prometheus-13b-v1.0
Maintainer: tomasmcm
31
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | View 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-v1.0model'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-v1.0model'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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