Yarn-Mistral-7b-128k
Maintainer: NousResearch
566
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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 Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of the Mistral-7B-v0.1 model and supports a 128k token context window. The model was created by NousResearch and demonstrates strong performance on long context benchmarks.
Model inputs and outputs
The Yarn-Mistral-7b-128k model takes text as input and generates text as output. It can be used for a variety of language tasks such as text generation, summarization, and question answering.
Inputs
- Text prompts
Outputs
- Generated text
Capabilities
The Yarn-Mistral-7b-128k model excels at tasks requiring long-range context, such as summarizing long documents or generating coherent multi-paragraph text. It maintains good performance even when the context window is extended to 128k tokens, outperforming the original Mistral-7B-v0.1 model.
What can I use it for?
The Yarn-Mistral-7b-128k model can be used for a variety of natural language processing tasks, such as text generation, summarization, and question answering. Its long context capabilities make it well-suited for applications that require understanding and generating long-form text, such as creative writing, technical documentation, or research summarization.
Things to try
One interesting thing to try with the Yarn-Mistral-7b-128k model is to provide it with a lengthy prompt or context and see how it is able to generate coherent and relevant text. The model's ability to maintain context over a 128k token window allows it to produce more consistent and informative outputs compared to models with shorter context windows.
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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