superhot-13b-8k-no-rlhf-test
Maintainer: kaiokendev
62
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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 superhot-13b-8k-no-rlhf-test model, developed by kaiokendev, is a second prototype of the SuperHOT language model. It features a 13B parameter size and an increased context length of up to 8K tokens, without the use of Reinforcement Learning from Human Feedback (RLHF). This model builds upon the techniques described in kaiokendev's blog post to extend the context length beyond the typical 2K-4K range.
Similar models include the Pygmalion-13B-SuperHOT-8K-GPTQ and the Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ, both of which incorporate the SuperHOT techniques to increase the context length.
Model inputs and outputs
Inputs
- Text prompts of up to 8192 tokens
Outputs
- Continuation of the input text, with the model generating new text based on the provided context
Capabilities
The superhot-13b-8k-no-rlhf-test model is capable of generating text with an extended context length of up to 8192 tokens. This allows the model to maintain coherence and consistency over longer passages of text, making it suitable for tasks that require understanding and generating content over multiple paragraphs or pages.
What can I use it for?
The extended context length of this model makes it well-suited for applications that require generating long-form content, such as creative writing, article generation, or summarization. The lack of RLHF means the model may be less constrained in its outputs, potentially allowing for more diverse and experimental content generation.
Things to try
One key aspect to explore with this model is the impact of the extended context length on the generated text. You can experiment with prompts that span multiple paragraphs or pages to see how the model maintains coherence and consistency over longer passages. Additionally, you can try comparing the outputs of this model to those of models with more typical context lengths to understand the differences in the generated content.
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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