().isoformat()
     _state["phase"] = "done"
     print(
-        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}",
+        f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}  "
+        f"total={_state['total']}  per_gpu={_state['per_gpu']}",
         flush=True,
     )
     # 提交完成后继续保持进程存活，等待平台停止
@@ -243,4 +367,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()