Update status of ai21labs/Jamba-v0.1_eval_request_False_bfloat16_Original to FAILED
8cc9ba9
verified
| { | |
| "model": "ai21labs/Jamba-v0.1", | |
| "base_model": "", | |
| "revision": "main", | |
| "private": false, | |
| "precision": "bfloat16", | |
| "params": 51.57, | |
| "architectures": "JambaForCausalLM", | |
| "weight_type": "Original", | |
| "main_language": "English", | |
| "status": "FAILED", | |
| "submitted_time": "2024-04-07T15:38:46Z", | |
| "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)", | |
| "source": "leaderboard", | |
| "job_id": 445, | |
| "job_start_time": "2024-04-15T02-21-15.174991", | |
| "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 82.19 MiB is free. Process 1855914 has 79.26 GiB memory in use. Of the allocated memory 78.72 GiB is allocated by PyTorch, and 44.06 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 225, in wait_download_and_run_request\n torch.cuda.empty_cache()\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 65, 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 415, in _wrapper\n f\"WARNING: using {fn.__name__} with positional arguments is \"\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 558, 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 3531, in from_pretrained\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3958, in _load_pretrained_model\n if any(module_to_keep_in_fp32 in name.split(\".\") for module_to_keep_in_fp32 in keep_in_fp32_modules):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 812, in _load_state_dict_into_meta_model\n if param_name not in loaded_state_dict_keys or param_name not in expected_keys:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\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 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 82.19 MiB is free. Process 1855914 has 79.26 GiB memory in use. Of the allocated memory 78.72 GiB is allocated by PyTorch, and 44.06 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" | |
| } |