Update status of allenai/tulu-2-dpo-7b_eval_request_False_bfloat16_Original to FINISHED

allenai/tulu-2-dpo-7b_eval_request_False_bfloat16_Original.json

@@ -7,12 +7,24 @@
     "params": 7.0,
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
-    "status": "FAILED",
+    "status": "FINISHED",
     "submitted_time": "2024-03-05T16:37:20Z",
     "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
-    "job_id": 336,
-    "job_start_time": "2024-04-02T02-30-25.319584",
-    "error_msg": "CUDA out of memory. Tried to allocate 86.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 76.19 MiB is free. Process 4070277 has 45.46 GiB memory in use. Process 4074833 has 16.47 GiB memory in use. Process 188848 has 792.00 MiB memory in use. Process 209361 has 16.56 GiB memory in use. Of the allocated memory 378.00 MiB is allocated by PyTorch, and 0 bytes 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 561, 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 86.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 76.19 MiB is free. Process 4070277 has 45.46 GiB memory in use. Process 4074833 has 16.47 GiB memory in use. Process 188848 has 792.00 MiB memory in use. Process 209361 has 16.56 GiB memory in use. Of the allocated memory 378.00 MiB is allocated by PyTorch, and 0 bytes 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"
+    "job_id": 330,
+    "job_start_time": "2024-04-02T00-29-33.604027",
+    "eval_version": "1.1.0",
+    "result_metrics": {
+        "enem_challenge": 0.5150454863540938,
+        "bluex": 0.44089012517385257,
+        "oab_exams": 0.3826879271070615,
+        "assin2_rte": 0.882693297611393,
+        "assin2_sts": 0.6797107988195017,
+        "faquad_nli": 0.6767812942008486,
+        "hatebr_offensive": 0.8485191831468404,
+        "portuguese_hate_speech": 0.6881970837369898,
+        "tweetsentbr": 0.6409551735643995
+    },
+    "result_metrics_average": 0.63949781885722,
+    "result_metrics_npm": 0.47383474887492505
 }