FNNX is a versatile and standardized format designed to package machine learning models consistently, irrespective of the framework, domain, platform, or programming language. It aims to keep a balance between strictness and flexibility through a range of variants/dialects. Strict variants are ideal for well-defined use cases, ensuring consistency and portability across platforms. Flexible variants, on the other hand, accommodate the fast-paced nature of machine learning, allowing greater freedom for experimentation and innovation.
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The documentation and use cases are coming soon. Stay tuned!