L3-8B-Stheno-v3.2-GGUF-IQ-Imatrix
Maintainer: Lewdiculous
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| Property | Value |
|---|---|
| Run this model | Run 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 L3-8B-Stheno-v3.2-GGUF-IQ-Imatrix model, created by maintainer Lewdiculous, is a version of the Stheno model that has been further optimized and refined. It builds upon previous iterations like the L3-8B-Stheno-v3.1-GGUF-IQ-Imatrix and L3-8B-Stheno-v3.1 models created by Sao10K. The v3.2 version includes a mix of SFW and NSFW storywriting data, more instructional data, and other performance improvements.
Model inputs and outputs
The L3-8B-Stheno-v3.2-GGUF-IQ-Imatrix model is a text-to-text AI model that can generate and continue various types of text-based content. It is capable of tasks like roleplaying, storytelling, and general language understanding and generation.
Inputs
- Text prompts and instructions
Outputs
- Continuation of the input text
- Generated text in the style and tone of the prompt
- Responses to open-ended questions and prompts
Capabilities
The L3-8B-Stheno-v3.2-GGUF-IQ-Imatrix model is highly capable at roleplaying, immersing itself in character personas, and generating coherent and engaging text. It has been trained on a diverse dataset and can handle a wide range of topics and scenarios. The model has also been optimized for efficiency, offering multiple quantization options to balance quality and file size.
What can I use it for?
The L3-8B-Stheno-v3.2-GGUF-IQ-Imatrix model is well-suited for creative writing applications, interactive storytelling, and roleplaying scenarios. It could be used to power chatbots, text-based games, or interactive narrative experiences. The model's versatility also makes it potentially useful for general language tasks like summarization, question-answering, and content generation.
Things to try
Experiment with the model's roleplaying capabilities by providing detailed character descriptions and prompts. Try generating multi-turn dialogues or narratives and see how the model maintains coherence and consistency. Additionally, explore the model's performance on more instructional or task-oriented prompts to see how it adapts to different styles and genres of text.
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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