datasets = [ [ dict( abbr='LongBench_trec', eval_cfg=dict( evaluator=dict( type='opencompass.datasets.LongBenchClassificationEvaluator' ), pred_postprocessor=dict( type='opencompass.datasets.trec_postprocess'), pred_role='BOT'), infer_cfg=dict( inferencer=dict( max_out_len=64, type='opencompass.openicl.icl_inferencer.GenInferencer'), prompt_template=dict( template=dict(round=[ dict( prompt= 'Please determine the type of the question below. Here are some examples of questions.\n\n{context}\n{input}', role='HUMAN'), ]), type= 'opencompass.openicl.icl_prompt_template.PromptTemplate'), retriever=dict( type='opencompass.openicl.icl_retriever.ZeroRetriever')), name='trec', path='opencompass/Longbench', reader_cfg=dict( input_columns=[ 'context', 'input', ], output_column='all_labels', test_split='test', train_split='test'), type='opencompass.datasets.LongBenchtrecDataset'), ], ] eval = dict(runner=dict(task=dict(dump_details=True))) models = [ dict( abbr='gated_deltanet', batch_size=128, max_seq_len=2048, model_kwargs=dict( device_map='auto', torch_dtype='torch.bfloat16', trust_remote_code=True), path='download_model/hgrn2-1.3B-100B', run_cfg=dict(num_gpus=1), tokenizer_kwargs=dict(padding_side='left', truncation_side='left'), tokenizer_path='download_model/hgrn2-1.3B-100B', type='opencompass.models.HuggingFaceBaseModel'), ] work_dir = 'outputs/default/20251219_164057'