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Trivial: standardize single curly quotes (don't ask)

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  1. README.md +3 -3
README.md CHANGED
@@ -10,7 +10,7 @@ datasets:
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  # dolly-v2-7b Model Card
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  ## Summary
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- Databricks `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform
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  that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records
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  [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated
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  by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
@@ -29,7 +29,7 @@ running inference for various GPU configurations.
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  ## Model Overview
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  `dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from
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- [EleutherAIs](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned
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  on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA)
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  ## Usage
@@ -139,7 +139,7 @@ Moreover, we find that `dolly-v2-7b` does not have some capabilities, such as we
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  ### Dataset Limitations
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  Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses.
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- - **The Pile**: GPT-Js pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
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  it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
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  in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
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  associations.
 
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  # dolly-v2-7b Model Card
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  ## Summary
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+ Databricks' `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform
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  that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records
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  [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated
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  by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation,
 
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  ## Model Overview
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  `dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from
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+ [EleutherAI's](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned
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  on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA)
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  ## Usage
 
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  ### Dataset Limitations
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  Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses.
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+ - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,
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  it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly
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  in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit
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  associations.