Implement temperature + top- $k$ + top- $p$ sampling from a logits vector in numpy

Easy

Inference & Serving1 / 5

Chapter 241 / 5

Inference & Serving

Implement temperature + top- $k$ + top- $p$ sampling from a logits vector in numpy

Easy

Target interface

sample(logits, temperature=1.0, top_k=0, top_p=1.0, rng=None)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

import numpy as np

def sample(logits, temperature=1.0, top_k=0, top_p=1.0, rng=None):
    raise NotImplementedError

def _softmax(x):
    raise NotImplementedError

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Chapter 241 / 5

Inference & Serving

Implement temperature + top- $k$ + top- $p$ sampling from a logits vector in numpy

Easy

Target interface

sample(logits, temperature=1.0, top_k=0, top_p=1.0, rng=None)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.