https://github.com/codecaine-zz/llm_temperature_cheatsheet

LLM temperature prompt cheat sheet
https://github.com/codecaine-zz/llm_temperature_cheatsheet

cheat guide llm ollama openai prompt sheet

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LLM temperature prompt cheat sheet

• Host: GitHub
• URL: https://github.com/codecaine-zz/llm_temperature_cheatsheet
• Owner: codecaine-zz
• Created: 2024-06-08T16:43:56.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-08T17:04:45.000Z (7 months ago)
• Last Synced: 2024-06-08T18:24:40.509Z (7 months ago)
• Topics: cheat, guide, llm, ollama, openai, prompt, sheet
• Homepage:
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Guide to Large Language Model Temperature Prompting

Welcome to the guide on using temperature prompting with large language models! In this readme, we will explore the concept of temperature and how it can be used to control the creativity and randomness of generated text from language models.

## What is Temperature?

Temperature is a parameter that can be adjusted when generating text using large language models. It controls the randomness and diversity of the generated output. A higher temperature value (e.g., 1.0) leads to more randomness and creative output, while a lower temperature value (e.g., 0.5) produces more focused and deterministic text.

## How to Use Temperature Prompting

To use temperature prompting with large language models, follow these steps:

1. Choose a large language model framework or library that supports temperature control, such as OpenAI's GPT-3 or Hugging Face's Transformers.

2. Set the desired temperature value before generating text. Most frameworks provide an option to specify the temperature parameter.

3. Experiment with different temperature values to achieve the desired level of randomness and creativity in the generated text.

4. Generate text using the language model, taking into account the specified temperature value.

5. Analyze the output and iterate on the temperature value if necessary to fine-tune the generated text.

Remember that the optimal temperature value may vary depending on the specific use case and desired output. It's recommended to experiment and adjust the temperature parameter accordingly.

## Conclusion

Temperature prompting is a powerful technique for controlling the creativity and randomness of generated text from large language models. By adjusting the temperature value, you can fine-tune the output to suit your specific needs. Experiment with different temperature values to find the right balance between creativity and coherence in your generated text.

Happy temperature prompting!