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{
"model": "databricks/dbrx-base",
"base_model": "",
"revision": "main",
"private": false,
"precision": "bfloat16",
"params": 131.597,
"architectures": "DbrxForCausalLM",
"weight_type": "Original",
"main_language": "English",
"status": "FAILED",
"submitted_time": "2024-04-07T15:38:13Z",
"model_type": "🟢 : pretrained",
"source": "leaderboard",
"job_id": 393,
"job_start_time": "2024-04-07T18-28-58.240299",
"error_msg": "CUDA error: out of memory\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n",
"traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 190, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 64, in run_request\n results = run_eval_on_model(\n ^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 55, in run_eval_on_model\n result = evaluate(\n ^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n results = evaluator.simple_evaluate(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 415, in _wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 159, in simple_evaluate\n results = evaluate(\n ^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 415, in _wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 343, in evaluate\n resps = getattr(lm, reqtype)(cloned_reqs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1426, in generate_until\n batch_size, _ = self._detect_batch_size_and_length()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 815, in _detect_batch_size_and_length\n batch_size, max_length = forward_batch()\n ^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 144, in decorator\n return function(batch_size, max_length, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 806, in forward_batch\n test_batch = torch.ones(\n ^^^^^^^^^^^\nRuntimeError: CUDA error: out of memory\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n\n"
} |