Learning path to fully understand the Kimi K3 technical report?[D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
Hi everyone,
Can anyone suggest a learning path to fully understand the technical report for Kimi K3?
My background:
- I've taken a graduate-level deep learning course.
- I understand the Transformer architecture, attention, and the basics of LLMs.
- I'm familiar with DeepSeek's OCR models but I haven't studied topics like MoE, MLA, distributed training, or modern post-training in depth.
I'm looking for a roadmap that would help me read the K3 report and understand the design choices instead of just recognizing the terminology.
Thanks!
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