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README.md

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 - generated_from_trainer
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+license: apache-2.0
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 # Zebra-Llama: Towards Extremely Efficient Hybrid Models
 Zebra-Llama is a family of hybrid large language models (LLMs) proposed by AMD that achieve Transformer-level accuracy with near-State Space Model (SSM) efficiency. While standard Transformers are limited by the quadratic complexity of self-attention and the large memory footprint of their key-value (KV) cache, Zebra-Llama offers a practical and scalable solution.
 
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 This model, `Zebra-Llama-1B-4MLA-12M2`, is created by efficiently adapting the pre-trained Llama-3.2-1B-Instruct model. It composes efficient hybrid layers, combining Multi-head Latent Attention (MLA) for KV cache compression and Mamba2 (an SSM) for computational efficiency. This approach bypasses the need for costly pre-training from scratch.
 
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-Zebra-Llama is a family of hybrid large language models (LLMs) proposed by AMD that achieve Transformer-level accuracy with near-State Space Model (SSM) efficiency. While standard Transformers are limited by the quadratic complexity of self-attention and the large memory footprint of their key-value (KV) cache, Zebra-Llama offers a practical and scalable solution.
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-This model, Zebra-Llama-1B-4MLA-12M2, is created by efficiently adapting the pre-trained Llama-3.2-1B-Instruct model. It composes efficient hybrid layers, combining Multi-head Latent Attention (MLA) for KV cache compression and Mamba2 (an SSM) for computational efficiency. This approach bypasses the need for costly pre-training from scratch.
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 The composition follows a three-stage pipeline to effectively transfer knowledge from the pre-trained Transformer.
 
 
@@ -136,4 +125,4 @@ If you find this model useful, please consider citing the original paper:
   journal={arXiv preprint arXiv:2503.11132},
   year={2025}
 }
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