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update OpenELM-3B
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README.md
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@@ -8,7 +8,7 @@ license_link: LICENSE
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*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
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We introduce **OpenELM**, a family of **Open
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Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
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```bash
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# OpenELM-3B-Instruct
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hf_model=OpenELM-3B-Instruct
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# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
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tokenizer=meta-llama/Llama-2-7b-hf
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```BibTex
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@article{mehtaOpenELMEfficientLanguage2024,
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title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}
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shorttitle = {{OpenELM}},
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url = {https://arxiv.org/abs/2404.14619v1},
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language = {en},
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*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
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We introduce **OpenELM**, a family of **Open** **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.
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Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
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```bash
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# OpenELM-3B-Instruct
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hf_model=apple/OpenELM-3B-Instruct
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# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
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tokenizer=meta-llama/Llama-2-7b-hf
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```BibTex
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@article{mehtaOpenELMEfficientLanguage2024,
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title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}},
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shorttitle = {{OpenELM}},
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url = {https://arxiv.org/abs/2404.14619v1},
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language = {en},
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