@0x0SojalSec: Created an entire 16-hour free YouTube playlist on how to build a DeepSeek model from scratch. it goes over the papers,…
Summary
A 16-hour free YouTube playlist created by @0x0SojalSec teaches how to build a DeepSeek model from scratch, covering papers, theory, and code implementation including attention mechanisms, mixture of experts, and positional encodings.
View Cached Full Text
Cached at: 06/29/26, 12:20 AM
Created an entire 16-hour free YouTube playlist on how to build a DeepSeek model from scratch.
it goes over the papers, explains the theory, and implements the code.
and he Cover :
- Attention mechanism fully explained
- Multi-head latent attention
- Grouped query attention
- Everything about positional encodings
- Mixture of experts (MoE)
Similar Articles
@rasbt: Added a DeepSeek Sparse Attention (DSA) from-scratch implementation to my LLMs-from-scratch repo thanks to an awesome n…
Sebastian Raschka added a from-scratch implementation of DeepSeek Sparse Attention (DSA) to the LLMs-from-scratch educational repository, including motivation, overview, and a GPT-style reference implementation.
DeepSeek open-sources inference optimizations with 60–85% faster generation [pdf]
DeepSeek open-sourced DeepSpec, a full-stack codebase for training and evaluating draft models for speculative decoding, enabling 60-85% faster generation. It includes data preparation, training, and evaluation scripts with support for multiple draft model algorithms (DSpark, DFlash, Eagle3).
@DeRonin_: DeepSeek just dropped a 5-page paper + free GitHub repo that makes any LLM respond 80% faster it's called speculative d…
DeepSeek released a paper and MIT-licensed open-source implementation of speculative decoding (DSpark) that speeds up LLM responses by up to 80% by using a small 'guess' model and a large 'check' model, achieving both speed and accuracy without tradeoffs.
deepseek-ai/DeepSeek-V4-Flash-DSpark
DeepSeek releases V4 series of Mixture-of-Experts language models (Pro 1.6T/49B activated, Flash 284B/13B activated) supporting one-million-token context with hybrid attention and speculative decoding, claiming best open-source model performance.
@0x0SojalSec: This free Deep Learning resource is insane bro, Perfect for self-learners. 68 interactive Python notebooks. One of the …
A tweet promoting a free deep learning resource with 68 interactive Python notebooks covering topics from basics to advanced techniques like GANs and diffusion models, ideal for self-learners.