Understanding Temperature, Top-p, and Top-k in LLMs

Large Language Models (LLMs) like GPT-4 use sampling techniques to generate text. Parameters such as temperature, top-p, and top-k play a crucial role in controlling the randomness and diversity of the generated output. In this post, we’ll explore these parameters, their effects, and how to configure them using the Python Gemini API.

Temperature 🌡️

The temperature parameter controls the randomness of the model's output. It adjusts the probability distribution of the next token:

Low temperature (e.g., 0.2): Makes the output more deterministic and focused. The model is more likely to choose high-probability tokens.
High temperature (e.g., 1.0 or above): Increases randomness, leading to more creative but less predictable outputs.

Example with Gemini API

In the Gemini API, you can set the temperature parameter to control randomness:

from gemini import GeminiClient

client = GeminiClient(api_key="your_api_key")

response = client.generate(
    prompt="Write a short story about a robot learning to cook.",
    temperature=0.7  # Adjust randomness
)

print(response.text)

If you set temperature=0, the model will behave deterministically, always choosing the most likely token.

Top-p (Nucleus Sampling) 🎯

The top-p parameter (also known as nucleus sampling) controls the cumulative probability of the tokens considered for generation:

Low top-p (e.g., 0.1): Limits the model to only the most probable tokens, making the output more focused.
High top-p (e.g., 0.9): Includes a wider range of tokens, increasing diversity.

When top-p=1.0, all tokens are considered, effectively disabling nucleus sampling.

Example with Gemini API

You can set top-p in the Gemini API to control the diversity of the output:

response = client.generate(
    prompt="Explain the concept of entropy in physics.",
    top_p=0.9  # Include 90% of the cumulative probability
)

print(response.text)

0.9 means Include 90% of the cumulative probability.

Top-k 🔢

The top-k parameter limits the number of tokens considered for generation to the top k most probable tokens:

Low top-k (e.g., 10): Restricts the model to a small set of high-probability tokens, reducing randomness.
High top-k (e.g., 50 or 100): Allows more tokens to be considered, increasing diversity.

When top-k=0, this parameter is effectively disabled, and all tokens are considered.

Example with Gemini API

The Gemini API does not currently support setting top-k directly. However, if supported in the future, it would PROBABLY look something like this:

response = client.generate(
    prompt="Describe the lifecycle of a star.",
    top_k=50  # Consider the top 50 tokens
)

print(response.text)

For now, you can combine temperature and top-p to achieve similar effects.

Combining Parameters 🛠️

You can combine temperature, top-p, and top-k to fine-tune the model's behavior. For example:

Use a low temperature with a low top-p for focused and deterministic outputs.
Use a high temperature with a high top-p for creative and diverse outputs.

Example with Gemini API

response = client.generate(
    prompt="What are the ethical implications of AI in healthcare?",
    temperature=0.8,  # Add some randomness
    top_p=0.85        # Include 85% of the cumulative probability
)

print(response.text)

Key Notes ⚠️

temperature and top-p are supported in the Gemini API, but top-k is not currently configurable.
Setting both temperature and top-p together allows for fine-grained control over the output.
Experiment with these parameters to find the right balance between creativity and focus for your use case.

Conclusion 🏆

Understanding and configuring temperature, top-p, and top-k can significantly impact the behavior of LLMs. By adjusting these parameters, you can tailor the model's output to suit your specific needs, whether it's generating deterministic responses or exploring creative possibilities. The Gemini API makes it easy to experiment with these settings and unlock the full potential of LLMs.