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@@ -51,15 +51,15 @@ It achieves the following results on the evaluation set:
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  ## Intended uses & limitations
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- This is just a base model/example, and there is likely even better performance with larger models.
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- Additionally, this was trained on a dataset of **only** instructions+outputs with the `inputs` filtered out/dropped. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following icecream flavors: oreo, mint chip, chocolate chip, cookies and cream"*
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  ## Training and evaluation data
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  See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text.
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- - some of the API examples are purposefully weird to demonstrate generalizability of the model
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  ## Training procedure
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  ## Intended uses & limitations
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+ This is just a base model/example. There is likely to be even better performance with larger models.
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+ Additionally, this was trained on a dataset of **only** instructions+outputs, with the `inputs` filtered out. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"*.
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  ## Training and evaluation data
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  See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text.
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+ - Some of the API examples are intentionally weird to demonstrate the generalizability of the model.
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  ## Training procedure
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