v3.1.0

What's Changed

3.1.0

Features

New .explain() method 🎉

Better understand the source of a completion, specifically on how much each section of a prompt impacts the completion.

To get started, you can simply pass in a prompt you used with a model and the completion the model gave and generate an explanation:

from aleph_alpha_client import Client, CompletionRequest, ExplanationRequest, Prompt

client = Client(token=os.environ["AA_TOKEN"])
prompt = Prompt.from_text("An apple a day, ")
model_name = "luminous-extended"

# create a completion request
request = CompletionRequest(prompt=prompt, maximum_tokens=32)
response = client.complete(request, model=model_name)

# generate an explanation
request = ExplanationRequest(prompt=prompt, target=response.completions[0].completion)
response = client.explain(request, model=model_name)

To visually see the results, you can also use this in our Playground.

We also have more documentation and examples available for you to read.

AtMan (Attention Manipulation)

Under the hood, we are leveraging the method from our AtMan paper to help generate these explanations. And we've also exposed these controls anywhere you can submit us a prompt!

So if you have other use cases for attention manipulation, you can pass these AtMan controls as part of your prompt items.

from aleph_alpha_client import Prompt, Text, TextControl

Prompt([
  Text("Hello, World!", controls=[TextControl(start=0, length=5, factor=0.5)]),
  Image.from_url(
    "https://cdn-images-1.medium.com/max/1200/1*HunNdlTmoPj8EKpl-jqvBA.png",
    controls=[ImageControl(top=0.25, left=0.25, height=0.5, width=0.5, factor=2.0)]
  )
])

For more information, check out our documentation and examples.

Full Changelog: v3.0.0...v3.1.0