Update status of TIGER-Lab/MAmmoTH2-8x7B-Plus_eval_request_False_bfloat16_Original to FAILED

TIGER-Lab/MAmmoTH2-8x7B-Plus_eval_request_False_bfloat16_Original.json

@@ -8,10 +8,12 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "RUNNING",
+    "status": "FAILED",
     "submitted_time": "2024-05-17T07:43:39Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 677,
-    "job_start_time": "2024-05-21T03-30-38.659975"
+    "job_start_time": "2024-05-21T03-30-38.659975",
+    "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 31.70 GiB is free. Process 915063 has 2.06 GiB memory in use. Process 3012429 has 4.54 GiB memory in use. Process 49948 has 41.04 GiB memory in use. Of the allocated memory 39.52 GiB is allocated by PyTorch, and 66.78 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
+    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 200, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, 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 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 887, in _load_state_dict_into_meta_model\n    set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/accelerate/utils/modeling.py\", line 400, in set_module_tensor_to_device\n    new_value = value.to(device)\n                ^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 31.70 GiB is free. Process 915063 has 2.06 GiB memory in use. Process 3012429 has 4.54 GiB memory in use. Process 49948 has 41.04 GiB memory in use. Of the allocated memory 39.52 GiB is allocated by PyTorch, and 66.78 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n"
 }