pygmalion-2-13b
Maintainer: PygmalionAI
72
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The pygmalion-2-13b is an instruction-tuned version of the Llama-2 large language model, developed by PygmalionAI. It is biased towards fiction writing and conversation, and is an evolution of the earlier Pygmalion models. The model was trained on a mixture of regular instruction data alongside roleplay, fictional stories and conversations with synthetically generated instructions. While similar to the Pygmalion-13b and Pygmalion-7b models, the pygmalion-2-13b has been designed with a focus on more natural language interaction and creative writing tasks.
Model inputs and outputs
Inputs
- The model takes in text prompts, which can include special tokens such as
<|system|>,<|user|>and<|model|>to indicate different roles and control the length and style of the generated output.
Outputs
- The model generates text, primarily focused on creative writing, storytelling, and natural language conversations.
Capabilities
The pygmalion-2-13b model is particularly skilled at generating coherent and engaging fictional narratives, as well as natural-sounding dialogue. It can be used to aid in the creative writing process, generate character backstories and worldbuilding details, and facilitate interactive roleplay scenarios.
What can I use it for?
The pygmalion-2-13b model could be useful for a variety of creative and conversational applications, such as:
- Assisting writers and storytellers in ideation, character development, and narrative generation
- Powering interactive fiction and choose-your-own-adventure experiences
- Enabling more natural and immersive chatbots and virtual assistants
- Facilitating collaborative worldbuilding and roleplaying games
Things to try
One interesting aspect of the pygmalion-2-13b model is its ability to maintain coherent personas and narratives over longer conversational exchanges. You could try prompting the model with a character description and backstory, then engaging in an extended dialogue to see how it develops the character and storyline. Additionally, experimenting with different prompt structures and special tokens could yield interesting creative results.
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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