Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- tatsu-lab/alpaca
+---
+
+## 🍮 🦙 Flan-Alpaca: Instruction Tuning from Humans and Machines
+
+Thanks to [declare-lab](https://huggingface.co/declare-lab) for the training [repository](https://github.com/declare-lab/flan-alpaca), contains code for extending the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
+synthetic instruction tuning to existing instruction-tuned models such as [Flan-T5](https://arxiv.org/abs/2210.11416).
+The pretrained models and demos are available on HuggingFace 🤗 :
+
+| Model                                                                     | Parameters | Training GPUs   |
+|---------------------------------------------------------------------------|------------|-----------------|
+| [Flan-Alpaca-Base](https://huggingface.co/declare-lab/flan-alpaca-base)   | 220M       | 1x A6000        |
+| [Flan-Alpaca-Large](https://huggingface.co/declare-lab/flan-alpaca-large) | 770M       | 1x A6000        |
+| [Flan-Alpaca-XL](https://huggingface.co/declare-lab/flan-alpaca-xl)       | 3B         | 1x A6000        |
+| [Flan-Alpaca-XXL](https://huggingface.co/declare-lab/flan-alpaca-xxl)     | 11B        | 4x A6000 (FSDP) |
+| [Flan-Alpaca-UL2](https://huggingface.co/0-hero/flan-alpaca-ul2)          | 20B        | 4x A100 (80G) (FSDP)  |
+
+### Why?
+
+[Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) represents an exciting new direction
+to approximate the performance of large language models (LLMs) like ChatGPT cheaply and easily.
+Concretely, they leverage an LLM such as GPT-3 to generate instructions as synthetic training data.
+The synthetic data which covers more than 50k tasks can then be used to finetune a smaller model.
+However, the original implementation is less accessible due to licensing constraints of the
+underlying [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) model.
+Furthermore, users have noted [potential noise](https://github.com/tloen/alpaca-lora/issues/65) in the synthetic
+dataset. Hence, it may be better to explore a fully accessible model that is already trained on high-quality (but
+less diverse) instructions such as [Flan-T5](https://arxiv.org/abs/2210.11416).
+
+### Usage
+
+```
+from transformers import pipeline
+
+prompt = "Write an email about an alpaca that likes flan"
+model = pipeline(model="0-hero/flan-alpaca-ul2")
+model(prompt, max_length=128, do_sample=True)
+
+```
+
+Readme forked from declare-lab/flan-alpaca-xxl