xzyao Amrrs commited on
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fixed a simple typo, apologies if not required (#3)

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- fixed a simple typo, apologies if not required (29aa10c2c0d90c18fb408154e7c5ae5194b442ce)


Co-authored-by: amrrs <Amrrs@users.noreply.huggingface.co>

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  1. README.md +1 -1
README.md CHANGED
@@ -85,7 +85,7 @@ widget:
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  > With a new decentralized training algorithm, we fine-tuned GPT-J (6B) on 3.53 billion tokens, resulting in GPT-JT (6B), a model that outperforms many 100B+ parameter models on classification benchmarks.
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  We incorporated a collection of open techniques and datasets to build GPT-JT:
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- - GPT-JT is a folk of [EleutherAI](https://www.eleuther.ai)'s [GPT-J (6B)](https://huggingface.co/EleutherAI/gpt-j-6B);
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  - We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, allowing the model to see bidirectional context of the prompt;
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  - The model was trained on a large collection of diverse data, including [Chain-of-Thought (CoT)](https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html), [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3), [Natural-Instructions (NI) dataset](https://github.com/allenai/natural-instructions).
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  > With a new decentralized training algorithm, we fine-tuned GPT-J (6B) on 3.53 billion tokens, resulting in GPT-JT (6B), a model that outperforms many 100B+ parameter models on classification benchmarks.
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  We incorporated a collection of open techniques and datasets to build GPT-JT:
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+ - GPT-JT is a fork of [EleutherAI](https://www.eleuther.ai)'s [GPT-J (6B)](https://huggingface.co/EleutherAI/gpt-j-6B);
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  - We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, allowing the model to see bidirectional context of the prompt;
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  - The model was trained on a large collection of diverse data, including [Chain-of-Thought (CoT)](https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html), [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3), [Natural-Instructions (NI) dataset](https://github.com/allenai/natural-instructions).
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