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27 lines
1.4 KiB
Python
27 lines
1.4 KiB
Python
from mmengine.config import read_base
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with read_base():
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from .models.hf_internlm.lmdeploy_internlm2_chat_7b import models as internlm2_chat_7b_200k
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from .models.hf_internlm.hf_internlm2_chat_7b import models as internlm2_chat_7b
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# Evaluate needlebench_4k, adjust the configuration to use 8k, 32k, 128k, 200k, or 1000k if necessary.
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# from .datasets.needlebench.needlebench_4k.needlebench_4k import needlebench_datasets
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# from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
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# only eval original "needle in a haystack test" in needlebench_4k
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from .datasets.needlebench.needlebench_4k.needlebench_single_4k import needlebench_zh_datasets, needlebench_en_datasets
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from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
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# eval Ancestral Tracing Challenge(ATC)
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# from .datasets.needlebench.atc.atc_choice_50 import needlebench_datasets
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# from .summarizers.needlebench import atc_summarizer_50 as summarizer
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datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])
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for m in internlm2_chat_7b:
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m['max_seq_len'] = 32768 # Ensure InternLM2-7B model can receive the full length of long texts, adjust for other models based on their supported maximum sequence length.
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m['max_out_len'] = 2000 # Ensure complete responses from the model in multi-needle retrieval tasks.
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models = internlm2_chat_7b
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work_dir = './outputs/needlebench'
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