Weni/WeniGPT-QA-Zephyr-7B-4.0.2-KTO_eval_request_False_bfloat16_Adapter.json

{
    "model": "Weni/WeniGPT-QA-Zephyr-7B-4.0.2-KTO",
    "base_model": "HuggingFaceH4/zephyr-7b-beta",
    "revision": "main",
    "private": false,
    "precision": "bfloat16",
    "params": 7.0,
    "architectures": "?",
    "weight_type": "Adapter",
    "main_language": "Portuguese",
    "status": "FAILED",
    "submitted_time": "2024-04-09T02:55:53Z",
    "model_type": "💬 : chat models (RLHF, DPO, IFT, ...)",
    "source": "leaderboard",
    "job_id": 446,
    "job_start_time": "2024-04-14T03-34-47.129646",
    "error_msg": "Error(s) in loading state_dict for PeftModelForCausalLM:\n\tsize mismatch for base_model.model.model.embed_tokens.weight: copying a param with shape torch.Size([32002, 4096]) from checkpoint, the shape in current model is torch.Size([32000, 4096]).\n\tsize mismatch for base_model.model.lm_head.weight: copying a param with shape torch.Size([32002, 4096]) from checkpoint, the shape in current model is torch.Size([32000, 4096]).",
    "traceback": "Traceback (most recent call last):\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 196, in wait_download_and_run_request\n    run_request(\n  File \"/workspace/repos/llm_leaderboard/llm_leaderboard_eval_bot/evaluate_llms.py\", line 65, 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.model.embed_tokens.weight: copying a param with shape torch.Size([32002, 4096]) from checkpoint, the shape in current model is torch.Size([32000, 4096]).\n\tsize mismatch for base_model.model.lm_head.weight: copying a param with shape torch.Size([32002, 4096]) from checkpoint, the shape in current model is torch.Size([32000, 4096]).\n"
}