Update status of Qwen/Qwen1.5-110B_eval_request_False_4bit_Original to FAILED

Qwen/Qwen1.5-110B_eval_request_False_4bit_Original.json

@@ -8,10 +8,12 @@
     "architectures": "Qwen2ForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "RUNNING",
+    "status": "FAILED",
     "submitted_time": "2024-05-23T02:02:29Z",
     "model_type": "🟢 : pretrained",
     "source": "leaderboard",
     "job_id": 741,
-    "job_start_time": "2024-05-26T01-35-11.153523"
+    "job_start_time": "2024-05-26T01-35-11.153523",
+    "error_msg": "CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.15 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.99 GiB memory in use. Of the allocated memory 39.41 GiB is allocated by PyTorch, and 116.11 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 198, 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 889, in _load_state_dict_into_meta_model\n    hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/quantizers/quantizer_bnb_4bit.py\", line 216, in create_quantized_param\n    new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 324, in to\n    return self._quantize(device)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 289, in _quantize\n    w_4bit, quant_state = bnb.functional.quantize_4bit(\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/functional.py\", line 1169, in quantize_4bit\n    out = torch.zeros(((n + 1) // mod, 1), dtype=quant_storage, device=A.device)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.15 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.99 GiB memory in use. Of the allocated memory 39.41 GiB is allocated by PyTorch, and 116.11 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"
 }