Create README.md
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README.md
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---
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language:
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- en
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datasets:
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- mindchain/wikitext2
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- yahma/alpaca-cleaned
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metrics:
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- perplexity
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- accuracy
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base_model:
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- TinyLlama/TinyLlama_v1.1
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model-index:
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- name: TinyLlama_v1.1_1bit_BitDistiller
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results:
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- task:
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type: multiple-choice
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name: QA Benchmarking
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dataset:
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type: allenai/arc
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name: ARC-Challenge
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config: challenge
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split: test
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metrics:
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- type: accuracy
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name: Accuracy
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value: 0.2150170648464164
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- type: accuracy
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name: Normalized Accuracy
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value: 0.24744027303754265
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- task:
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type: multiple-choice
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name: QA Benchmarking
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dataset:
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type: hellaswag
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name: HellaSwag
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split: test
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metrics:
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- type: accuracy
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name: Accuracy
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value: 0.2568213503286198
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- type: accuracy
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name: Normalized Accuracy
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value: 0.253359888468433
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| 44 |
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- task:
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type: multiple-choice
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name: QA Benchmarking
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dataset:
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type: piqa
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name: PIQA
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split: validation
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metrics:
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- type: accuracy
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name: Accuracy
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| 54 |
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value: 0.5282916213275299
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| 55 |
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- type: accuracy
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| 56 |
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name: Normalized Accuracy
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| 57 |
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value: 0.5027203482845702
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| 58 |
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- task:
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| 59 |
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type: multiple-choice
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| 60 |
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name: QA Benchmarking
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| 61 |
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dataset:
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| 62 |
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type: winogrande
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name: Winogrande
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| 64 |
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split: test
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metrics:
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| 66 |
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- type: accuracy
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| 67 |
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name: Accuracy
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| 68 |
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value: 0.5122336227308603
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| 69 |
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- task:
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| 70 |
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type: multiple-choice
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| 71 |
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name: QA Benchmarking
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| 72 |
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dataset:
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type: aggregated
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| 74 |
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name: QA-Avg
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metrics:
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- type: accuracy
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name: QA Average
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value: 0.3780991480835666
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| 79 |
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---
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