From da0d8c05e10cae9b60cac3b3a734a78c4935ad53 Mon Sep 17 00:00:00 2001 From: Chi Wang Date: Fri, 21 Apr 2023 21:41:16 -0700 Subject: [PATCH] Blog post for LLM tuning (#986) * outline * revision * eval function signature * first draft * link * format * example * cleanup * average * move figure * tldr * bold * bold * tag --- README.md | 6 +- website/blog/.gitkeep | 0 .../img/level2algebra.png | Bin 0 -> 40332 bytes .../img/level3algebra.png | Bin 0 -> 41130 bytes .../img/level4algebra.png | Bin 0 -> 36724 bytes .../img/level5algebra.png | Bin 0 -> 35338 bytes .../blog/2023-04-21-LLM-tuning-math/index.mdx | 74 ++++++++++++++++++ website/blog/authors.yml | 5 ++ website/docs/Getting-Started.md | 7 +- website/docusaurus.config.js | 19 ++--- 10 files changed, 94 insertions(+), 17 deletions(-) delete mode 100644 website/blog/.gitkeep create mode 100644 website/blog/2023-04-21-LLM-tuning-math/img/level2algebra.png create mode 100644 website/blog/2023-04-21-LLM-tuning-math/img/level3algebra.png create mode 100644 website/blog/2023-04-21-LLM-tuning-math/img/level4algebra.png create mode 100644 website/blog/2023-04-21-LLM-tuning-math/img/level5algebra.png create mode 100644 website/blog/2023-04-21-LLM-tuning-math/index.mdx create mode 100644 website/blog/authors.yml diff --git a/README.md b/README.md index a91851b349..6ade62fde7 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Build](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml/badge.svg)](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml) ![Python Version](https://img.shields.io/badge/3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue) [![Downloads](https://pepy.tech/badge/flaml)](https://pepy.tech/project/flaml) -[![Join the chat at https://gitter.im/FLAMLer/community](https://badges.gitter.im/FLAMLer/community.svg)](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) + [![](https://img.shields.io/discord/1025786666260111483?logo=discord&style=flat)](https://discord.gg/Cppx2vSPVP) @@ -117,11 +117,9 @@ You can find a detailed documentation about FLAML [here](https://microsoft.githu In addition, you can find: -- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML. - - Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research). -- FAQ [here](https://microsoft.github.io/FLAML/docs/FAQ). +- Discord [here](https://discord.gg/Cppx2vSPVP). - Contributing guide [here](https://microsoft.github.io/FLAML/docs/Contribute). diff --git a/website/blog/.gitkeep b/website/blog/.gitkeep deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/website/blog/2023-04-21-LLM-tuning-math/img/level2algebra.png b/website/blog/2023-04-21-LLM-tuning-math/img/level2algebra.png new file mode 100644 index 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For example, if n = 5 and 3 of the responses contain a final answer 301 while 2 of the responses contain a final answer 159, we choose 301 as the final answer. This can help with resolving potential errors due to randomness. We use the average accuracy and average inference cost as the metric to evaluate the performance over a dataset. The inference cost of a particular instance is measured by the price per 1K tokens and the number of tokens consumed. + +## Experiment Results + +The first figure in this blog post shows the average accuracy and average inference cost of each configuration on the level 2 Algebra test set. + +Surprisingly, the tuned gpt-3.5-turbo model is selected as a better model and it vastly outperforms untuned gpt-4 in accuracy (92% vs. 70%) with equal or 2.5 times higher inference budget. +The same observation can be obtained on the level 3 Algebra test set. + +![level 3 algebra](img/level3algebra.png) + +However, the selected model changes on level 4 Algebra. + +![level 4 algebra](img/level4algebra.png) + +This time gpt-4 is selected as the best model. The tuned gpt-4 achieves much higher accuracy (56% vs. 44%) and lower cost than the untuned gpt-4. +On level 5 the result is similar. + +![level 5 algebra](img/level5algebra.png) + +We can see that FLAML has found different optimal model and inference parameters for each subset of a particular level, which shows that these parameters matter in cost-sensitive LLM applications and need to be carefully tuned or adapted. + +An example notebook to run these experiments can be found at: https://github.com/microsoft/FLAML/blob/v1.2.1/notebook/autogen_chatgpt.ipynb + +## Analysis and Discussion + +While gpt-3.5-turbo demonstrates competitive accuracy with voted answers in relatively easy algebra problems under the same inference budget, gpt-4 is a better choice for the most difficult problems. In general, through parameter tuning and model selection, we can identify the opportunity to save the expensive model for more challenging tasks, and improve the overall effectiveness of a budget-constrained system. + +There are many other alternative ways of solving math problems, which we have not covered in this blog post. When there are choices beyond the inference parameters, they can be generally tuned via [`flaml.tune`](docs/Use-Cases/Tune-User-Defined-Function). + +The need for model selection, parameter tuning and cost saving is not specific to the math problems. The [Auto-GPT](https://github.com/Significant-Gravitas/Auto-GPT) project is an example where high cost can easily prevent a generic complex task to be accomplished as it needs many LLM inference calls. + +## For Further Reading + +* [Research paper about the tuning technique](https://arxiv.org/abs/2303.04673) +* [Documentation about `flaml.autogen`](docs/Use-Cases/Auto-Generation) + +*Do you have any experience to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our [Discord](https://discord.gg/Cppx2vSPVP) server for discussion.* diff --git a/website/blog/authors.yml b/website/blog/authors.yml new file mode 100644 index 0000000000..f0746a203b --- /dev/null +++ b/website/blog/authors.yml @@ -0,0 +1,5 @@ +sonichi: + name: Chi Wang + title: Principal Researcher at Microsoft Research + url: https://www.linkedin.com/in/chi-wang-49b15b16/ + image_url: https://github.com/sonichi.png diff --git a/website/docs/Getting-Started.md b/website/docs/Getting-Started.md index 3fe35ca1ca..752ec1357e 100644 --- a/website/docs/Getting-Started.md +++ b/website/docs/Getting-Started.md @@ -108,11 +108,10 @@ The optimization can help you maximize the utility out of these expensive models ### Where to Go Next? -* Understand the use cases for [Task-oriented AutoML](Use-Cases/task-oriented-automl), [Tune user-defined function](Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](Use-Cases/Zero-Shot-AutoML). -* Find code examples under "Examples": from [AutoML - Classification](Examples/AutoML-Classification) to [Tune - PyTorch](Examples/Tune-PyTorch). -* Find [talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML. +* Understand the use cases for [Auto Generation](Use-Cases/Auto-Generation), [Task-oriented AutoML](Use-Cases/Task-Oriented-Automl), [Tune user-defined function](Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](Use-Cases/Zero-Shot-AutoML). +* Find code examples under "Examples": from [AutoGen - OpenAI](Examples/AutoGen-OpenAI) to [Tune - PyTorch](Examples/Tune-PyTorch). * Learn about [research](Research) around FLAML. -* Refer to [SDK](reference/automl/automl) and [FAQ](FAQ). +* Chat on [Discord](https://discord.gg/Cppx2vSPVP). If you like our project, please give it a [star](https://github.com/microsoft/FLAML/stargazers) on GitHub. If you are interested in contributing, please read [Contributor's Guide](Contribute). diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js index 67d574962b..715727bd45 100644 --- a/website/docusaurus.config.js +++ b/website/docusaurus.config.js @@ -32,6 +32,7 @@ module.exports = { position: 'left', label: 'SDK', }, + {to: 'blog', label: 'Blog', position: 'left'}, { type: 'doc', docId: 'FAQ', @@ -57,23 +58,23 @@ module.exports = { // }, // ], // }, - // { - // title: 'Community', - // items: [ + { + title: 'Community', + items: [ // // { // // label: 'Stack Overflow', // // href: 'https://stackoverflow.com/questions/tagged/pymarlin', // // }, - // // { - // // label: 'Discord', - // // href: 'https://discordapp.com/invite/docusaurus', - // // }, + { + label: 'Discord', + href: 'https://discord.gg/Cppx2vSPVP', + }, // // { // // label: 'Twitter', // // href: 'https://twitter.com/docusaurus', // // }, - // ], - // }, + ], + }, // { // title: 'More', // items: [