phi-3-mini-4k-instruct

Maintainer: lucataco

Total Score

76

Last updated 6/19/2024
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Model overview

The phi-3-mini-4k-instruct is a 3.8B parameter, lightweight, state-of-the-art open model trained with the Phi-3 datasets by lucataco. It is similar to other models like the reliberate-v3, absolutereality-v1.8.1, instant-id, and phi-2 in its capabilities.

Model inputs and outputs

The phi-3-mini-4k-instruct model takes a text prompt as input and generates text outputs. The key inputs include:

Inputs

  • Prompt: The text prompt to send to the model.
  • Max Length: The maximum number of tokens to generate.
  • Temperature: Adjusts the randomness of the outputs.
  • Top K: Samples from the top k most likely tokens when decoding text.
  • Top P: Samples from the top p percentage of most likely tokens when decoding text.
  • Repetition Penalty: Penalty for repeated words in the generated text.
  • System Prompt: The system prompt provided to the model.

Outputs

  • The model generates a list of text outputs based on the provided inputs.

Capabilities

The phi-3-mini-4k-instruct model is capable of generating text outputs based on the provided prompts. It can be used for a variety of language tasks, such as text generation, summarization, and question answering.

What can I use it for?

The phi-3-mini-4k-instruct model can be used for a variety of projects, such as creating chatbots, generating creative writing, or augmenting content creation workflows. It could be particularly useful for companies looking to automate certain text-based tasks or enhance their existing language models.

Things to try

One interesting thing to try with the phi-3-mini-4k-instruct model is to experiment with different temperature and top-k/top-p settings to see how they affect the diversity and coherence of the generated text. You could also try providing more detailed or specific prompts to see how the model responds and whether it can generate relevant and informative outputs.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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