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README.md
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# TL;DR
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> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
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# Model Details
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## Model Description
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> These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size.
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# Evaluation
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## Testing Data, Factors & Metrics
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The
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For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf).
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** Google Cloud TPU Pods
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- **Hours used:** More information needed
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- **Cloud Provider:** GCP
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- **Compute Region:** More information needed
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# TL;DR
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If you already know T5, FLAN-T5 is just better at everything! It is a **bigger model (+ XX parameters)** was trained on **more tasks (+ XX )**, **more data (+ XX tokens)** and **more languags (+ XX languages)**. As mentioned in the first few lines of the abstract :
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> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
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**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large).
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# Model Details
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## Model Description
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> These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size.
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The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax).
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# Evaluation
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## Testing Data, Factors & Metrics
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The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:
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For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf).
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
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- **Hours used:** More information needed
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- **Cloud Provider:** GCP
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- **Compute Region:** More information needed
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