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1
Intro
2
The Github Repo
3
Why Diffusion For Text?
4
Diving into the Code
5
Pre-Trained SEDD Demo
6
Intro to Code and 1st Step
7
How I Started
8
Noise and Transition Graph
9
Script Demoing Perturbation
10
Looking into Sample Transition Function
11
Looking into Sampler
12
Running an Actual Train/Training Script
13
Questions and Discussion
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn to implement Discrete Diffusion Modeling for text generation through a detailed 45-minute technical video that breaks down the paper "Estimating the Ratios of the Data Distribution." Explore a competitive alternative to GPT-2 using diffusion techniques, starting with an introduction to the GitHub repository and the fundamental concepts of applying diffusion to text generation. Progress through hands-on code demonstrations, including a pre-trained SEDD demo, noise and transition graph analysis, perturbation scripts, and sample transition functions. Master the implementation details of the sampler and experience running actual training scripts, concluding with an interactive Q&A session. Access additional resources including the original paper, detailed notes, and community support through Oxen AI's platform, which specializes in dataset versioning for AI development.

Training LLMs with Diffusion Techniques - From Theory to Implementation

Oxen
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