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# LMFlow
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[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/OptimalScale/LMFlow/blob/main/LICENSE)
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[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)
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[![Doc](https://img.shields.io/badge/Website-Doc-orange.svg)](https://optimalscale.github.io/LMFlow/)
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## Model Performance
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| | PubMedQA | MedQA-USMLE | MedMCQA | Average |
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| | PubMedQA (ID) | MedQA-USMLE (OOD) | MedMCQA (ID) | Average |
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|:---------:|:--------:|:-----------:|:-------:|:----:|
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| Human (pass) | 60.0 | 50.0 | | |
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| Human (expert) | 78.0 | 87.0 | 90.0 | 85.0 |
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| LLaMA 30B | 1.8 | 43.4 | 30.3 | 25.2 |
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| | | | | | |
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| Task-tuned LLaMA 7B (Full) | **75.1** | 44.5 | 49.9 | 56.5 |
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| Task-tuned LLaMA 30B (LoRA) | 74 | 51.3 | **50.2**|**58.5**|
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| Task-tuned LLaMA 30B (LoRA) | 74.0 | 51.3 | **50.2**|**58.5**|
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The LLaMA 30B (LoRA) performance is achieved with only **~16h** finetuning in a
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single 8 \* A100 server. For more performance, including instruction tuning
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results, please refer to our
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[Documentation](https://optimalscale.github.io/LMFlow/).
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The LLaMA 30B (LoRA) performance is achieved with only **~16h** finetuning on the training split of PubMedQA and MedMCQA with a single 8 \* A100 server.
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For more performance, including instruction tuning results, please refer to our [Documentation](https://optimalscale.github.io/LMFlow/).
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## Supported Pipelines
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