3. Top-p – is similar to top-k, except the choice scoping is not by the token count, but by the sum of the token probabilities. By the way, you can try out the prompt here: https://
os.cohere.ai/custom-preset?
ref=Startup-Idea-Generator-nyrnln&e=generate
…
PROMPT ENGINEERING
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Top-p Sampling Technique for Language Models Explained
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Temperature vs Top-k/Top-p: Token Selection Explained
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How is it different from temperature? In a nutshell:
• Temperature – how to choose between tokens
• Top-k/top-p – how many tokens to choose from As a result,
• Lower top-k/top-p = more predictable
• Higher top-k/top-p = more creative -
Top-k and Top-p: Controlling Model Token Selection
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2. Top-k (also top-p) – controls the list from which the model can make its choices. Increasing the top-k increases the number of tokens that the model can choose from, and vice versa.
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Low Temperature AI Generates Automatic To-Do List App
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With a low temperature (0.2), we get an idea of an automatic to-do list app. Interesting, but nothing revolutionary. (model-generated text in bold)
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Temperature Controls Model Token Generation Creativity
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1. Temperature – controls how the model chooses from its next choice of tokens. Increasing the temperature makes generating tokens with lower likelihoods more probable, and vice versa. As a result:
• Lower temperature = more predictable
• Higher temperature = more creative -
Understanding Token Likelihood Parameters in Language Models
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It does this by giving a “likelihood” number to each token. So, for the phrase “I like to bake …”, “cookies” has a higher likelihood than “chairs.” “chairs” can still appear, but “cookies” has a much higher chance. But we can change this behavior with those 3 parameters.
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Adjusting Model Parameters: Temperature, Top-k, Top-p
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So, how can you do this? By adjusting the model parameters. There are 3 parameters you can adjust:
1. Temperature
2. Top-k
3. Top-p But before we see how to use them, let’s first understand how the model selects the next token to generate. -
Human demonstrations limited early instruction tuning models
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Early attempts at instruction tuning relied entirely on demonstrations from humans. This made the model easier to prompt, but the approach was limited by the inherent difficulty of manufacturing new humans.
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Instruction tuning merges human will into AI controlled by awkward kid
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Instruction tuning / RLHF is technically a Human Instrumentality Project, merging the preferences of countless humans to form an oversized, living amalgam of our will. We then hand control of it to a random, socially awkward kid and hope for the best.
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Fine-tuned Models Testing on Public Datasets
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Interesting. What kind of data are you inferring on? Is there some publicly available data that you’re testing on? Would love to test out some of our fine-tuned models against it.