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eduagarcia-temp
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llm_pt_leaderboard_requests

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eduagarcia commited on
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ddc0e65
•
1 Parent(s): faac587

Retry 22 FAILED models

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Files changed (22) hide show
  1. MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2_eval_request_False_bfloat16_Original.json +2 -4
  2. Qwen/Qwen2-57B-A14B-Instruct_eval_request_False_float16_Original.json +2 -4
  3. Qwen/Qwen2-57B-A14B_eval_request_False_bfloat16_Original.json +2 -4
  4. Qwen/Qwen2-72B_eval_request_False_bfloat16_Original.json +2 -4
  5. botbot-ai/CabraLlama3-70b_eval_request_False_bfloat16_Original.json +2 -4
  6. cloudyu/Mixtral_34Bx2_MoE_60B_eval_request_False_bfloat16_Original.json +2 -4
  7. cognitivecomputations/dolphin-2.6-mixtral-8x7b_eval_request_False_bfloat16_Original.json +2 -4
  8. cognitivecomputations/dolphin-2.6-mixtral-8x7b_eval_request_d099b57_False_bfloat16_Original.json +2 -4
  9. cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_626c825_False_bfloat16_Original.json +2 -4
  10. cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_628c376_False_bfloat16_Original.json +2 -4
  11. cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_9ad9d14_False_bfloat16_Original.json +2 -4
  12. cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_False_bfloat16_Original.json +2 -4
  13. cognitivecomputations/dolphin-2.9.1-llama-3-70b_eval_request_False_bfloat16_Original.json +2 -4
  14. failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3_eval_request_False_bfloat16_Original.json +2 -4
  15. freewheelin/free-evo-qwen72b-v0.8-re_eval_request_False_float16_Original.json +2 -4
  16. lmsys/vicuna-33b-v1.3_eval_request_False_float16_Original.json +2 -4
  17. mmnga/Llama-3-70B-japanese-suzume-vector-v0.1_eval_request_False_bfloat16_Original.json +2 -4
  18. paloalma/ECE-TW3-JRGL-V1_eval_request_2f08c7a_False_bfloat16_Original.json +2 -4
  19. paloalma/ECE-TW3-JRGL-V1_eval_request_2f08c7a_False_float16_Original.json +2 -4
  20. paloalma/Le_Triomphant-ECE-TW3_eval_request_False_bfloat16_Original.json +2 -4
  21. paloalma/TW3-JRGL-v2_eval_request_False_bfloat16_Original.json +2 -4
  22. tenyx/Llama3-TenyxChat-70B_eval_request_False_bfloat16_Original.json +2 -4
MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T03:57:13Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 884,
16
- "job_start_time": "2024-07-06T10-48-40.361441",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 380.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 10.34 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 380.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 10.34 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T03:57:13Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 884,
16
+ "job_start_time": "2024-07-06T10-48-40.361441"
 
 
17
  }
Qwen/Qwen2-57B-A14B-Instruct_eval_request_False_float16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "Qwen2MoeForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "Chinese",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-08T12:06:46Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 876,
16
- "job_start_time": "2024-07-06T03-19-18.813912",
17
- "error_msg": "CUDA out of memory. Tried to allocate 18.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 1857573 has 29.63 GiB memory in use. Process 2399723 has 49.71 GiB memory in use. Of the allocated memory 29.09 GiB is allocated by PyTorch, and 40.98 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 18.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 1857573 has 29.63 GiB memory in use. Process 2399723 has 49.71 GiB memory in use. Of the allocated memory 29.09 GiB is allocated by PyTorch, and 40.98 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"
19
  }
 
8
  "architectures": "Qwen2MoeForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "Chinese",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-08T12:06:46Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 876,
16
+ "job_start_time": "2024-07-06T03-19-18.813912"
 
 
17
  }
Qwen/Qwen2-57B-A14B_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "Qwen2MoeForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-08T03:07:39Z",
13
  "model_type": "🟢 : pretrained",
14
  "source": "manual",
15
  "job_id": 870,
16
- "job_start_time": "2024-07-05T20-04-24.289055",
17
- "error_msg": "CUDA out of memory. Tried to allocate 18.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 1857573 has 79.33 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 88.91 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 18.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.19 MiB is free. Process 1857573 has 79.33 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 88.91 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"
19
  }
 
8
  "architectures": "Qwen2MoeForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-08T03:07:39Z",
13
  "model_type": "🟢 : pretrained",
14
  "source": "manual",
15
  "job_id": 870,
16
+ "job_start_time": "2024-07-05T20-04-24.289055"
 
 
17
  }
Qwen/Qwen2-72B_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "Qwen2ForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-08T03:07:52Z",
13
  "model_type": "🟢 : pretrained",
14
  "source": "leaderboard",
15
  "job_id": 838,
16
- "job_start_time": "2024-06-18T13-27-33.990846",
17
- "error_msg": "CUDA out of memory. Tried to allocate 2.32 GiB. GPU 0 has a total capacty of 79.35 GiB of which 954.19 MiB is free. Process 421880 has 72.02 GiB memory in use. Process 744644 has 5.02 GiB memory in use. Of the allocated memory 69.67 GiB is allocated by PyTorch, and 1.85 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 159, in simple_evaluate\n results = 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 343, in evaluate\n resps = getattr(lm, reqtype)(cloned_reqs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1525, in generate_until\n cont = self._model_generate(\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1070, in _model_generate\n return self.model.generate(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/generation/utils.py\", line 1736, in generate\n result = self._sample(\n ^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/generation/utils.py\", line 2375, in _sample\n outputs = self(\n ^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/accelerate/hooks.py\", line 166, in new_forward\n output = module._old_forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/qwen2/modeling_qwen2.py\", line 1162, in forward\n logits = self.lm_head(hidden_states)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/accelerate/hooks.py\", line 161, in new_forward\n args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/accelerate/hooks.py\", line 347, in pre_forward\n set_module_tensor_to_device(\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 2.32 GiB. GPU 0 has a total capacty of 79.35 GiB of which 954.19 MiB is free. Process 421880 has 72.02 GiB memory in use. Process 744644 has 5.02 GiB memory in use. Of the allocated memory 69.67 GiB is allocated by PyTorch, and 1.85 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"
19
  }
 
8
  "architectures": "Qwen2ForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-08T03:07:52Z",
13
  "model_type": "🟢 : pretrained",
14
  "source": "leaderboard",
15
  "job_id": 838,
16
+ "job_start_time": "2024-06-18T13-27-33.990846"
 
 
17
  }
botbot-ai/CabraLlama3-70b_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "Portuguese",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-21T19:38:55Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 873,
16
- "job_start_time": "2024-07-06T01-16-16.587794",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "Portuguese",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-21T19:38:55Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 873,
16
+ "job_start_time": "2024-07-06T01-16-16.587794"
 
 
17
  }
cloudyu/Mixtral_34Bx2_MoE_60B_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-04-30T10:16:56Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 877,
16
- "job_start_time": "2024-07-06T03-22-34.698830",
17
- "error_msg": "CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 120.19 MiB is free. Process 1857573 has 29.52 GiB memory in use. Process 2399723 has 49.71 GiB memory in use. Of the allocated memory 28.79 GiB is allocated by PyTorch, and 243.04 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 120.19 MiB is free. Process 1857573 has 29.52 GiB memory in use. Process 2399723 has 49.71 GiB memory in use. Of the allocated memory 28.79 GiB is allocated by PyTorch, and 243.04 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"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-04-30T10:16:56Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 877,
16
+ "job_start_time": "2024-07-06T03-22-34.698830"
 
 
17
  }
cognitivecomputations/dolphin-2.6-mixtral-8x7b_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-17T07:12:51Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 831,
16
- "job_start_time": "2024-06-18T01-31-44.667504",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 58.19 MiB is free. Process 3873951 has 63.28 GiB memory in use. Process 3896110 has 16.01 GiB memory in use. Of the allocated memory 62.09 GiB is allocated by PyTorch, and 94.20 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 58.19 MiB is free. Process 3873951 has 63.28 GiB memory in use. Process 3896110 has 16.01 GiB memory in use. Of the allocated memory 62.09 GiB is allocated by PyTorch, and 94.20 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"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-17T07:12:51Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 831,
16
+ "job_start_time": "2024-06-18T01-31-44.667504"
 
 
17
  }
cognitivecomputations/dolphin-2.6-mixtral-8x7b_eval_request_d099b57_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-06-13T18:30:14Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 817,
16
- "job_start_time": "2024-06-15T15-00-37.264390",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU ",
18
- "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 71, 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 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 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 399, 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 \n"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-06-13T18:30:14Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 817,
16
+ "job_start_time": "2024-06-15T15-00-37.264390"
 
 
17
  }
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_626c825_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-29T06:49:40Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 779,
16
- "job_start_time": "2024-05-30T06-22-52.082627",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 10.43 GiB is free. Process 2378807 has 68.91 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 105.92 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 199, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 10.43 GiB is free. Process 2378807 has 68.91 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 105.92 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"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-29T06:49:40Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 779,
16
+ "job_start_time": "2024-05-30T06-22-52.082627"
 
 
17
  }
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_628c376_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-31T08:44:12Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 788,
16
- "job_start_time": "2024-06-12T15-32-24.307081",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacity of 31.75 GiB of which 37.75 MiB is free. Process 69470 has 31.71 GiB memory in use. Of the allocated memory 30.72 GiB is allocated by PyTorch, and 62.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 199, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 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 \"/workspace/repos/llm_leaderboard/transformers/src/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n model_class = _get_model_class(config, cls._model_mapping)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/modeling_utils.py\", line 3679, in from_pretrained\n if gguf_path is None and (low_cpu_mem_usage or (use_keep_in_fp32_modules and is_accelerate_available())):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/modeling_utils.py\", line 4106, in _load_pretrained_model\n # This will only initialize submodules that are not marked as initialized by the line above.\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/transformers/src/transformers/modeling_utils.py\", line 887, in _load_state_dict_into_meta_model\n or (not hf_quantizer.requires_parameters_quantization)\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/accelerate/utils/modeling.py\", line 399, 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 capacity of 31.75 GiB of which 37.75 MiB is free. Process 69470 has 31.71 GiB memory in use. Of the allocated memory 30.72 GiB is allocated by PyTorch, and 62.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-31T08:44:12Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 788,
16
+ "job_start_time": "2024-06-12T15-32-24.307081"
 
 
17
  }
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_9ad9d14_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-25T06:10:49Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 755,
16
- "job_start_time": "2024-05-27T02-48-01.373862",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 2.22 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 1619173 has 67.93 GiB memory in use. Of the allocated memory 67.32 GiB is allocated by PyTorch, and 109.98 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 199, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 2.22 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 1619173 has 67.93 GiB memory in use. Of the allocated memory 67.32 GiB is allocated by PyTorch, and 109.98 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"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-25T06:10:49Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 755,
16
+ "job_start_time": "2024-05-27T02-48-01.373862"
 
 
17
  }
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-13T16:00:21Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 749,
16
- "job_start_time": "2024-05-26T14-43-14.030329",
17
- "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 2.84 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 1514823 has 67.78 GiB memory in use. Of the allocated memory 67.28 GiB is allocated by PyTorch, and 102.05 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 199, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 2.84 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 1514823 has 67.78 GiB memory in use. Of the allocated memory 67.28 GiB is allocated by PyTorch, and 102.05 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"
19
  }
 
8
  "architectures": "MixtralForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-13T16:00:21Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 749,
16
+ "job_start_time": "2024-05-26T14-43-14.030329"
 
 
17
  }
cognitivecomputations/dolphin-2.9.1-llama-3-70b_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-24T20:26:58Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 880,
16
- "job_start_time": "2024-07-06T06-42-09.053742",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 428.19 MiB is free. Process 1857573 has 27.54 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.03 GiB is allocated by PyTorch, and 11.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 428.19 MiB is free. Process 1857573 has 27.54 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.03 GiB is allocated by PyTorch, and 11.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-24T20:26:58Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 880,
16
+ "job_start_time": "2024-07-06T06-42-09.053742"
 
 
17
  }
failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T03:59:19Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 885,
16
- "job_start_time": "2024-07-06T11-54-57.445036",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T03:59:19Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 885,
16
+ "job_start_time": "2024-07-06T11-54-57.445036"
 
 
17
  }
freewheelin/free-evo-qwen72b-v0.8-re_eval_request_False_float16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T03:56:50Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 883,
16
- "job_start_time": "2024-07-06T09-44-38.163564",
17
- "error_msg": "CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 208.19 MiB is free. Process 1857573 has 79.14 GiB memory in use. Of the allocated memory 78.53 GiB is allocated by PyTorch, and 120.34 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 208.19 MiB is free. Process 1857573 has 79.14 GiB memory in use. Of the allocated memory 78.53 GiB is allocated by PyTorch, and 120.34 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T03:56:50Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 883,
16
+ "job_start_time": "2024-07-06T09-44-38.163564"
 
 
17
  }
lmsys/vicuna-33b-v1.3_eval_request_False_float16_Original.json CHANGED
@@ -7,13 +7,11 @@
7
  "params": 33.0,
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
- "status": "FAILED",
11
  "submitted_time": "2024-03-05T16:46:42Z",
12
  "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
13
  "source": "leaderboard",
14
  "job_id": 740,
15
  "job_start_time": "2024-05-26T15-27-01.984914",
16
- "main_language": "English",
17
- "error_msg": "CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 60.19 MiB is free. Process 3627427 has 79.29 GiB memory in use. Of the allocated memory 75.33 GiB is allocated by PyTorch, and 3.46 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",
18
- "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 commit_hash = None\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\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 159, in simple_evaluate\n results = 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 343, in evaluate\n resps = getattr(lm, reqtype)(cloned_reqs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1525, in generate_until\n cont = self._model_generate(\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 1070, in _model_generate\n return self.model.generate(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/generation/utils.py\", line 1736, in generate\n result = self._sample(\n ^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/generation/utils.py\", line 2375, in _sample\n outputs = self(\n ^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py\", line 1164, in forward\n outputs = self.model(\n ^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py\", line 968, in forward\n layer_outputs = decoder_layer(\n ^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py\", line 713, in forward\n hidden_states, self_attn_weights, present_key_value = self.self_attn(\n ^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py\", line 629, in forward\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/cache_utils.py\", line 155, in update\n self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 60.19 MiB is free. Process 3627427 has 79.29 GiB memory in use. Of the allocated memory 75.33 GiB is allocated by PyTorch, and 3.46 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"
19
  }
 
7
  "params": 33.0,
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
+ "status": "RERUN",
11
  "submitted_time": "2024-03-05T16:46:42Z",
12
  "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
13
  "source": "leaderboard",
14
  "job_id": 740,
15
  "job_start_time": "2024-05-26T15-27-01.984914",
16
+ "main_language": "English"
 
 
17
  }
mmnga/Llama-3-70B-japanese-suzume-vector-v0.1_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T23:29:41Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 886,
16
- "job_start_time": "2024-07-06T13-13-10.550939",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T23:29:41Z",
13
  "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
14
  "source": "leaderboard",
15
  "job_id": 886,
16
+ "job_start_time": "2024-07-06T13-13-10.550939"
 
 
17
  }
paloalma/ECE-TW3-JRGL-V1_eval_request_2f08c7a_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-28T03:51:50Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 888,
16
- "job_start_time": "2024-07-06T16-36-30.068182",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 94.19 MiB is free. Process 1857573 has 79.25 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 94.19 MiB is free. Process 1857573 has 79.25 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-28T03:51:50Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 888,
16
+ "job_start_time": "2024-07-06T16-36-30.068182"
 
 
17
  }
paloalma/ECE-TW3-JRGL-V1_eval_request_2f08c7a_False_float16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-28T03:52:27Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 889,
16
- "job_start_time": "2024-07-06T18-23-51.839608",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 94.19 MiB is free. Process 1857573 has 79.25 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 94.19 MiB is free. Process 1857573 has 79.25 GiB memory in use. Of the allocated memory 78.75 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-28T03:52:27Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 889,
16
+ "job_start_time": "2024-07-06T18-23-51.839608"
 
 
17
  }
paloalma/Le_Triomphant-ECE-TW3_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T03:38:32Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 881,
16
- "job_start_time": "2024-07-06T07-49-03.236202",
17
- "error_msg": "CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 138.19 MiB is free. Process 1857573 has 27.82 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.20 GiB is allocated by PyTorch, and 120.41 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 138.19 MiB is free. Process 1857573 has 27.82 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.20 GiB is allocated by PyTorch, and 120.41 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T03:38:32Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 881,
16
+ "job_start_time": "2024-07-06T07-49-03.236202"
 
 
17
  }
paloalma/TW3-JRGL-v2_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-26T03:38:54Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 882,
16
- "job_start_time": "2024-07-06T08-43-38.043826",
17
- "error_msg": "CUDA out of memory. Tried to allocate 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 138.19 MiB is free. Process 1857573 has 27.82 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.20 GiB is allocated by PyTorch, and 120.41 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 384.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 138.19 MiB is free. Process 1857573 has 27.82 GiB memory in use. Process 2399723 has 51.39 GiB memory in use. Of the allocated memory 27.20 GiB is allocated by PyTorch, and 120.41 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-26T03:38:54Z",
13
  "model_type": "🤝 : base merges and moerges",
14
  "source": "leaderboard",
15
  "job_id": 882,
16
+ "job_start_time": "2024-07-06T08-43-38.043826"
 
 
17
  }
tenyx/Llama3-TenyxChat-70B_eval_request_False_bfloat16_Original.json CHANGED
@@ -8,12 +8,10 @@
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
- "status": "FAILED",
12
  "submitted_time": "2024-05-27T16:48:41Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 887,
16
- "job_start_time": "2024-07-06T14-48-00.234575",
17
- "error_msg": "CUDA out of memory. Tried to allocate 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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",
18
- "traceback": "Traceback (most recent call last):\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 201, in wait_download_and_run_request\n run_request(\n File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 71, 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 564, 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 3838, 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 4298, 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 895, 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 448.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 382.19 MiB is free. Process 1857573 has 78.97 GiB memory in use. Of the allocated memory 78.47 GiB is allocated by PyTorch, and 8.36 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"
19
  }
 
8
  "architectures": "LlamaForCausalLM",
9
  "weight_type": "Original",
10
  "main_language": "English",
11
+ "status": "RERUN",
12
  "submitted_time": "2024-05-27T16:48:41Z",
13
  "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
14
  "source": "leaderboard",
15
  "job_id": 887,
16
+ "job_start_time": "2024-07-06T14-48-00.234575"
 
 
17
  }