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  ---
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  license: apache-2.0
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  pipeline_tag: text2text-generation
 
 
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  ---
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  # SliceX AI™ ELM (Efficient Language Models)
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  **ELM** (which stands for **E**fficient **L**anguage **M**odels) is the first version in the series of cutting-edge language models from [SliceX AI](https://slicex.ai) that is designed to achieve the best in class performance in terms of _quality_, _throughput_ & _memory_.
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  This repository contains code to run our ELM models. The current ELM model `elm-v0.1` (named _Rambutan_) was pre-trained (an intermediate checkpoint was used) and then instruction fine-tuned for downstream tasks.
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  ELM models (in the `models` folder) in this repository come in three sizes (elm-1.0, elm-0.75 and elm-0.25). **All these different slices are derived from the same ELM-1.0 finetuned checkpoint** and supports the following use-case.
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- - news_classification
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  ```note
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  NOTE: ELM-v0.1 is an early version finetuned from an intermediate pretrained checkpoint & without any KV caching, decoding optimizations, or quantization applied.
 
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  ---
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  license: apache-2.0
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  pipeline_tag: text2text-generation
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+ datasets:
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+ - ag_news
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  ---
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  # SliceX AI™ ELM (Efficient Language Models)
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  **ELM** (which stands for **E**fficient **L**anguage **M**odels) is the first version in the series of cutting-edge language models from [SliceX AI](https://slicex.ai) that is designed to achieve the best in class performance in terms of _quality_, _throughput_ & _memory_.
 
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  This repository contains code to run our ELM models. The current ELM model `elm-v0.1` (named _Rambutan_) was pre-trained (an intermediate checkpoint was used) and then instruction fine-tuned for downstream tasks.
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  ELM models (in the `models` folder) in this repository come in three sizes (elm-1.0, elm-0.75 and elm-0.25). **All these different slices are derived from the same ELM-1.0 finetuned checkpoint** and supports the following use-case.
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+ - news_classification (ag_news)
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  ```note
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  NOTE: ELM-v0.1 is an early version finetuned from an intermediate pretrained checkpoint & without any KV caching, decoding optimizations, or quantization applied.