43 lines
2.6 KiB
Markdown
43 lines
2.6 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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datasets:
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- Locutusque/hercules-v2.0
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- CollectiveCognition/chats-data-2023-09-22
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language:
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- en
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---
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# lr-experiment1-7B
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The lr-experiment model series is a research project I'm conducting that I will be using to determine the best learning rate to use while fine-tuning Mistral. This model uses a learning rate of 2e-5 with a cosine scheduler and no warmup steps.
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I used Locutusque/Hercules-2.0-Mistral-7B as a base model, and further fine-tuned it on CollectiveCognition/chats-data-2023-09-22 using QLoRA for 3 epochs. I will be keeping track of evaluation results, and will comparing it to upcoming models.
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# Evals
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
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|---------------------------------|-------|------|------|--------|-----:|---|-----:|
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|agieval_nous |N/A |none |None |acc |0.3645|± |0.0093|
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| | |none |None |acc_norm|0.3468|± |0.0092|
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| - agieval_aqua_rat | 1|none |None |acc |0.2283|± |0.0264|
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| | |none |None |acc_norm|0.2283|± |0.0264|
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| - agieval_logiqa_en | 1|none |None |acc |0.2965|± |0.0179|
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| | |none |None |acc_norm|0.3303|± |0.0184|
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| - agieval_lsat_ar | 1|none |None |acc |0.2217|± |0.0275|
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| | |none |None |acc_norm|0.1783|± |0.0253|
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| - agieval_lsat_lr | 1|none |None |acc |0.4039|± |0.0217|
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| | |none |None |acc_norm|0.3686|± |0.0214|
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| - agieval_lsat_rc | 1|none |None |acc |0.4870|± |0.0305|
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| | |none |None |acc_norm|0.4424|± |0.0303|
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| - agieval_sat_en | 1|none |None |acc |0.6408|± |0.0335|
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| | |none |None |acc_norm|0.5971|± |0.0343|
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| - agieval_sat_en_without_passage| 1|none |None |acc |0.3932|± |0.0341|
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| | |none |None |acc_norm|0.3835|± |0.0340|
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| - agieval_sat_math | 1|none |None |acc |0.3455|± |0.0321|
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| | |none |None |acc_norm|0.2727|± |0.0301|
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| Groups |Version|Filter|n-shot| Metric |Value | |Stderr|
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|------------|-------|------|------|--------|-----:|---|-----:|
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|agieval_nous|N/A |none |None |acc |0.3645|± |0.0093|
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| | |none |None |acc_norm|0.3468|± |0.0092| |