MPT-7B-Storywriter-GGML
Maintainer: TheBloke
53
👁️
| 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 MPT-7B-Storywriter-GGML is a version of the MPT-7B language model fine-tuned for story writing and long-form text generation. It was developed by MosaicML and is available in 4-bit, 5-bit and 8-bit GGML formats for efficient CPU and GPU inference. The model builds on the base MPT-7B architecture, which uses techniques like FlashAttention and ALiBi for fast training and inference. By fine-tuning on a dataset of long-form fiction, the MPT-7B-Storywriter-GGML model is optimized for generating coherent, engaging stories with extremely long context lengths.
Model inputs and outputs
Inputs
- Raw text prompts for story generation
Outputs
- Continued story text based on the provided prompt, with the ability to generate passages tens of thousands of tokens long.
Capabilities
The MPT-7B-Storywriter-GGML model excels at generating long-form fictional stories and narratives. It can take short prompts and continue them for thousands of tokens, maintaining coherence, plot, and character development throughout. The model's use of techniques like ALiBi allows it to handle context lengths far beyond the typical 2048 tokens seen in other language models.
What can I use it for?
The MPT-7B-Storywriter-GGML model is well-suited for applications that require long-form text generation, such as interactive storytelling, fiction writing assistance, and creative writing tools. Its ability to maintain coherence over extended passages makes it useful for generating novel-length stories or narratives from simple prompts.
Companies may find this model useful for building interactive fiction experiences, AI-generated books, or other creative content generation tools. The GGML format also allows for efficient on-device inference, opening up possibilities for mobile or embedded applications.
Things to try
One interesting thing to try with the MPT-7B-Storywriter-GGML model is to provide it with a short prompt - just a sentence or two - and see how it expands that into a lengthy, cohesive story. The model's strong grasp of narrative structure allows it to take simple beginnings and weave them into compelling tales. Experiment with different genres, character types, or story hooks to see the breadth of its creative capabilities.
Another avenue to explore is the model's ability to handle extremely long context lengths. Try providing it with a multi-paragraph prompt or even the full text of a short story, then have it continue the narrative. Observe how it maintains consistency and develops the story over hundreds or thousands of additional tokens.
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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