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README.md

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-        <b>English</b> |
         <a href="https://huggingface.co/aidal/Persian-Mistral-7B#model-description">Model description</a> |
         <a href="https://huggingface.co/aidal/Persian-Mistral-7B#example-output">Example output</a> |
         <a href="https://huggingface.co/aidal/Persian-Mistral-7B#banchmark-results">Banchmark results</a> |
         <a href="https://huggingface.co/aidal/Persian-Mistral-7B#how-to-use">How to use</a> |
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 # Model description
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+>Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks.
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+>Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.
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+>This model card is for the base version of Jamba. It’s a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and a total of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU.
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 # Example output:
 
 **Example 1:**