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# 模型 | ||
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## Model | ||
How AI models are made: Data is a crucial component | ||
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data is upstream in process of developing good models | ||
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- Data prep, select data to train model on, across various sources | ||
- Data evaluation & curation, clean & curate to improve dataset quality | ||
- Model training | ||
- create custom model architecture using training frameworks | ||
- train & iterate models to improve performance | ||
- Model deployment, deploy models & continueously evaluate to fine-tun models | ||
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[BLOOM]() | ||
[LLAMA-1]() | ||
[LIaMA-2]() | ||
[Mistral-7B]() | ||
[OpenAI]() | ||
[Codegen]() | ||
[Stable Diffusion 1.0]() | ||
[CodeV1]() | ||
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## [DSD(Dense-Sparse-Dense)](https://arxiv.org/pdf/1607.04381.pdf) | ||
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- [模型数据下载](https://songhan.github.io/DSD/) | ||
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如何通过改进训练过程提高传统模型的准确率 | ||
如何通过改进训练过程提高传统模型的准确率 | ||
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## [onnx](https://onnx.ai/) | ||
> Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. | ||
[microsoft的版本](https://github.com/onnx/onnx)和[ORT--ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator](https://github.com/Microsoft/onnxruntime) | ||
[可参考大致流程](https://github.com/microsoft/onnxjs) | ||
[onnx example中可以找到一个例子,找到模型,把模型参数传入就可以得到结果了](https://github.com/microsoft/onnxruntime-inference-examples) |
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# 趋势 | ||
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## platform | ||
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- IaaS, Infrastructure as a service | ||
- PaaS, Platform as a service | ||
- SaaS, Software as a service | ||
- IQaaS, Intelligence as a service | ||
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## AI | ||
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## 参考 | ||
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- [The Ai Revolution]() |
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