mamba2-hybrid-8b-3t-4k
Maintainer: nvidia
61
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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 mamba2-hybrid-8b-3t-4k model is an 8-billion parameter Mamba-2-Hybrid language model released by NVIDIA. It was trained on 3.5 trillion tokens with a sequence length of 4,000 tokens. This model can be compared to an 8-billion parameter Transformer model trained on the same data with the same hyperparameters. NVIDIA also released longer context versions of the Mamba-2-Hybrid model with sequence lengths of 32,000 and 128,000 tokens.
Model inputs and outputs
The mamba2-hybrid-8b-3t-4k model takes text as input and generates text as output, making it a text-to-text model. It can be used for a variety of natural language processing tasks such as summarization, translation, and question answering.
Inputs
- Text data
Outputs
- Generated text
Capabilities
The mamba2-hybrid-8b-3t-4k model has demonstrated strong performance on a range of natural language tasks. It can generate coherent and contextually appropriate text, summarize long passages, and perform well on tasks requiring long-range reasoning.
What can I use it for?
The mamba2-hybrid-8b-3t-4k model can be used for a variety of applications, such as content generation, text summarization, and question answering. Its ability to handle long-range dependencies makes it well-suited for tasks that require understanding of complex, multi-sentence contexts. Companies could potentially use this model to automate the generation of marketing copy, product descriptions, or technical documentation.
Things to try
Researchers and developers can experiment with fine-tuning the mamba2-hybrid-8b-3t-4k model on specific tasks or datasets to further improve its performance. Additionally, exploring the model's capabilities in handling long-range dependencies and reasoning could lead to novel applications and insights.
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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