From 3ec6befd81dcf2413737115084475a65eea251ab Mon Sep 17 00:00:00 2001 From: mertyg Date: Tue, 11 Jun 2024 16:57:00 -0700 Subject: [PATCH] add index --- docs/source/index.rst | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/docs/source/index.rst b/docs/source/index.rst index 7cff16f..d22a059 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -29,6 +29,42 @@ This API is similar to the Pytorch API, making it simple to adapt to your use ca QuickStart ========== +If you know PyTorch, you know 80% of TextGrad. +Let's walk through the key components with a simple example. Say we want to use GPT-4o to generate a punchline for TextGrad. + +.. code-block:: python + + import textgrad as tg + # Step 1: Get an initial response from an LLM + model = tg.BlackboxLLM("gpt-4o") + punchline = model(tg.Variable("write a punchline for my github package about optimizing compound AI systems", role_description="prompt", requires_grad=False)) + punchline.set_role_description("a concise punchline that must hook everyone") + +Initial `punchline` from the model: +> Supercharge your AI synergy with our optimization toolkit – where compound intelligence meets peak performance! + +Not bad, but we (gpt-4o, I guess) can do better! Let's optimize the punchline using TextGrad. + +.. code-block:: python + + # Step 2: Define the loss function and the optimizer, just like in PyTorch! + loss_fn = tg.TextLoss("We want to have a super smart and funny punchline. Is the current one concise and addictive? Is the punch fun, makes sense, and subtle enough?") + optimizer = tg.TGD(parameters=[punchline]) + +.. code-block:: python + + # Step 3: Do the loss computation, backward pass, and update the punchline + loss = loss_fn(punchline) + loss.backward() + optimizer.step() + +Optimized punchline: +> Boost your AI with our toolkit – because even robots need a tune-up! + +Okay this model isn’t really ready for a comedy show yet (and maybe a bit cringy) but it is clearly trying. But who gets to maxima in one step? + +We have many more examples around how TextGrad can optimize all kinds of variables -- code, solutions to problems, molecules, prompts, and all that! + Tutorials ---------