datasets = [
    [
        dict(
            abbr='LongBench_passage_retrieval_en',
            eval_cfg=dict(
                evaluator=dict(
                    type='opencompass.datasets.LongBenchRetrievalEvaluator'),
                pred_role='BOT'),
            infer_cfg=dict(
                inferencer=dict(
                    max_out_len=32,
                    type='opencompass.openicl.icl_inferencer.GenInferencer'),
                prompt_template=dict(
                    template=dict(round=[
                        dict(
                            prompt=
                            'Here are 30 paragraphs from Wikipedia, along with an abstract. Please determine which paragraph the abstract is from.\n\n{context}\n\nThe following is an abstract.\n\n{input}\n\nPlease enter the number of the paragraph that the abstract is from. The answer format must be like "Paragraph 1", "Paragraph 2", etc.\n\nThe answer is: ',
                            role='HUMAN'),
                    ]),
                    type=
                    'opencompass.openicl.icl_prompt_template.PromptTemplate'),
                retriever=dict(
                    type='opencompass.openicl.icl_retriever.ZeroRetriever')),
            name='passage_retrieval_en',
            path='opencompass/Longbench',
            reader_cfg=dict(
                input_columns=[
                    'context',
                    'input',
                ],
                output_column='answers',
                test_split='test',
                train_split='test'),
            type='opencompass.datasets.LongBenchpassage_retrieval_enDataset'),
    ],
]
eval = dict(runner=dict(task=dict(dump_details=True)))
models = [
    dict(
        abbr='mask_deltanet',
        batch_size=128,
        max_seq_len=2048,
        model_kwargs=dict(
            device_map='auto',
            torch_dtype='torch.bfloat16',
            trust_remote_code=True),
        path='/mnt/jfzn/msj/train_exp/mask_deltanet_1B_rank4',
        run_cfg=dict(num_gpus=1),
        tokenizer_kwargs=dict(padding_side='left', truncation_side='left'),
        tokenizer_path='/mnt/jfzn/msj/train_exp/mask_gdn_1B_hrr-rank4',
        type='opencompass.models.HuggingFaceBaseModel'),
]
work_dir = 'outputs/default/20251128_162747'