Text Generation
Transformers
Safetensors
openelm
custom_code
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update OpenELM-3B

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  1. README.md +3 -3
README.md CHANGED
@@ -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**-source **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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@@ -106,7 +106,7 @@ pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0
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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
@@ -168,7 +168,7 @@ If you find our work useful, please cite:
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  ```BibTex
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  @article{mehtaOpenELMEfficientLanguage2024,
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- title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}-source {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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  *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},