stablelm-base-alpha-7b
Maintainer: stabilityai
211
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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
StableLM-Base-Alpha is a suite of 3B and 7B parameter decoder-only language models pre-trained on a diverse collection of English datasets. These models are designed to push beyond the context window limitations of existing open-source language models. The 3B model and 7B model are part of this suite.
The models are based on the NeoX transformer architecture and developed by Stability AI. They are licensed under the Creative Commons license (CC BY-SA-4.0), allowing for both commercial and non-commercial use as long as attribution is provided.
Model Inputs and Outputs
The StableLM-Base-Alpha models take in text prompts and generate continuation text. The input prompts can be of any length up to 4096 tokens. The models will then generate new tokens, with the ability to continue the text for up to 64 additional tokens.
Inputs
- Text prompts of up to 4096 tokens
Outputs
- Continued text, with the ability to generate up to 64 additional tokens
Capabilities
The StableLM-Base-Alpha models excel at a variety of text generation tasks, such as creative writing, summarization, and language modeling. They can be used to generate coherent and contextually relevant text, while maintaining a high level of fluency.
What Can I Use It For?
The StableLM-Base-Alpha models can be used as a foundation for a wide range of applications, such as:
- Content generation for blogs, articles, or stories
- Assistive writing tools to help users generate text
- Language modeling for downstream tasks like sentiment analysis or text classification
- Chatbots and conversational agents
- Summarization of long-form text
Things to Try
One interesting aspect of the StableLM-Base-Alpha models is their ability to maintain coherence and context over long sequences of text. You can try providing the models with prompts that require extended context, such as multi-paragraph narratives or complex instructions, and see how they respond. Additionally, you can experiment with different decoding strategies, such as adjusting the temperature or top-p sampling, to generate more diverse or controlled outputs.
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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