The quantitative models offered by Nexa AI have three core advantages over traditional AI models:
- performance optimization: By reducing the numerical precision of model parameters (e.g., from 32-bit floating-point to 8-bit integer), the model size is reduced by more than 75% with little impact on accuracy, and the inference speed is increased by 2-4 times.
- Resource savings: The quantized model requires significantly less memory and computational resources, making deployment on edge devices (e.g., cell phones, embedded systems) possible.
- Reduced energy consumption: Particularly suitable for scenarios that require continuous operation, it significantly reduces power consumption.
The following dimensions are recommended to be considered when selecting a model:
- Type of mission: model libraries are categorized by labels such as NLP/vision/speech, and core requirements should be identified first
- Precision Requirements: Benchmarking metrics (e.g., accuracy, F1 scores) for checking models
- hardware limitation: Concerns about the model's CPU/GPU, memory usage
- <strong]Language support</strong]: in particular, NLP models need to pay attention to the linguistic distribution of the training data
The platform provides detailed Model Card instructions, including input and output formats, performance benchmarks and sample code, and it is recommended to download a small-scale model for testing and validation.
This answer comes from the articleNexa: a small multimodal AI solution for local operationThe
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