mamba-2.8b-hf
Maintainer: state-spaces
60
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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
mamba-2.8b-hf is an AI model developed by state-spaces, the maintainer of the model. It is a 2.8 billion parameter model that uses the Mamba architecture, a new state space model that shows promising performance on language modeling tasks compared to previous subquadratic models. The Mamba architecture is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation.
Similar models include the mamba-2.8b-slimpj model, which uses the same Mamba architecture but is trained on the SlimPajama dataset, and the mamba-2.8b-instruct-openhermes model, which is fine-tuned on the OpenHermes dataset for instruction-following tasks.
Model inputs and outputs
Inputs
- Text prompts in natural language
Outputs
- Generates text continuations based on the input prompt
Capabilities
The mamba-2.8b-hf model is capable of generating coherent and contextually relevant text continuations given an initial prompt. It can be used for a variety of language generation tasks, such as story writing, dialogue generation, and summarization.
What can I use it for?
The mamba-2.8b-hf model can be used for a variety of text generation tasks, including creative writing, dialogue generation, and summarization. It could be particularly useful for companies or individuals looking to generate high-quality, contextually relevant text content at scale.
Things to try
One interesting aspect of the mamba-2.8b-hf model is its use of the Mamba architecture, which leverages structured state space models for efficient language modeling. Users could experiment with fine-tuning the model on specialized datasets or using different decoding strategies to see how it performs on various text generation tasks.
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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