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!
[link] [comments]
More from r/MachineLearning
-
I turned my dog Jackies Brain into a working AI [P]
Oct 14
-
Event2Vec: additive event embeddings where a linear score on a history decomposes exactly into per-event terms [P]
Oct 14
-
Binary noise classification using CRNN mean teacher algo [R]
Oct 14
-
Localized Neural learning vs backpropagation [D]
Oct 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.