Jev Course – Prefill-Only Inference & Decision Models from Scratch
Learn how to build high-speed decision-making pipelines for language models using Jev in PyTorch. This comprehensive course covers both encoder and decoder architectures, custom causal attention masking, and KV-cache reuse to bypass slow autoregressive generation. You will also explore fine-tuning techniques, teacher-student distillation, and reinforcement learning methods like GRPO to optimize decision latency and accuracy.Code: https://github.com/vivekkalyanarangan30/how-jev-works
✏️ Course developed by Vivek Kalyanarangan.
📘 Author, Quantization and Fast Inference (Manning)
https://hubs.la/Q04ff8F40
⭐️ Chapters ⭐️
– 0:00:00 Introduction & Course Overview
– 0:01:12 What is Jev & LLM Prefill vs. Decode
– 0:03:26 Jev Interfaces: Choice, Score, and Boolean
– 0:05:30 Prerequisites & Hardware Requirements
– 0:07:10 The 3-Phase Roadmap
– 0:08:00 Baseline: Vanilla LLM Token-by-Token Decoding
– 0:13:00 Stepping Through KV Cache & Autoregressive Loops
– 0:21:24 Encoder-Only Architecture for Fast Decisions
– 0:25:26 Implementing Encoder-Based Jev in PyTorch
– 0:32:00 Question Spans, Mask Tokens & Mean Pooling
– 0:36:30 Dot Product Interactions, Linear Heads & Fine-Tuning Approaches
– 0:40:42 Decoder-Only Architecture & Context Caching
– 0:46:22 Custom Causal Attention Masking for Multi-Question Packing
– 0:52:16 Implementing Decoder-Based Jev with KV Cache Re-use
– 1:03:52 Extracting Decision Point Logits & Token Probabilities
– 1:09:12 Handling Multi-Token Collision Issues
– 1:11:15 Training Workflows & Teacher-Student Distillation
– 1:12:35 Reinforcement Learning with GRPO & Perturbed Trajectories
– 1:16:54 Log Loss, Spherical Scores & Ranked Probability Score (RPS)
– 1:21:38 Constructing the Final RLCD Loss Function
– 1:23:17 Encoder vs. Decoder: Trade-offs & Architecture Comparison
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp
🎉 Thanks to our Champion and Sponsor supporters:
👾 @omerhattapoglu1158
👾 @goddardtan
👾 @akihayashi6629
👾 @kikilogsin
👾 @anthonycampbell2148
👾 @tobymiller7790
👾 @rajibdassharma497
👾 @CloudVirtualizationEnthusiast
👾 @adilsoncarlosvianacarlos
👾 @martinmacchia1564
👾 @ulisesmoralez4160
👾 @_Oscar_
👾 @jedi-or-sith2728
👾 @justinhual1290
--
Learn to code for free and get a developer job: https://www.freecodecamp.org
Read hundreds of articles on programming: https://freecodecamp.org/news Receive SMS online on sms24.me
TubeReader video aggregator is a website that collects and organizes online videos from the YouTube source. Video aggregation is done for different purposes, and TubeReader take different approaches to achieve their purpose.
Our try to collect videos of high quality or interest for visitors to view; the collection may be made by editors or may be based on community votes.
Another method is to base the collection on those videos most viewed, either at the aggregator site or at various popular video hosting sites.
TubeReader site exists to allow users to collect their own sets of videos, for personal use as well as for browsing and viewing by others; TubeReader can develop online communities around video sharing.
Our site allow users to create a personalized video playlist, for personal use as well as for browsing and viewing by others.
@YouTubeReaderBot allows you to subscribe to Youtube channels.
By using @YouTubeReaderBot Bot you agree with YouTube Terms of Service.
Use the @YouTubeReaderBot telegram bot to be the first to be notified when new videos are released on your favorite channels.
Look for new videos or channels and share them with your friends.
You can start using our bot from this video, subscribe now to Jev Course – Prefill-Only Inference & Decision Models from Scratch
What is YouTube?
YouTube is a free video sharing website that makes it easy to watch online videos. You can even create and upload your own videos to share with others. Originally created in 2005, YouTube is now one of the most popular sites on the Web, with visitors watching around 6 billion hours of video every month.
