Eagle 7B

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Eagle 7B is a 7.52 billion parameter language model built on the RWKV-v5 architecture, a linear transformer design that offers significantly lower inference costs compared to traditional transformers. Trained on 1.1 trillion tokens across over 100 languages, Eagle 7B excels in multilingual benchmarks, outperforming other models in its class. In English evaluations, it approaches the performance levels of larger models like Falcon, LLaMA2, and Mistral. As an "Attention-Free Transformer," Eagle 7B provides efficient processing without the computational overhead associated with attention mechanisms. Released under the Apache 2.0 license, it is freely available for both personal and commercial use. Key Features and Functionality: - RWKV-v5 Architecture: Utilizes a linear transformer design, achieving 10-100 times lower inference costs compared to traditional transformers. - Multilingual Training: Trained on 1.1 trillion tokens encompassing over 100 languages, ensuring robust performance across diverse linguistic contexts. - High Performance: Outperforms all 7B class models in multilingual benchmarks and approaches the performance of larger models in English evaluations. - Attention-Free Mechanism: Operates without traditional attention mechanisms, enhancing computational efficiency. - Open Source Availability: Licensed under Apache 2.0, allowing unrestricted personal and commercial use. Primary Value and User Solutions: Eagle 7B addresses the need for efficient, high-performing language models that support a wide range of languages. Its linear transformer architecture reduces computational costs, making advanced AI capabilities more accessible and environmentally sustainable. By excelling in multilingual benchmarks, Eagle 7B enables developers and organizations to build applications that cater to a global audience without compromising performance. Its open-source nature fosters innovation and customization, allowing users to fine-tune the model for specific use cases.