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--- |
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license: apache-2.0 |
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datasets: |
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- tatsu-lab/alpaca |
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--- |
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## 🍮 🦙 Flan-Alpaca: Instruction Tuning from Humans and Machines |
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📣 Introducing **Red-Eval** to evaluate the safety of the LLMs using several jailbreaking prompts. With **Red-Eval** one could jailbreak/red-team GPT-4 with a 65.1% attack success rate and ChatGPT could be jailbroken 73% of the time as measured on DangerousQA and HarmfulQA benchmarks. More details are here: [Code](https://github.com/declare-lab/red-instruct) and [Paper](https://arxiv.org/abs/2308.09662). |
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📣 We developed Flacuna by fine-tuning Vicuna-13B on the Flan collection. Flacuna is better than Vicuna at problem-solving. Access the model here [https://huggingface.co/declare-lab/flacuna-13b-v1.0](https://huggingface.co/declare-lab/flacuna-13b-v1.0). |
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📣 Curious to know the performance of 🍮 🦙 **Flan-Alpaca** on large-scale LLM evaluation benchmark, **InstructEval**? Read our paper [https://arxiv.org/pdf/2306.04757.pdf](https://arxiv.org/pdf/2306.04757.pdf). We evaluated more than 10 open-source instruction-tuned LLMs belonging to various LLM families including Pythia, LLaMA, T5, UL2, OPT, and Mosaic. Codes and datasets: [https://github.com/declare-lab/instruct-eval](https://github.com/declare-lab/instruct-eval) |
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📣 **FLAN-T5** is also useful in text-to-audio generation. Find our work at [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango) if you are interested. |
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Our [repository](https://github.com/declare-lab/flan-alpaca) contains code for extending the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) |
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synthetic instruction tuning to existing instruction-tuned models such as [Flan-T5](https://arxiv.org/abs/2210.11416). |
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We have a [live interactive demo](https://huggingface.co/spaces/joaogante/transformers_streaming) thanks to [Joao Gante](https://huggingface.co/joaogante)! |
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We are also benchmarking many instruction-tuned models at [declare-lab/flan-eval](https://github.com/declare-lab/flan-eval). |
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Our pretrained models are fully available on HuggingFace 🤗 : |
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| Model | Parameters | Instruction Data | Training GPUs | |
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|----------------------------------------------------------------------------------|------------|----------------------------------------------------------------------------------------------------------------------------------------------------|-----------------| |
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| [Flan-Alpaca-Base](https://huggingface.co/declare-lab/flan-alpaca-base) | 220M | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 | |
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| [Flan-Alpaca-Large](https://huggingface.co/declare-lab/flan-alpaca-large) | 770M | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 | |
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| [Flan-Alpaca-XL](https://huggingface.co/declare-lab/flan-alpaca-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 1x A6000 | |
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| [Flan-Alpaca-XXL](https://huggingface.co/declare-lab/flan-alpaca-xxl) | 11B | [Flan](https://github.com/google-research/FLAN), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 4x A6000 (FSDP) | |
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| [Flan-GPT4All-XL](https://huggingface.co/declare-lab/flan-gpt4all-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [GPT4All](https://github.com/nomic-ai/gpt4all) | 1x A6000 | |
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| [Flan-ShareGPT-XL](https://huggingface.co/declare-lab/flan-sharegpt-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [ShareGPT](https://github.com/domeccleston/sharegpt)/[Vicuna](https://github.com/lm-sys/FastChat) | 1x A6000 | |
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| [Flan-Alpaca-GPT4-XL*](https://huggingface.co/declare-lab/flan-alpaca-gpt4-xl) | 3B | [Flan](https://github.com/google-research/FLAN), [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | 1x A6000 | |
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*recommended for better performance |
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### Why? |
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[Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) represents an exciting new direction |
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to approximate the performance of large language models (LLMs) like ChatGPT cheaply and easily. |
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Concretely, they leverage an LLM such as GPT-3 to generate instructions as synthetic training data. |
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The synthetic data which covers more than 50k tasks can then be used to finetune a smaller model. |
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However, the original implementation is less accessible due to licensing constraints of the |
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underlying [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) model. |
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Furthermore, users have noted [potential noise](https://github.com/tloen/alpaca-lora/issues/65) in the synthetic |
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dataset. Hence, it may be better to explore a fully accessible model that is already trained on high-quality (but |
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less diverse) instructions such as [Flan-T5](https://arxiv.org/abs/2210.11416). |
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### Usage |
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``` |
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from transformers import pipeline |
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prompt = "Write an email about an alpaca that likes flan" |
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model = pipeline(model="declare-lab/flan-alpaca-gpt4-xl") |
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model(prompt, max_length=128, do_sample=True) |
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# Dear AlpacaFriend, |
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# My name is Alpaca and I'm 10 years old. |
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# I'm excited to announce that I'm a big fan of flan! |
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# We like to eat it as a snack and I believe that it can help with our overall growth. |
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# I'd love to hear your feedback on this idea. |
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# Have a great day! |
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# Best, AL Paca |
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``` |
