prometheus-13b-v1.0

Maintainer: tomasmcm

Total Score

31

Last updated 6/9/2024
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PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkView on Arxiv

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Model overview

The prometheus-13b-v1.0 is an alternative to GPT-4 when evaluating large language models (LLMs) and reward models for reinforcement learning from human feedback (RLHF). It was developed by tomasmcm, the same creator behind the llamaguard-7b and qwen1.5-72b models. Similar to the codellama-13b and llava-13b models, the prometheus-13b-v1.0 is a 13 billion parameter model focused on specific capabilities.

Model inputs and outputs

The prometheus-13b-v1.0 model takes in a text prompt and generates output text. The input and output specifications are as follows:

Inputs

  • Prompt: The text prompt to send to the model.
  • Max Tokens: The maximum number of tokens to generate per output sequence.
  • Temperature: A float that controls the randomness of the sampling, with lower values making the model more deterministic and higher values making it more random.
  • Presence Penalty: A float that penalizes new tokens based on whether they appear in the generated text so far, with values > 0 encouraging the use of new tokens and values < 0 encouraging the repetition of tokens.
  • Frequency Penalty: A float that penalizes new tokens based on their frequency in the generated text so far, with values > 0 encouraging the use of new tokens and values < 0 encouraging the repetition of tokens.
  • Top K: An integer that controls the number of top tokens to consider, with -1 meaning to consider all tokens.
  • Top P: A float that controls the cumulative probability of the top tokens to consider, with values between 0 and 1.
  • Stop: A list of strings that stop the generation when they are generated.

Outputs

  • Output: The generated text output.

Capabilities

The prometheus-13b-v1.0 model is capable of generating high-quality text that can be used for a variety of tasks, such as content creation, question answering, and language modeling. It is particularly useful for evaluating the performance of other LLMs and reward models for RLHF.

What can I use it for?

The prometheus-13b-v1.0 model can be used for a variety of applications, such as:

  • Content creation: The model can be used to generate text for blog posts, articles, and other types of content.
  • Language modeling: The model can be used to evaluate the performance of other LLMs by comparing their outputs to the prometheus-13b-v1.0 model's outputs.
  • Reward modeling: The model can be used to evaluate the performance of reward models for RLHF by comparing their outputs to the prometheus-13b-v1.0 model's outputs.

Things to try

Some interesting things to try with the prometheus-13b-v1.0 model include:

  • Experimenting with different parameter settings, such as temperature and top-k/top-p, to see how they affect the model's output.
  • Comparing the model's outputs to those of other LLMs to evaluate its performance.
  • Using the model as a baseline for evaluating the performance of reward models for RLHF.
  • Exploring the model's capabilities in specific domains, such as question answering or content generation.


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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