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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 was
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- We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, which allows the model to use bidirectional context to process 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 was based on [GPT-J (6B)](https://huggingface.co/EleutherAI/gpt-j-6B), created by [EleutherAI](https://www.eleuther.ai);
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- We used [UL2](https://github.com/google-research/google-research/tree/master/ul2)'s training objective, which allows the model to use bidirectional context to process 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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