llamaguard-7b
Maintainer: tomasmcm
540
| 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
llamaguard-7b is a 7 billion parameter Llama 2-based model developed by tomasmcm that serves as an input-output safeguard. It is similar in size and capabilities to models like codellama-7b, codellama-7b-instruct, and other Llama-based models. The key focus of llamaguard-7b is on providing input-output safeguards to help ensure safe and responsible language model usage.
Model inputs and outputs
llamaguard-7b takes a text prompt as input and generates a text output. The model provides various input parameters to control the generation, including:
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, encouraging the use of new tokens.
- Frequency Penalty: A float that penalizes new tokens based on their frequency in the generated text so far, also encouraging the use of new 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, if generated, will stop the generation.
Outputs
- Output: The generated text output from the model.
Capabilities
llamaguard-7b is a capable language model that can be used for a variety of natural language processing tasks, such as text generation, summarization, and translation. It is particularly well-suited for applications that require safe and responsible language model usage, as it includes various safeguards and controls to help ensure the generated output is appropriate and aligned with the user's intentions.
What can I use it for?
llamaguard-7b can be used in a wide range of applications, including content creation, chatbots, virtual assistants, and even creative writing. Its input-output safeguards make it well-suited for use cases where the generated text needs to be carefully controlled, such as in educational, medical, or financial contexts. Additionally, the model's flexibility allows it to be fine-tuned or adapted for more specialized use cases, such as code generation or programming assistance.
Things to try
One interesting aspect of llamaguard-7b is its ability to generate text with a high degree of coherence and logical consistency, while still maintaining a sense of creativity and originality. Users can experiment with adjusting the model's input parameters, such as temperature and top-k/top-p values, to explore the range of outputs and find the sweet spot that works best for their specific use case. Additionally, users can try providing the model with different types of prompts, from creative writing exercises to task-oriented instructions, to see how it responds and adapts to different contexts.
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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