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Flan Alpaca Xxl



๐Ÿฎ ๐Ÿฆ™ Flan-Alpaca: Instruction Tuning from Humans and Machines ๐Ÿ“ฃ We developed Flacuna by fine-tuning Vicuna-13B on the Flan collection. Flacuna is better than Vicuna at problem-solving. Access the model here ๐Ÿ“ฃ Curious to know the performance of ๐Ÿฎ ๐Ÿฆ™ Flan-Alpaca on large-scale LLM evaluation benchmark, InstructEval? Read our paper 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: ๐Ÿ“ฃ FLAN-T5 is also useful in text-to-audio generation. Find our work at 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 ๐Ÿค— : *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


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Creator Models

Tango Full$?5
Tango Full Ft Audiocaps$?178
Segue W2v2 Base$?254
Flan Gpt4all Xl$?428
Tango Full Ft Audio Music Caps$?0

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Model NameFlan Alpaca Xxl

๐Ÿฎ ๐Ÿฆ™ Flan-Alpaca: Instruction Tuning from Humans and Machines ...

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