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*This model was released on 2023-03-03 and added to Hugging Face Transformers on 2023-06-20.*

# FLAN-UL2

[FLAN-UL2](https://huggingface.co/papers/2210.11416) investigates instruction finetuning of language models, focusing on scaling task diversity, model size, and chain-of-thought data. The authors show that instruction finetuning substantially improves performance across model families (PaLM, T5, U-PaLM), prompting strategies (zero-shot, few-shot, CoT), and benchmarks (MMLU, BBH, TyDiQA, MGSM). Notably, Flan-PaLM 540B finetuned on 1.8K tasks surpasses PaLM 540B by 9.4% on average and achieves state-of-the-art results, including 75.2% on five-shot MMLU. Additionally, publicly released Flan-T5 checkpoints demonstrate strong few-shot abilities rivaling much larger models, underscoring instruction finetuning as a broadly effective method for enhancing pretrained language models.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline("text-generation", model="google/flan-ul2", dtype="auto")
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ggoogle/flan-ul2", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- The model is heavy (~40GB in half precision). Load the model in 8-bit and use `device_map="auto"` to avoid out-of-memory issues.