add Kunlunxin_p-800 as 4th GPU with filtered model list

main.py

@@ -1,8 +1,8 @@
 """
 xc_validation_strategy — 主入口
 
-启动后针对 3 张 GPU 卡（Biren_166m / Cambricon_mlu-370-x8 / MetaX_c-500）
+启动后针对 4 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，
+Kunlunxin_p-800）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）。
 """
@@ -117,11 +117,44 @@ METAX_MODELS = [
     "rpant/iolai26-solve",
 ]
 
-# 按顺序处理：Biren → Cambricon → MetaX
+KUNLUNXIN_MODELS = [
+    "Patriae/patriae-cuban-dialect-model",
+    "Phoenix9781/evolai-tf-model-105",
+    "clear-blue-sky/evolai-reborn-tfm-008",
+    "clear-blue-sky/evolai-reborn-tfm-010",
+    "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",
+    "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",
+    "EthanGao123/CellHermes-v1.0",
+    "RecursiveMAS/Mixture-Science-BioMistral-7B",
+    "chenyitian-shanshu/SIRL-Gurobi",
+    "RedHatAI/gemma-2-9b-it",
+    "gaunernst/gemma-3-27b-it-qat-autoawq",
+    "promotion/qwen3-8b-simpo-avg-b2p5-g1p0-s42",
+    "llamaindex/vdr-2b-multi-v1",
+    "KordAI/Typhoon-Gemma3-KordTranslate-EN-TH-4B",
+    "TheDrummer/UnslopNemo-12B-v3",
+    "gradients-io-tournaments/tournament-tourn_c5d86c82ce819a79_20260706-b78a01d4-0a6a-49e1-9190-5e88ae329937-5DS6XMVr",
+    "ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1",
+]
+
+# 按顺序处理：Biren → Cambricon → MetaX → Kunlunxin
 GPU_JOBS: List[Tuple[str, List[str]]] = [
     ("Biren_166m",           BIREN_MODELS),
     ("Cambricon_mlu-370-x8", CAMBRICON_MODELS),
     ("MetaX_c-500",          METAX_MODELS),
+    ("Kunlunxin_p-800",      KUNLUNXIN_MODELS),
 ]
 TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)
 
@@ -249,10 +282,37 @@ 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']
+"""
+    elif gpu_type == "Kunlunxin_p-800":
+        return f"""docker_image: git.modelhub.org.cn:9443/enginex/xc-llm-kunlun
+nv_docker_image: harbor.4pd.io/dooke/vllm/vllm/vllm-openai:v0.11.0
+framework: vllm
+lang: en
+storage: gpfs
+api: chat
+temperature: 0.4
+repetition_penalty: 1.1
+top_p: 0.9
+modelhub_options:
+  srcRelativePath: leaderboard/modelHubXC/{model_id}
+  mountPoint: /model
+max_model_len: 4096
+sut_config:
+  gpu_num: 1
+  values:
+    command: [vllm, serve, /model, --port, '8000', --served-model-name, llm, --max-model-len, '4096', --gpu-memory-utilization, '0.9', --enforce-eager, --trust-remote-code, -tp, '1']
+ref_config:
+  gpu_num: 1
+  values:
+    command: [vllm, serve, /model, --port, '80', --served-model-name, llm, --max-model-len, '4096', --enforce-eager, --trust-remote-code, -tp, '1']
 """
     else:
         raise ValueError(f"未知的 GPU_TYPE: {gpu_type}")
     
+    
+    
+    
+
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