vicuna-13b
Maintainer: replicate
251
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
vicuna-13b is an open-source large language model (LLM) developed by Replicate. It is based on Meta's LLaMA model and has been fine-tuned on user-shared conversations from ShareGPT. According to the provided information, vicuna-13b outperforms comparable models like Stanford Alpaca, and reaches 90% of the quality of OpenAI's ChatGPT and Google Bard.
Model inputs and outputs
vicuna-13b is a text-based LLM that can be used to generate human-like responses to prompts. The model takes in a text prompt as input and produces a sequence of text as output.
Inputs
- Prompt: The text prompt that the model will use to generate a response.
- Seed: A seed for the random number generator, used for reproducibility.
- Debug: A boolean flag to enable debugging output.
- Top P: The percentage of most likely tokens to sample from when decoding text.
- Temperature: A parameter that adjusts the randomness of the model's outputs.
- Repetition Penalty: A penalty applied to repeated words in the generated text.
- Max Length: The maximum number of tokens to generate in the output.
Outputs
- Output: An array of strings representing the generated text.
Capabilities
vicuna-13b is capable of generating human-like responses to a wide variety of prompts, from open-ended conversations to task-oriented instructions. The model has shown strong performance in evaluations compared to other LLMs, suggesting it can be a powerful tool for applications like chatbots, content generation, and more.
What can I use it for?
vicuna-13b can be used for a variety of applications, such as:
- Developing conversational AI assistants or chatbots
- Generating text content like articles, stories, or product descriptions
- Providing task-oriented assistance, such as answering questions or providing instructions
- Exploring the capabilities of large language models and their potential use cases
Things to try
One interesting aspect of vicuna-13b is its ability to generate responses that capture the nuances and patterns of human conversation, as it was trained on real user interactions. You could try prompting the model with more open-ended or conversational prompts to see how it responds, or experiment with different parameter settings to explore the model's capabilities.
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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