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eduagarcia-temp
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llm_pt_leaderboard_requests

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eduagarcia commited on May 26

Commit

316d78d

•

1 Parent(s): 9f439c6

Retry 15 FAILED models

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Files changed (15) hide show

CultriX/NeuralMona_MoE-4x7B_eval_request_False_bfloat16_Original.json +2 -4
Qwen/Qwen1.5-110B_eval_request_False_4bit_Original.json +2 -4
Weyaxi/Bagel-Hermes-34B-Slerp_eval_request_False_bfloat16_Original.json +2 -4
WizardLMTeam/WizardLM-13B-V1.0_eval_request_False_float16_Original.json +2 -4
Xwin-LM/Xwin-LM-13B-V0.1_eval_request_False_float16_Original.json +2 -4
Xwin-LM/Xwin-LM-13B-V0.2_eval_request_False_float16_Original.json +2 -4
alpindale/WizardLM-2-8x22B_eval_request_False_4bit_Original.json +2 -4
cognitivecomputations/WizardLM-30B-Uncensored_eval_request_False_float16_Original.json +2 -4
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_9ad9d14_False_bfloat16_Original.json +2 -4
cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_False_bfloat16_Original.json +2 -4
fblgit/UNA-SimpleSmaug-34b-v1beta_eval_request_False_bfloat16_Original.json +2 -4
jondurbin/bagel-dpo-34b-v0.5_eval_request_False_bfloat16_Original.json +2 -4
lmsys/vicuna-33b-v1.3_eval_request_False_float16_Original.json +2 -4
migtissera/Tess-M-v1.3_eval_request_False_float16_Original.json +2 -4
mlabonne/Beyonder-4x7B-v3_eval_request_False_float16_Original.json +2 -4

CultriX/NeuralMona_MoE-4x7B_eval_request_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-15T18:00:24Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 735,
-    "job_start_time": "2024-05-25T22-30-56.294232",
-    "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.96 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.66 GiB memory in use. Of the allocated memory 39.50 GiB is allocated by PyTorch, and 61.16 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 29.96 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.66 GiB memory in use. Of the allocated memory 39.50 GiB is allocated by PyTorch, and 61.16 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"
 }

     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-15T18:00:24Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 735,
+    "job_start_time": "2024-05-25T22-30-56.294232"
 }

Qwen/Qwen1.5-110B_eval_request_False_4bit_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "Qwen2ForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-23T02:02:29Z",
     "model_type": "🟢 : pretrained",
     "source": "leaderboard",
     "job_id": 741,
-    "job_start_time": "2024-05-26T01-35-11.153523",
-    "error_msg": "CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.15 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.99 GiB memory in use. Of the allocated memory 39.41 GiB is allocated by PyTorch, and 116.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 889, in _load_state_dict_into_meta_model\n    hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/quantizers/quantizer_bnb_4bit.py\", line 216, in create_quantized_param\n    new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 324, in to\n    return self._quantize(device)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 289, in _quantize\n    w_4bit, quant_state = bnb.functional.quantize_4bit(\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/functional.py\", line 1169, in quantize_4bit\n    out = torch.zeros(((n + 1) // mod, 1), dtype=quant_storage, device=A.device)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.15 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.99 GiB memory in use. Of the allocated memory 39.41 GiB is allocated by PyTorch, and 116.11 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n"
 }

     "architectures": "Qwen2ForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-23T02:02:29Z",
     "model_type": "🟢 : pretrained",
     "source": "leaderboard",
     "job_id": 741,
+    "job_start_time": "2024-05-26T01-35-11.153523"
 }

Weyaxi/Bagel-Hermes-34B-Slerp_eval_request_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-15T21:22:26Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 736,
-    "job_start_time": "2024-05-25T22-33-12.695510",
-    "error_msg": "CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.47 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 998.00 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.47 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 998.00 KiB 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-15T21:22:26Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 736,
+    "job_start_time": "2024-05-25T22-33-12.695510"
 }

WizardLMTeam/WizardLM-13B-V1.0_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-22T01:02:59Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 738,
-    "job_start_time": "2024-05-25T22-37-34.851892",
-    "error_msg": "CUDA out of memory. Tried to allocate 430.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.68 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.94 GiB memory in use. Of the allocated memory 35.74 GiB is allocated by PyTorch, and 3.74 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 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 727, in forward\n    hidden_states = self.mlp(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/transformers/models/llama/modeling_llama.py\", line 216, in forward\n    down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n                               ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 430.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.68 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.94 GiB memory in use. Of the allocated memory 35.74 GiB is allocated by PyTorch, and 3.74 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-22T01:02:59Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 738,
+    "job_start_time": "2024-05-25T22-37-34.851892"
 }

Xwin-LM/Xwin-LM-13B-V0.1_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-22T01:13:16Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 739,
-    "job_start_time": "2024-05-25T23-44-57.849182",
-    "error_msg": "CUDA out of memory. Tried to allocate 98.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.10 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 41.04 GiB memory in use. Of the allocated memory 37.10 GiB is allocated by PyTorch, and 2.48 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 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 156, in update\n    self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)\n                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 98.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.10 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 41.04 GiB memory in use. Of the allocated memory 37.10 GiB is allocated by PyTorch, and 2.48 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-22T01:13:16Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 739,
+    "job_start_time": "2024-05-25T23-44-57.849182"
 }

Xwin-LM/Xwin-LM-13B-V0.2_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-22T01:13:35Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 740,
-    "job_start_time": "2024-05-26T00-38-56.863712",
-    "error_msg": "CUDA out of memory. Tried to allocate 430.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.25 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.89 GiB memory in use. Of the allocated memory 35.41 GiB is allocated by PyTorch, and 4.02 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 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 727, in forward\n    hidden_states = self.mlp(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/transformers/models/llama/modeling_llama.py\", line 216, in forward\n    down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n                               ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 430.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.25 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.89 GiB memory in use. Of the allocated memory 35.41 GiB is allocated by PyTorch, and 4.02 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-22T01:13:35Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 740,
+    "job_start_time": "2024-05-26T00-38-56.863712"
 }

alpindale/WizardLM-2-8x22B_eval_request_False_4bit_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-22T00:58:38Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 738,
-    "job_start_time": "2024-05-25T22-37-33.803025",
-    "error_msg": "CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.38 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.76 GiB memory in use. Of the allocated memory 39.34 GiB is allocated by PyTorch, and 327.89 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 889, in _load_state_dict_into_meta_model\n    hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/quantizers/quantizer_bnb_4bit.py\", line 216, in create_quantized_param\n    new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 324, in to\n    return self._quantize(device)\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/nn/modules.py\", line 289, in _quantize\n    w_4bit, quant_state = bnb.functional.quantize_4bit(\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/bitsandbytes/functional.py\", line 1165, in quantize_4bit\n    absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntorch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.38 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.76 GiB memory in use. Of the allocated memory 39.34 GiB is allocated by PyTorch, and 327.89 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"
 }

     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-22T00:58:38Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 738,
+    "job_start_time": "2024-05-25T22-37-33.803025"
 }

cognitivecomputations/WizardLM-30B-Uncensored_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-22T01:03:18Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 735,
-    "job_start_time": "2024-05-25T23-11-20.193654",
-    "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",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-22T01:03:18Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 735,
+    "job_start_time": "2024-05-25T23-11-20.193654"
 }

cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_9ad9d14_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-25T06:10:49Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 729,
-    "job_start_time": "2024-05-25T15-39-42.803523",
-    "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 72.19 MiB is free. Process 3627427 has 79.27 GiB memory in use. Of the allocated memory 78.64 GiB is allocated by PyTorch, and 127.59 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 72.19 MiB is free. Process 3627427 has 79.27 GiB memory in use. Of the allocated memory 78.64 GiB is allocated by PyTorch, and 127.59 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"
 }

     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-25T06:10:49Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 729,
+    "job_start_time": "2024-05-25T15-39-42.803523"
 }

cognitivecomputations/dolphin-2.7-mixtral-8x7b_eval_request_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-13T16:00:21Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 732,
-    "job_start_time": "2024-05-25T18-46-35.987059",
-    "error_msg": "CUDA out of memory. Tried to allocate 32.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 24.19 MiB is free. Process 3627427 has 79.32 GiB memory in use. Of the allocated memory 78.69 GiB is allocated by PyTorch, and 127.59 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 32.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 24.19 MiB is free. Process 3627427 has 79.32 GiB memory in use. Of the allocated memory 78.69 GiB is allocated by PyTorch, and 127.59 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"
 }

     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-13T16:00:21Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 732,
+    "job_start_time": "2024-05-25T18-46-35.987059"
 }

fblgit/UNA-SimpleSmaug-34b-v1beta_eval_request_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-15T21:23:20Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 736,
-    "job_start_time": "2024-05-25T22-33-13.233264",
-    "error_msg": "CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.95 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 998.00 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.95 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 998.00 KiB 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-15T21:23:20Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 736,
+    "job_start_time": "2024-05-25T22-33-13.233264"
 }

jondurbin/bagel-dpo-34b-v0.5_eval_request_False_bfloat16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-16T08:00:20Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 737,
-    "job_start_time": "2024-05-25T22-35-27.550364",
-    "error_msg": "CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.47 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 1012.00 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.47 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.67 GiB memory in use. Of the allocated memory 39.58 GiB is allocated by PyTorch, and 1012.00 KiB 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-16T08:00:20Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 737,
+    "job_start_time": "2024-05-25T22-35-27.550364"
 }

lmsys/vicuna-33b-v1.3_eval_request_False_float16_Original.json CHANGED Viewed

@@ -7,13 +7,11 @@
     "params": 33.0,
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
-    "status": "FAILED",
     "submitted_time": "2024-03-05T16:46:42Z",
     "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 731,
     "job_start_time": "2024-05-25T17-08-30.528855",
-    "main_language": "English",
-    "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",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 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"
 }

     "params": 33.0,
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
+    "status": "RERUN",
     "submitted_time": "2024-03-05T16:46:42Z",
     "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 731,
     "job_start_time": "2024-05-25T17-08-30.528855",
+    "main_language": "English"
 }

migtissera/Tess-M-v1.3_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-16T16:32:50Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 737,
-    "job_start_time": "2024-05-25T22-35-28.436932",
-    "error_msg": "CUDA out of memory. Tried to allocate 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 30.01 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.62 GiB memory in use. Of the allocated memory 39.52 GiB is allocated by PyTorch, and 1012.00 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 280.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 30.01 GiB is free. Process 1279938 has 8.72 GiB memory in use. Process 412887 has 40.62 GiB memory in use. Of the allocated memory 39.52 GiB is allocated by PyTorch, and 1012.00 KiB 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"
 }

     "architectures": "LlamaForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-16T16:32:50Z",
     "model_type": "🔶 : fine-tuned/fp on domain-specific datasets",
     "source": "leaderboard",
     "job_id": 737,
+    "job_start_time": "2024-05-25T22-35-28.436932"
 }

mlabonne/Beyonder-4x7B-v3_eval_request_False_float16_Original.json CHANGED Viewed

@@ -8,12 +8,10 @@
     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
-    "status": "FAILED",
     "submitted_time": "2024-05-15T18:00:59Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 735,
-    "job_start_time": "2024-05-25T22-30-56.351551",
-    "error_msg": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 79.35 GiB of which 29.40 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.74 GiB memory in use. Of the allocated memory 39.54 GiB is allocated by PyTorch, and 112.90 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 198, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 70, in run_request\n    results = run_eval_on_model(\n              ^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/run_eval.py\", line 60, in run_eval_on_model\n    result = evaluate(\n             ^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/lm_eval_util.py\", line 145, in evaluate\n    results = evaluator.simple_evaluate(\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/utils.py\", line 419, in _wrapper\n    return fn(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/evaluator.py\", line 100, in simple_evaluate\n    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/api/model.py\", line 134, in create_from_arg_string\n    return cls(**args, **args2)\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 304, in __init__\n    self._create_model(\n  File \"/workspace/repos/llm_leaderboard/lm-evaluation-harness-pt/lm_eval/models/huggingface.py\", line 616, in _create_model\n    self._model = self.AUTO_MODEL_CLASS.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\n    return model_class.from_pretrained(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 3754, in from_pretrained\n    ) = cls._load_pretrained_model(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 4214, in _load_pretrained_model\n    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/transformers/modeling_utils.py\", line 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 29.40 GiB is free. Process 1279938 has 492.00 MiB memory in use. Process 1295240 has 8.72 GiB memory in use. Process 412902 has 40.74 GiB memory in use. Of the allocated memory 39.54 GiB is allocated by PyTorch, and 112.90 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"
 }

     "architectures": "MixtralForCausalLM",
     "weight_type": "Original",
     "main_language": "English",
+    "status": "RERUN",
     "submitted_time": "2024-05-15T18:00:59Z",
     "model_type": "🤝 : base merges and moerges",
     "source": "leaderboard",
     "job_id": 735,
+    "job_start_time": "2024-05-25T22-30-56.351551"
 }