🍮 🦙 Flan-Alpaca: Instruction Tuning from Humans and Machines
📣 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 and Paper.
📣 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.
📣 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. 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
📣 FLAN-T5 is also useful in text-to-audio generation. Find our work at https://github.com/declare-lab/tango if you are interested.
Our repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5. We have a live interactive demo thanks to Joao Gante! We are also benchmarking many instruction-tuned models at declare-lab/flan-eval. Our pretrained models are fully available on HuggingFace 🤗 :
| Model | Parameters | Instruction Data | Training GPUs |
|---|---|---|---|
| Flan-Alpaca-Base | 220M | Flan, Alpaca | 1x A6000 |
| Flan-Alpaca-Large | 770M | Flan, Alpaca | 1x A6000 |
| Flan-Alpaca-XL | 3B | Flan, Alpaca | 1x A6000 |
| Flan-Alpaca-XXL | 11B | Flan, Alpaca | 4x A6000 (FSDP) |
| Flan-GPT4All-XL | 3B | Flan, GPT4All | 1x A6000 |
| Flan-ShareGPT-XL | 3B | Flan, ShareGPT/Vicuna | 1x A6000 |
| Flan-Alpaca-GPT4-XL* | 3B | Flan, GPT4-Alpaca | 1x A6000 |
*recommended for better performance
Why?
Alpaca 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 model. Furthermore, users have noted potential noise 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.
Usage
from transformers import pipeline
prompt = "Write an email about an alpaca that likes flan"
model = pipeline(model="declare-lab/flan-alpaca-gpt4-xl")
model(prompt, max_length=128, do_sample=True)
# Dear AlpacaFriend,
# My name is Alpaca and I'm 10 years old.
# I'm excited to announce that I'm a big fan of flan!
# We like to eat it as a snack and I believe that it can help with our overall growth.
# I'd love to hear your feedback on this idea.
# Have a great day!
# Best, AL Paca