Update dominguesm/mambarim-110m-chat_eval_request_False_float16_Adapter.json

dominguesm/mambarim-110m-chat_eval_request_False_float16_Adapter.json

@@ -8,12 +8,10 @@
     "architectures": "?",
     "weight_type": "Adapter",
     "main_language": "Portuguese",
-    "status": "FAILED",
+    "status": "RERUN",
     "submitted_time": "2024-05-06T09:36:01Z",
     "model_type": "💬 : chat (RLHF, DPO, IFT, ...)",
     "source": "leaderboard",
     "job_id": 591,
-    "job_start_time": "2024-05-06T09-38-22.866304",
-    "error_msg": "Error(s) in loading state_dict for PeftModelForCausalLM:\n\tsize mismatch for base_model.model.backbone.embeddings.base_layer.weight: copying a param with shape torch.Size([32002, 768]) from checkpoint, the shape in current model is torch.Size([32000, 768]).\n\tsize mismatch for base_model.model.backbone.embeddings.lora_embedding_A.default: copying a param with shape torch.Size([8, 32002]) from checkpoint, the shape in current model is torch.Size([8, 32000]).\n\tsize mismatch for base_model.model.lm_head.base_layer.weight: copying a param with shape torch.Size([32002, 768]) from checkpoint, the shape in current model is torch.Size([32000, 768]).\n\tsize mismatch for base_model.model.lm_head.lora_B.default.weight: copying a param with shape torch.Size([32002, 8]) from checkpoint, the shape in current model is torch.Size([32000, 8]).",
-    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 190, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 69, 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 637, in _create_model\n    self._model = PeftModel.from_pretrained(\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/peft/peft_model.py\", line 356, in from_pretrained\n    model.load_adapter(model_id, adapter_name, is_trainable=is_trainable, **kwargs)\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/peft/peft_model.py\", line 730, in load_adapter\n    load_result = set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/peft/utils/save_and_load.py\", line 249, in set_peft_model_state_dict\n    load_result = model.load_state_dict(peft_model_state_dict, strict=False)\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/miniconda3/envs/torch21/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 2153, in load_state_dict\n    raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\nRuntimeError: Error(s) in loading state_dict for PeftModelForCausalLM:\n\tsize mismatch for base_model.model.backbone.embeddings.base_layer.weight: copying a param with shape torch.Size([32002, 768]) from checkpoint, the shape in current model is torch.Size([32000, 768]).\n\tsize mismatch for base_model.model.backbone.embeddings.lora_embedding_A.default: copying a param with shape torch.Size([8, 32002]) from checkpoint, the shape in current model is torch.Size([8, 32000]).\n\tsize mismatch for base_model.model.lm_head.base_layer.weight: copying a param with shape torch.Size([32002, 768]) from checkpoint, the shape in current model is torch.Size([32000, 768]).\n\tsize mismatch for base_model.model.lm_head.lora_B.default.weight: copying a param with shape torch.Size([32002, 8]) from checkpoint, the shape in current model is torch.Size([32000, 8]).\n"
+    "job_start_time": "2024-05-06T09-38-22.866304"
 }