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{
"model": "deepseek-ai/deepseek-moe-16b-base",
"base_model": "",
"revision": "main",
"private": false,
"precision": "bfloat16",
"params": 16.376,
"architectures": "DeepseekForCausalLM",
"weight_type": "Original",
"status": "FAILED",
"submitted_time": "2024-02-05T23:08:52Z",
"model_type": "🟢 : pretrained",
"source": "script",
"job_id": 348,
"job_start_time": "2024-04-02T09-25-05.375938",
"error_msg": "CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 4070277 has 538.00 MiB memory in use. Process 4074833 has 27.62 GiB memory in use. Process 188848 has 32.47 GiB memory in use. Process 209361 has 18.72 GiB memory in use. Of the allocated memory 22.56 GiB is allocated by PyTorch, and 4.55 GiB 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 238, 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 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 297, in __init__\n self._create_model(\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 608, 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 556, 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 3502, 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 3926, 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 805, 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 347, in set_module_tensor_to_device\n new_value = value.to(device)\n ^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 4070277 has 538.00 MiB memory in use. Process 4074833 has 27.62 GiB memory in use. Process 188848 has 32.47 GiB memory in use. Process 209361 has 18.72 GiB memory in use. Of the allocated memory 22.56 GiB is allocated by PyTorch, and 4.55 GiB 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",
"main_language": "?"
} |