Analysis Engine
./gen.py ./resources/instructions.json
，详情可参考新收录的资料
Next up, let’s load the model onto our GPUs. It’s time to understand what we’re working with and make hardware decisions. Kimi-K2-Thinking is a state-of-the-art open weight model. It’s a 1 trillion parameter mixture-of-experts model with multi-headed latent attention, and the (non-shared) expert weights are quantized to 4 bits. This means it comes out to 594 GB with 570 GB of that for the quantized experts and 24 GB for everything else.
For the longest time, I would NOT allow people to write tests because I thought that culturally, we need to have a culture of shipping fast and we should be dogfooding our own product and that should be enough. At some point, I realized that I was affecting the quality of our product and productivity and I changed my mind. In some ways, it was too late. That's why now that we're rewriting everything, we started with tests from the ground up and the most strict TypeScript mode.