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README.md
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should probably proofread and complete it, then remove this comment. -->
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<p align="center" width="100%">
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini/main/images/
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</p>
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# LaMini-GPT-124M
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[![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()
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This model is one of our LaMini model series in paper "[LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini)".
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini/).
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You can view other LaMini model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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<table>
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<thead>
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<tr>
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<th>Base model</th>
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<th colspan="4">LaMini series (#parameters)</th>
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</tr>
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</thead>
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<tbody>
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## Training Procedure
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<p align="center" width="100%">
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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</p>
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We initialize with [gpt2](https://huggingface.co/gpt2) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 124M.
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### Training Hyperparameters
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```bibtex
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@misc{lamini,
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title={LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions},
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author={},
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year={2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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should probably proofread and complete it, then remove this comment. -->
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<p align="center" width="100%">
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
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</p>
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# LaMini-GPT-124M
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[![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()
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This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
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You can view other LaMini-LM model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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<table>
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<thead>
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<tr>
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<th>Base model</th>
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<th colspan="4">LaMini-LM series (#parameters)</th>
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</tr>
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</thead>
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<tbody>
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## Training Procedure
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<p align="center" width="100%">
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<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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</p>
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We initialize with [gpt2](https://huggingface.co/gpt2) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 124M.
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### Training Hyperparameters
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```bibtex
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@misc{lamini,
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title={LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
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author={Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji},
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year={2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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